CoronARe: A coronary artery reconstruction challenge - Cimen, Serkan; Unberath, Mathias; Frangi, Alejandro; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017)
CoronARe ranks state-of-the-art methods in symbolic and tomographic coronary artery reconstruction from interventional C-arm rotational angiography. Specifically, we benchmark the performance of the methods using accurately pre-processed data, and study the effects of imperfect pre-processing conditions (segmentation and background subtraction errors). In this first iteration of the challenge, evaluation is performed in a controlled environment using digital phantom images, where accurate 3D ground truth is known.
CRIS-Team Scopus Importer:2022-06-05
Keywords: 3D reconstruction; Angiography; C-arm Cone-beam CT; Motion compensation

Coronary Artery Plaque Characterization from CCTA Scans Using Deep Learning and Radiomics - Denzinger, Felix; Wels, Michael; Ravikumar, Nishant; Breininger, Katharina; Reidelshöfer, Anika; Eckert, Joachim; Sühling, Michael; Schmermund, Axel; Maier, Andreas (MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 2019)
Assessing coronary artery plaque segments in coronary CT angiography scans is an important task to improve patient management and clinical outcomes, as it can help to decide whether invasive investigation and treatment are necessary. In this work, we present three machine learning approaches capable of performing this task. The first approach is based on radiomics, where a plaque segmentation is used to calculate various shape-, intensity- and texture-based features under different image transformations. A second approach is based on deep learning and relies on centerline extraction as sole prerequisite. In the third approach, we fuse the deep learning approach with radiomic features. On our data the methods reached similar scores as simulated fractional flow reserve (FFR) measurements, which - in contrast to our methods - requires an exact segmentation of the whole coronary tree and often time-consuming manual interaction. In literature, the performance of simulated FFR reaches an AUC between 0.79–0.93 predicting an abnormal invasive FFR that demands revascularization. The radiomics approach achieves an AUC of 0.84, the deep learning approach 0.86 and the combined method 0.88 for predicting the revascularization decision directly. While all three proposed methods can be determined within seconds, the FFR simulation typically takes several minutes. Provided representative training data in sufficient quantities, we believe that the presented methods can be used to create systems for fully automatic non-invasive risk assessment for a variety of adverse cardiac events.<br
Keywords: Plaque characterization; Computer aided diagnosis; Coronary CT angiography; Radiomics; Deep learning

Coronary Plaque Analysis for CT Angiography Clinical Research - Denzinger, Felix; Wels, Michael; Hopfgartner, Christian; Lu, Jing; Schöbinger, Max; Maier, Andreas; Sühling, Michael (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Correction of artifacts from misregistered B-scans in orthogonally scanned and registered OCT angiography - Ploner, Stefan; Schottenhamml, Julia; Moult, Eric M.; Husvogt, Lennart; Alibhai, A. Yasin; Waheed, Nadia K.; Duker, Jay S.; Fujimoto, James G.; Maier, Andreas (Investigative Ophthalmology & Visual Science, 2019)
Purpose : While individual volumetric optical coherence tomography (OCT) scans suffer from distortion especially along the slowly scanned transverse axis, registration enables merging of two orthogonally oriented B-scans into one averaged volume with corrected distortion and reduced noise. However, registration errors can occur, which appear as blurred or duplicated vessels in en-face OCT angiography (OCTA) images. We present a fully automatic method that excludes artifacts and locally smooths resulting en-face images to achieve a uniform noise level. Noise non-uniformity emerges from differing numbers of averaged samples. This occurs when misregistrations or saccadic OCTA B-scans are excluded or due to varying depth of projected layers in en-face images. Uniform noise is critical for subsequent analysis algorithms to produce consistent results throughout the image. Methods : Two orthogonally scanned OCT volumes are registered and individual en-face OCTA images are computed. By design, errors in registration occur along B-scans, thus they extend along horizontal or vertical lines in the en-face images. As vessels do not coincide within misregistrations, these lines are detected via their high difference between the en-face images. The scan with B-scan orientation along the line can be identified as the one containing the misregistration and only that scan's B-scan is excluded, while the valid data in the other scan is preserved. To compensate for locally increased noise due to smaller numbers of averaged samples, adaptive Gaussian smoothing is applied. The standard deviation is controlled locally by the number of excluded scans and the remaining scans' sampling densities and projected layers' thickness, such that the total contribution of samples per pixel is kept uniform. The method was evaluated on 8 healthy eyes and 6 eyes with various pathologies. Results : Figure 1 shows the classification performance, Figure 2 compares merged images. Conclusions : The number of artifacts in orthogonal scan based OCTA volume registration was reduced while preventing noise non-uniformity due to varying numbers of averaged samples per pixe

Correction propagation for user-assisted optical coherence tomography segmentation: general framework and application to Bruch’s membrane segmentation - Stromer, Daniel; Moult, Eric M.; Chen, Siyu; Waheed, Nadja K.; Maier, Andreas; Fujimoto, James G. (Biomedical Optics Express, 2020)
Optical coherence tomography (OCT) is a commonly used ophthalmic imaging modality. While OCT has traditionally been viewed cross-sectionally (i.e., as a sequence of B-scans), higher A-scan rates have increased interest in en face OCT visualization and analysis. The recent clinical introduction of OCT angiography (OCTA) has further spurred this interest, with chorioretinal OCTA being predominantly displayed via en face projections. Although en face visualization and quantitation are natural for many retinal features (e.g., drusen and vasculature), it requires segmentation. Because manual segmentation of volumetric OCT data is prohibitively laborious in many settings, there has been significant research and commercial interest in developing automatic segmentation algorithms. While these algorithms have achieved impressive results, the variability of image qualities and the variety of ocular pathologies cause even the most robust automatic segmentation algorithms to err. In this study, we develop a user-assisted segmentation approach, complementary to fully-automatic methods, wherein correction propagation is used to reduce the burden of manually correcting automatic segmentations. The approach is evaluated for Bruch’s membrane segmentation in eyes with advanced age-related macular degeneration.<br
Keywords: Angiography; Image processing; Ophthalmic imaging; Optical coherence tomography; Retinal nerve fiber layer; Segmentation

Correction to: Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing (Magnetic Resonance Materials in Physics, Biology and Medicine, (2020), 10.1007/s10334-019-00819-2) - Utzschneider, Matthias; Behl, Nicolas G.R.; Lachner, Sebastian; Gast, Lena; Maier, Andreas; Uder, Michael; Nagel, Armin Michael (Magnetic Resonance Materials in Physics Biology and Medicine, 2020)
The original version of this article unfortunately contained a mistake. Title was incorrect. The corrected title is given below. Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing. The original article has been corrected.
CRIS-Team Scopus Importer:2020-02-25

Correlation of Vascular Impairment with Geographic Atrophy Progression Analyzed with Swept Source OCT Angiography and Variable Interscan Time Analysis - Despotovic, Ivana; Moult, Eric M.; Rebhun, Carl B.; Ploner, Stefan; Zacharia, Jennifer; Alibhai, A. Yasin; Moreira-Neto, Carlos; Lee, Byungkun; Baumal, Caroline R.; Rosenfeld, Philip; Duker, Jay S.; Fujimoto, James G.; et al. (ARVO annual meeting 2017, 2017)
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.correl
Keywords: oct; octa; oct angiography; vista; variable interscan time analysis; blood flow; Geographic Atrophy

Corrigendum to: Intraoperative imaging modalities and compensation for brain shift in tumor resection surgery (International Journal of Biomedical Imaging (2017) 2017 (6028645) DOI: 10.1155/2017/6028645) - Bayer, Siming; Maier, Andreas; Ostermeier, Martin; Fahrig, Rebecca (International Journal of Biomedical Imaging, 2019)
In the article titled "Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery" [1], there following two errors should be corrected: (1) The number of categories should be thirteen instead of fourteen in the following three sentences: (i) The sentence "In total, 126 papers regarding this topic are analyzed in a comprehensive summary and are categorized according to fourteen criteria" in the abstract section. (ii) The sentence "The fourteen categories of interest are brain shift compensation strategy, global transformation model, local transformation model, computational platform, registration basis, optimization method, similarity metric, intraoperative modality, constitutive model type, mesh element, quantification object, validation, and treatment (the categories in [13] are physical, surgical, biological, intraoperative imaging, other, registration, and modeling. This categorization focuses on the causes of brain shift rather than on the algorithmic approaches to identify and correct for brain shift)" in the Methods and Contribution section. (iii) The sentence "In total, 116 publications are grouped by the defined fourteen categories" in the Methods and Contribution section. (2) Reference number [132] should be changed as follows: F. L. Bookstein, "Principal warps: thin-plate splines and the decomposition of deformations," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 6, pp. 567-585, June 1989. doi: 10.1109/34.24792.
CRIS-Team Scopus Importer:2019-10-29

CraquelureNet: Matching The Crack Structure In Historical Paintings For Multi-Modal Image Registration - Sindel, Aline; Maier, Andreas; Christlein, Vincent (2021 IEEE International Conference on Image Processing (ICIP), 2021)
Visual light photography, infrared reflectography, ultraviolet fluorescence photography and x-radiography reveal even hidden compositional layers in paintings. To investigate the connections between these images, a multi-modal registration is essential. Due to varying image resolutions, modality dependent image content and depiction styles, registration poses a challenge. Historical paintings usually show crack structures called craquelure in the paint. Since craquelure is visible by all modalities, we extract craquelure features for our multi-modal registration method using a convolutional neural network. We jointly train our keypoint detector and descriptor using multi-task learning. We created a multi-modal dataset of historical paintings with keypoint pair annotations and class labels for craquelure detection and matching. Our method demonstrates the best registration performance on the multi-modal dataset in comparison to competing method
Keywords: Multi-modal registration; keypoint detection; descriptor learning; historical paintings; crack detection

Critical Review of Processing and Classification Techniques for Images and Spectra in Microplastic Research - Cowger, Win; Gray, Andrew; Christiansen, Silke H.; DeFrond, Hannah; Deshpande, Ashok D.; Hemabessiere, Ludovic; Lee, Eunah; Mill, Leonid; Munno, Keenan; Ossmann, Barbara E.; Pittroff, Marco; Rochman, Chelsea; et al. (Applied Spectroscopy, 2020)
Microplastic research is a rapidly developing field, with urgent needs for high throughput and automated analysis techniques. We conducted a review covering image analysis from optical microscopy, scanning electron microscopy, fluorescence microscopy, and spectral analysis from Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, pyrolysis gas–chromatography mass–spectrometry, and energy dispersive X-ray spectroscopy. These techniques were commonly used to collect, process, and interpret data from microplastic samples. This review outlined and critiques current approaches for analysis steps in image processing (color, thresholding, particle quantification), spectral processing (background and baseline subtraction, smoothing and noise reduction, data transformation), image classification (reference libraries, morphology, color, and fluorescence intensity), and spectral classification (reference libraries, matching procedures, and best practices for developing in-house reference tools). We highlighted opportunities to advance microplastic data analysis and interpretation by (i) quantifying colors, shapes, sizes, and surface topologies with image analysis software, (ii) identifying threshold values of particle characteristics in images that distinguish plastic particles from other particles, (iii) advancing spectral processing and classification routines, (iv) creating and sharing robust spectral libraries, (v) conducting double blind and negative controls, (vi) sharing raw data and analysis code, and (vii) leveraging readily available data to develop machine learning classification models. We identified analytical needs that we could fill and developed supplementary information for a reference library of plastic images and spectra, a tutorial for basic image analysis, and a code to download images from peer reviewed literature. Our major findings were that research on microplastics was progressing toward the use of multiple analytical methods and increasingly incorporating chemical classification. We suggest that new and repurposed methods need to be developed for high throughput screening using a diversity of approaches and highlight machine learning as one potential avenue toward this capability.
CRIS-Team Scopus Importer:2020-10-02
Keywords: artificial intelligence; automation; fluorescence; Fourier transform infrared; FT-IR; gas chromatography–mass spectrometry; GC–MS; high throughput; image analysis; machine learning; Microplastic; microscopy; plastic; plastic pollution

Cross-Domain Metal Segmentation for CBCT Metal Artifact Reduction - Rohleder, Maximilian; Gottschalk, Tristan; Maier, Andreas; Kreher, Bjoern W. (Proceedings of SPIE - The International Society for Optical Engineering, 2022)
Metallic objects in the volume of a CBCT system can cause various artifacts after image reconstruction such as bright and dark streaks, local distortions of CT values and shadowing. In the intraoperative setting, this drastically reduces clinical value and can harden decision making. Most existing approaches to reduce such artifacts rely on a threshold-based metal segmentation in the reconstruction domain, which is prone to failure; especially in cases with extreme artifacts. Faulty metal masks impair these inpainting-based MAR methods and at times even worsen image quality by introducing secondary artifacts. In this work, a novel neural network topology is proposed to segment metallic objects in CBCT reconstruction domain by leveraging information of the given raw projection images. A reconstruction operator is embedded into this architecture, which enables the model to yield projection and reconstruction domain information during end-to-end training. This cross-domain approach is compared to the self-configuring segmentation method”nnUNet”, which predicts the three dimensional metal masks directly from the artifact corrupted reconstruction. To provide a baseline, a segmentation using a global dice-optimal threshold is determined. Segmentation results on simulated data confirmed by 5-fold cross validation show that the cross-domain network yields a mean dice coefficient of 0.87 ± 0.05 at a prediction time of 4s per volume. The reference method achieves 0.86 ± 0.03 in 43s, whereas the optimal similarity using a threshold averages to 0.45 ± 0.22.
CRIS-Team Scopus Importer:2022-11-25
Keywords: Cone-Beam CT; Known Operator Integration; Metal Artifacts; Segmentation

Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition - Hernandez, Abner; Pérez Toro, Paula Andrea; Nöth, Elmar; Orozco Arroyave, Juan Rafael; Maier, Andreas; Yang, Seung Hee (Proceedings of Interspeech 2022, 2022)
State-of-the-art automatic speech recognition (ASR) systems perform well on healthy speech. However, the performance on impaired speech still remains an issue. The current study explores the usefulness of using Wav2Vec self-supervised speech representations as features for training an ASR system for dysarthric speech. Dysarthric speech recognition is particularly difficult as several aspects of speech such as articulation, prosody and phonation can be impaired. Specifically, we train an acoustic model with features extracted from Wav2Vec, Hubert, and the cross-lingual XLSR model. Results suggest that speech representations pretrained on large unlabelled data can improve word error rate (WER) performance. In particular, features from the multilingual model led to lower WERs than Fbanks or models trained on a single language. Improvements were seen in English speakers with cerebral palsy caused dysarthria (UASpeech corpus), Spanish speakers with Parkinsonian dysarthria (PC-GITA corpus) and Italian speakers with paralysis-based dysarthria (EasyCall corpus). Compared to using Fbank features, XLSR-based features reduced WERs by 6.8%, 22.0%, and 7.0% for the UASpeech, PC-GITA, and EasyCall corpus, respectively.<br
Keywords: wav2vec; speech recognition; dysarthric speech; Parkinson’s disease; cerebral palsy

Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach - Kurzendorfer, Tanja; Mewes, Philip; Maier, Andreas; Strobel, Norbert; Brost, Alexander (IEEE Transactions on Medical Imaging, 2016)
Cryo-balloon catheters have attracted an increasing amount of interest in the medical community as they can reduce patient risk during left atrial pulmonary vein ablation procedures. As cryo-balloon catheters are not equipped with electrodes, they cannot be localized automatically by electro-anatomical mapping systems. As a consequence, X-ray fluoroscopy has remained an important means for guidance during the procedure. Most recently, image guidance methods for fluoroscopy-based procedures have been proposed, but they provide only limited support for cryo-balloon catheters and require significant user interaction. To improve this situation, we propose a novel method for automatic cryo-balloon catheter detection in fluoroscopic images by detecting the cryo-balloon catheter's built-in X-ray marker. Our approach is based on a blob detection algorithm to find possible X-ray marker candidates. Several of these candidates are then excluded using prior knowledge. For the remaining candidates, several catheter specific features are introduced. They are processed using a machine learning approach to arrive at the final X-ray marker position. Our method was evaluated on 75 biplane fluoroscopy images from 40 patients, from two sites, acquired with a biplane angiography system. The method yielded a success rate of 99.0% in plane A and 90.6% in plane B, respectively. The detection achieved an accuracy of 1.00mm ± 0.82mm in plane A and 1.13mm ± 0.24mm in plane B. The localization in 3-D was associated with an average error of 0.36mm ± 0.86mm.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.cryoba
Keywords: Ablation; atrial fibrillation; cryo-balloon; detection; electrophysiology; fluoroscopy; left atrium

CT Image Reconstruction Basics - Hornegger, Joachim; Maier, Andreas; Kowarschik, Markus (MR and CT Perfusion and Pharmacokinetic Imaging: Clinical Applications and Theoretical Principles, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.ctimag
Keywords: CT Reconstruction; Introduction

Cycle Time Reduction in Process Integrated Computed Tomography using Compressed Sensing - Schön, Tobias; Römer, Florian; Oeckl, Steven; Grossmann, Marcus; Gruber, Roland; Jung, Alexander; Del Galdo, Giovanni (2015)

Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm - Bertram, Christof A.; Marzahl, Christian; Bartel, Alexander; Stayt, Jason; Bonsembiante, Federico; Beeler-Marfisi, Janet; Barton, Ann K.; Brocca, Ginevra; Gelain, Maria E.; Glasel, Agnes; Du Preez, Kelly; Weiler, Kristina; et al. (Veterinary Pathology, 2022)
Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator’s and algorithmic performance included a ground truth dataset, the mean annotators’ THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis.
CRIS-Team Scopus Importer:2022-12-02
Keywords: artificial intelligence; automated image analysis; bronchoalveolar lavage fluid; computational pathology; digital pathology; equine; pulmonary hemorrhage; respiratory disease; total hemosiderin score

Data Completeness Estimation for 3D C-Arm Scans with Rotated Detector to Enlarge the Lateral Field-of-View - Stromer, Daniel; Kugler, Patrick; Bauer, Sebastian; Lauritsch, Günter; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.dataco_2
Keywords: Field-of-View Enlargement; Detector Rotation

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior - Huang, Yixing; Preuhs, Alexander; Lauritsch, Guenter; Manhart, Michael; Huang, Xiaolin; Maier, Andreas (Machine Learning for Medical Image Reconstruction, 2019)
Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise.Thus, generating reconstructed images directly from a neural network appears inadequate. We propose to constrain the reconstructed images to be consistent with the measured projection data, while the unmeasured information is complemented by learning based methods. For this purpose, a data consistent artifact reduction (DCAR) method is introduced: First, a prior image is generated from an initial limited angle reconstruction via deep learning as a substitute for missing information. Afterwards, a conventional iterative reconstruction algorithm is applied, integrating the data consistency in the measured angular range and the prior information in the missing angular range. This ensures data integrity in the measured area, while inaccuracies incorporated by the deep learning prior lie only in areas where no information is acquired. The proposed DCAR method achieves significant image quality improvement: for 120-degree cone-beam limited angle tomography more than 10% RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy case compared with a state-of-the-art U-Net based method.
Keywords: Deep learning; Limited angle tomography; Data consistency; Poisson noise; Robustness; Generalization ability

Data Driven Gating in SPECT using Isomap - Reymann, Maximilian; Vija, A. Hans; Maier, Andreas (Society of Nuclear Medicine and Molecular Imaging Annual Meeting 2023, 2023)

Data-driven reduction of cardiac models - Mihai Itu, Lucian; Meister, Felix; Sharma, Puneet; Passerini, Tiziano (Artificial Intelligence for Computational Modeling of the Heart, 2020)
A common challenge in physiological modeling is the trade-off between the fidelity of the model and its complexity. Reliable models of cardiac physiology and pathology could have a tremendous value in supporting clinical decisions, by enabling advanced diagnosis, precise risk stratification and personalized therapy planning. However, for this technology to express its potential it is crucial that the data processing time is compatible with the fast-paced clinical workflow. In the ideal case, detailed and patient-specific physics phenomena should be reproduced and simulated with processing times enabling real-time interaction with the model solution. In many cases, real-time processing cannot be achieved if not at the expense of the descriptive power of the model. This chapter describes data-driven approaches for the reduction of physiological models, with the aim of retaining the ability of the model to describe complex physical phenomena, while at the same time drastically reducing the computational complexity.<br
Keywords: Model reduction; Meta modeling; Real-time physics; Atrial electrophysiology; Fractional flow reserve; Cardiac mechanics

Data-Driven Speed-of-Sound Reconstruction for Medical Ultrasound: Impacts of Training Data Format and Imperfections on Convergence - Khun Jush, Farnaz; Dueppenbecker, Peter Michael; Maier, Andreas (Medical Image Understanding and Analysis, 2021)
B-mode imaging is a qualitative method and its interpretation depends on users' experience. Quantitative tissue information can increase precision and decrease user ambiguity. For example, Speed-of-Sound (SoS) in tissue is known to carry diagnostic information. Studies showed the possibility of SoS reconstruction from ultrasound raw data (a.k.a., RF data) using deep neural networks (DNNs). However, many ultrasound systems are designed to process demodulated data (i.e., IQ data) and often decimate data in early stages of acquisition. In this study we investigated the impacts of input data format and decimation on convergence of the DNNs for SoS reconstruction. Our results show that fully data-driven SoS reconstruction is possible using demodulated ultrasound data presented in Cartesian or Polar format using an encoder-decoder network. We performed a study using only amplitude and only phase information of ultrasound data for SoS reconstruction. Our results showed that distortion of the phase information results in inconsistent SoS predictions, indicating sensitivity of the investigated approach to phase information. We demonstrated that without losing significant accuracy, decimated IQ data can be used for SoS reconstruction.

Data Extrapolation from Learned Prior Images for Truncation Correction in Computed Tomography - Huang, Yixing; Preuhs, Alexander; Manhart, Michael; Lauritsch, Günter; Maier, Andreas (IEEE Transactions on Medical Imaging, 2021)
Data truncation is a common problem in computed tomography (CT). Truncation causes cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the FOV. Deep learning has achieved impressive results in CT reconstruction from limited data. However, its robustness is still a concern for clinical applications. Although the image quality of learning-based compensation schemes may be inadequate for clinical diagnosis, they can provide prior information for more accurate extrapolation than conventional heuristic extrapolation methods. With extrapolated projection, a conventional image reconstruction algorithm can be applied to obtain a final reconstruction. In this work, a general plug-and-play (PnP) method for truncation correction is proposed based on this idea, where various deep learning methods and conventional reconstruction algorithms can be plugged in. Such a PnP method integrates data consistency for measured data and learned prior image information for truncated data. This shows to have better robustness and interpretability than deep learning only. To demonstrate the efficacy of the proposed PnP method, two state-of-the-art deep learning methods, FBPConvNet and Pix2pixGAN, are investigated for truncation correction in cone-beam CT in noise-free and noisy cases. Their robustness is evaluated by showing false negative and false positive lesion cases. With our proposed PnP method, false lesion structures are corrected for both deep learning methods. For FBPConvNet, the root-mean-square error (RMSE) inside the FOV can be improved from 92HU to around 30HU by PnP in the noisy case. Pix2pixGAN solely achieves better image quality than FBPConvNet solely for truncation correction in general. PnP further improves the RMSE inside the FOV from 42HU to around 27HU for Pix2pixGAN. The efficacy of PnP is also demonstrated on real clinical head data.<br
Keywords: Deep learning; robustness; interpretability; truncation correction; computed tomography

Dataset of pages from early printed books with multiple font groups - Seuret, Mathias; Limbach, Saskia; Weichselbaumer, Nikolaus; Maier, Andreas; Christlein, Vincent (ACM International Conference Proceeding Series, 2019)
Based on contemporary scripts, early printers developed a large variety of different fonts. While fonts may slightly differ from one printer to another, they can be divided into font groups, such as Textura, Antiqua, or Fraktur. The recognition of font groups is important for computer scientists to select adequate OCR models, and of high interest to humanities scholars studying early printed books and the history of fonts. In this paper, we introduce a new, public dataset for the recognition of font groups in early printed books, and evaluate several state-of-the-art CNNs for the font group recognition task. The dataset consists of more than 35 600 page images, each page showing up to five different font groups, of which ten are considered in this dataset.
CRIS-Team Scopus Importer:2019-11-19
Keywords: Antiqua; Bastarda; Book history; Dataset; Digital humanities; Document analysis; Fonts; Fraktur; Gotico-Antiqua; Greek; Hebrew; Historical documents; Incunabula; Italic; Neural network; Rotunda; Schwabacher; Textura; Type; Typography

Dataset on Bi- and Multi-nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors - Bertram, Christof A.; Donovan, Taryn A.; Tecilla, Marco; Bartenschlager, Florian; Fragoso, Marco; Wilm, Frauke; Marzahl, Christian; Breininger, Katharina; Maier, Andreas; Klopfleisch, Robert; Aubreville, Marc (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Decoupling Respiratory and Angular Variation in Rotational X-ray Scans Using a Prior Bilinear Model - Geimer, Tobias; Keall, Paul; Breininger, Katharina; Caillet, Vincent; Dunbar, Michelle; Bert, Christoph; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
Data-driven respiratory signal extraction from rotational X-ray scans is a challenge as angular effects overlap with respiration-induced change in the scene. In this paper, we use the linearity of the X-ray transform to propose a bilinear model based on a prior 4D scan to separate angular and respiratory variation. The bilinear estimation process is supported by a B-spline interpolation using prior knowledge about the trajectory angle. Consequently, extraction of respiratory features simplifies to a linear problem. Though the need for a prior 4D CT seems steep, our proposed use-case of driving a respiratory motion model in radiation therapy usually meets this requirement. We evaluate on DRRs of 5 patient 4D CTs in a leave-one-phase-out manner and achieve a mean estimation error of 3.01% in the gray values for unseen viewing angles. We further demonstrate suitability of the extracted weights to drive a motion model for treatments with a continuously rotating gantry.
CRIS-Team Scopus Importer:2019-06-21
Keywords: Bilinear model; Feature extraction; Motion model; Respiratory signal; X-ray projection

DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany - Wenninger, Marc; Maier, Andreas; Schmidt, Jochen (Scientific Data, 2021)
Real-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. The dataset contains recordings of 15 homes over a period of up to 3.5 years, wherein total 50 appliances have been recorded at a frequency of 1 Hz. Recorded appliances are of significance for load-shifting purposes such as dishwashers, washing machines and refrigerators. One home also includes three-phase mains readings that can be used for disaggregation tasks. Additionally, DEDDIAG contains manual ground truth event annotations for 14 appliances, that provide precise start and stop timestamps. Such annotations have not been published for any long-term electricity dataset we are aware of.
CRIS-Team Scopus Importer:2021-07-30

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images - Zhong, Xia; Amrehn, Mario; Ravikumar, Nishant; Chen, Shuqing; Strobel, Norbert; Birkhold, Annette; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Scientific Reports, 2021)
In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation. Point-wise features that comprise spatial positions (and surface normal vectors in the case of structured meshes), and their corresponding image features, are used to encode the observation and represent the underlying 3D volume. The actor and value networks are applied iteratively to estimate a sequence of transformations that enable accurate delineation of object boundaries. The proposed approach was extensively evaluated in both segmentation and registration tasks using a variety of challenging clinical datasets. Our method has fewer trainable parameters and lower computational complexity compared to the 3D U-Net, and it is independent of the volume resolution. We show that the proposed method is applicable to mono- and multi-modal segmentation tasks, achieving significant improvements over the state-of-the-art for the latter. The flexibility of the proposed framework is further demonstrated for a multi-modal registration application. As we learn to predict actions rather than a target, the proposed method is more robust compared to the 3D U-Net when dealing with previously unseen datasets, acquired using different protocols or modalities. As a result, the proposed method provides a promising multi-purpose segmentation and registration framework, particular in the context of image-guided interventions.
CRIS-Team Scopus Importer:2021-02-26

Deep autofocus with cone-beam CT consistency constraint - Preuhs, Alexander; Manhart, Michael; Roser, Philipp; Stimpel, Bernhard; Syben-Leisner, Christopher; Psychogios, Marios; Kowarschik, Markus; Maier, Andreas (Bildverarbeitung für die Medizin, 2020)

DeepCEST 7 T: Fast and homogeneous mapping of 7 T CEST MRI parameters and their uncertainty quantification - Hunger, Leonie; Rajput, Junaid Rasool; Klein, Kiril; Mennecke, Angelika; Fabian, Moritz; Schmidt, Manuel; Glang, Felix; Herz, Kai; Liebig, Patrick; Nagel, Armin Michael; Scheffler, Klaus; Dörfler, Arnd; et al. (Magnetic Resonance in Medicine, 2022)
Purpose In this work, we investigated the ability of neural networks to rapidly and robustly predict Lorentzian parameters of multi-pool CEST MRI spectra at 7 T with corresponding uncertainty maps to make them quickly and easily available for routine clinical use. Methods We developed a deepCEST 7 T approach that generates CEST contrasts from just 1 scan with robustness against B-1 inhomogeneities. The input data for a neural feed-forward network consisted of 7 T in vivo uncorrected Z-spectra of a single B-1 level, and a B-1 map. The 7 T raw data were acquired using a 3D snapshot gradient echo multiple interleaved mode saturation CEST sequence. These inputs were mapped voxel-wise to target data consisting of Lorentzian amplitudes generated conventionally by 5-pool Lorentzian fitting of normalized, denoised, B-0- and B-1-corrected Z-spectra. The deepCEST network was trained with Gaussian negative log-likelihood loss, providing an uncertainty quantification in addition to the Lorentzian amplitudes. Results The deepCEST 7 T network provides fast and accurate prediction of all Lorentzian parameters also when only a single B-1 level is used. The prediction was highly accurate with respect to the Lorentzian fit amplitudes, and both healthy tissues and hyperintensities in tumor areas are predicted with a low uncertainty. In corrupted cases, high uncertainty indicated wrong predictions reliably. Conclusion The proposed deepCEST 7 T approach reduces scan time by 50% to now 6:42 min, but still delivers both B-0- and B-1-corrected homogeneous CEST contrasts along with an uncertainty map, which can increase diagnostic confidence. Multiple accurate 7 T CEST contrasts are delivered within seconds.
CRIS-Team WoS Importer:2022-12-02

Deep Denoising for Hearing Aid Applications - Aubreville, Marc; Ehrensperger, Kai; Rosenkranz, T.; Graf, B.; Puder, H.; Maier, Andreas (2018 16TH INTERNATIONAL WORKSHOP ON ACOUSTIC SIGNAL ENHANCEMENT (IWAENC), 2018)
CRIS-Team WoS Importer:2019-02-27

DeepECG: Towards 3-D Continuous Cardiac MRI without ECG-Gating - Deep Learning based R-Wave Classification for Automated Cardiac Phase Binning - Hoppe, Elisabeth; Wetzl, Jens; Yoon, Seung Su; Schneider, Manuel; Stimpel, Bernhard; Preuhs, Alexander; Maier, Andreas (International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition, 2020)

DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio - Schröter, Hendrik; Escalante-B, Alberto N.; Rosenkranz, Tobias; Maier, Andreas (International Workshop on Acoustic Signal Enhancement (IWAENC 2022), 2022)
Deep learning-based speech enhancement has seen huge improvements and recently also expanded to full band audio (48 kHz). However, many approaches have a rather high computational complexity and require big temporal buffers for real time usage e.g. due to temporal convolutions or attention. Both make those approaches not feasible on embedded devices. This work further extends DeepFilterNet, which exploits harmonic structure of speech allowing for efficient speech enhancement (SE). Several optimizations in the training procedure, data augmentation, and network structure result in state-of-the-art SE performance while reducing the real-time factor to 0.04 on a notebook Core-i5 CPU. This makes the algorithm applicable to run on embedded devices in real-time. The DeepFilterNet framework can be obtained under an open source license. <br
Keywords: DeepFilterNet; speech enhancement; full-band; two-stage modeling

DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering - Schröter, Hendrik; Escalante-B, Alberto N.; Rosenkranz, Tobias; Maier, Andreas (ICASSP 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022)
Complex-valued processing has brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram, while complex masks (CM) are usually preferred over real-valued masks due to their ability to modify the phase. Recent work proposed to use a complex filter instead of a point-wise multiplication with a mask. This allows to incorporate information from previous and future time steps exploiting local correlations within each frequency band. In this work, we propose DeepFilterNet, a two stage speech enhancement framework utilizing deep filtering. First, we enhance the spectral envelope using ERB-scaled gains modeling the human frequency perception. The second stage employs deep filtering to enhance the periodic components of speech. Additionally to taking advantage of perceptual properties of speech, we enforce network sparsity via separable convolutions and extensive grouping in linear and recurrent layers to design a low complexity architecture. We further show that our two stage deep filtering approach outperforms complex masks over a variety of frequency resolutions and latencies and demonstrate convincing performance compared to other state-of-the-art models.
Keywords: deep filtering; speech enhancement

DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement - Schröter, Hendrik; Rosenkranz, Tobias; Escalante-B, Alberto N.; Maier, Andreas (INTERSPEECH 2023, 2023)
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep Filtering (DF) was proposed to directly estimate a complex filter in frequency domain to take advantage of these correlations. In this work, we present a real-time speech enhancement demo using DeepFilterNet. DeepFilterNet's efficiency is enabled by exploiting domain knowledge of speech production and psychoacoustic perception. Our model is able to match state-of-the-art speech enhancement benchmarks while achieving a real-time-factor of 0.19 on a single threaded notebook CPU. The framework as well as pretrained weights have been published under an open source license.
Keywords: deep learning; speech enhancement; multi-frame filtering; deep filtering

Deep Generalized Max Pooling - Christlein, Vincent; Spranger, Lukas; Seuret, Mathias; Nikolaou, Angelos; Král, Pavel; Maier, Andreas (2019 International Conference on Document Analysis and Recognition (ICDAR), 2019)
Global pooling layers are an essential part of Convolutional Neural Networks (CNN). They are used to aggregate activations of spatial locations to produce a fixed-size vector in several state-of-the-art CNNs. Global average pooling or global max pooling are commonly used for converting convolutional features of variable size images to a fix-sized embedding. However, both pooling layer types are computed spatially independent: each individual activation map is pooled and thus activations of different locations are pooled together. In contrast, we propose Deep Generalized Max Pooling that balances the contribution of all activations of a spatially coherent region by re-weighting all descriptors so that the impact of frequent and rare ones is equalized. We show that this layer is superior to both average and max pooling on the classification of Latin medieval manuscripts (CLAMM'16, CLAMM'17), as well as writer identification (Historical-WI'17

Deep Geometric Supervision Improves Spatial Generalization in Orthopedic Surgery Planning - Kordon, Florian Johannes; Maier, Andreas; Swartman, Benedict; Privalov, Maxim; El Barbari, Jan Siad; Kunze, Holger (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, 2022)
Careful surgical planning facilitates the precise and safe placement of implants and grafts in reconstructive orthopedics. Current attempts to (semi-)automatic planning separate the extraction of relevant anatomical structures on X-ray images and perform the actual positioning step using geometric post-processing. Such separation requires optimization of a proxy objective different from the actual planning target, limiting generalization to complex image impressions and the positioning accuracy that can be achieved. We address this problem by translating the geometric steps to a continuously differentiable function, enabling end-to-end gradient flow. Combining this companion objective function with the original proxy formulation improves target positioning directly while preserving the geometric relation of the underlying anatomical structures. We name this concept Deep Geometric Supervision. The developed method is evaluated for graft fixation site identification in medial patellofemoral ligament (MPFL) reconstruction surgery on (1) 221 diagnostic and (2) 89 intra-operative knee radiographs. Using the companion objective reduces the median Euclidean Distance error for MPFL insertion site localization from (1) 2.29mm" role="presentation">2.29mm to 1.58mm" role="presentation">1.58mm and (2) 8.70px" role="presentation">8.70px to 3.44px" role="presentation">3.44px, respectively. Furthermore, we empirically show that our method improves spatial generalization for strongly truncated anatom
CRIS-Team Scopus Importer:2022-10-14
Keywords: Surgical planning; Orthopedics; Landmark detection

Deep Image Denoising in SPECT - Reymann, Maximilian; Würfl, Tobias; Ritt, Philipp; Cachovan, Michal; Stimpel, Bernhard; Maier, Andreas (European Journal of Nuclear Medicine and Molecular Imaging (2019) 46 (Suppl 1): S1–S952, 10.1007/s00259-019-04486-2, 2019)
Aim/Introduction: Single Photon Emitted Computed Tomography (SPECT) images are characterized by a high degree of image noise, which is due to the low photon count at the detector. Recently, several deep learning-based approaches for image denoising have been proposed for low-dose Computed Tomography (CT). We investigate their suitability for SPECT denoising. Materials and Methods: In order to have a groundtruth for training the networks, a Monte Carlo Simulation was set up with the SIMIND software, where in total 24 SPECT acquisitions of brains, lungs, livers and skeleton were simulated with the XCAT phantom. Pairs of noisy and clean SPECT images were then used for training the neural networks. The networks tested were the U-Net as proposed by Heinrich et al. [1], the convolutional denoising autoencoder (CNN DAE) proposed by Gondara et al. [2] and a custom U-Net that had a depth of 4 layers with 2 convolutions, Batch Normalization and ReLU in each layer. Training of the networks was done with the hyperparameters as described in the original publications. Results: The best denoising performance of the neural networks was achieved by either the CNN DAE when measured with the Structural Similarity (SSIM) index of 0.933 or by the custom U-Net with a Peak Signal to Noise Ratio (PSNR) of 30.03 dB. This corresponded to a signal quality improvement of 0.09 compared to the noisy input when measured with the SSIM, or 5.9 dB when measured with the PSNR. The denoised SPECT images showed improved visual appearance, however, gain in image quality was not as distinct as on low-dose CT data. We assume this was due to the image characteristics of SPECT images, which are characterized by few distinct image values and a more heterogeneous pixel intensity distribution compared to CT data. Conclusion: Image characteristics in SPECT pose a special challenge to deep learning-based image denoising methods, as the signal is sparse and contains a high degree of noise. We believe this, in conjunction with the limited number of samples explains the weaker benefit in performance in SPECT when compared to low-dose CT data. We assume that with a more diverse training set and specialized loss function suitable for SPECT data , we can further increase performanc
Keywords: Deep Learning; SPECT; Denoising; Monte Carlo Simulation; XCAT; Phantom

Deep Iterative 2D/3D Registration - Jaganathan, Srikrishna; Wang, Jian; Borsdorf, Anja; Shetty, Karthik; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 2021)
Deep Learning-based 2D/3D registration methods are highly robust but often lack the necessary registration accuracy for clinical application. A refinement step using the classical optimization-based 2D/3D registration method applied in combination with Deep Learning-based techniques can provide the required accuracy. However, it also increases the runtime. In this work, we propose a novel Deep Learning driven 2D/3D registration framework that can be used end-to-end for iterative registration tasks without relying on any further refinement step. We accomplish this by learning the update step of the 2D/3D registration framework using Point-to-Plane Correspondences. The update step is learned using iterative residual refinement-based optical flow estimation, in combination with the Point-to-Plane correspondence solver embedded as a known operator. Our proposed method achieves an average runtime of around 8s, a mean re-projection distance error of 0.60±0.40 mm with a success ratio of 97% and a capture range of 60 mm. The combination of high registration accuracy, high robustness, and fast runtime makes our solution ideal for clinical applications.

Deep learning acceleration of Total Lagrangian Explicit Dynamics for soft tissue mechanics - Meister, Felix; Passerini, Tiziano; Mihalef, Viorel; Tuysuzoglu, Ahmet; Maier, Andreas; Mansi, Tommaso (Computer Methods in Applied Mechanics and Engineering, 2020)
Simulating complex soft tissue deformations has been an intense research area in the fields of computer graphics or computational physiology for instance. A desired property is the ability to perform fast, if not real-time, simulations while being physically accurate. Numerical schemes have been explored to speed up finite element methods, like the Total Lagrangian Explicit Dynamics (TLED). However, real-time applications still come at the price of accuracy and fidelity. In this work, we explore the use of neural networks as function approximators to accelerate the time integration of TLED, while being generic enough to handle various geometries, motion and materials without having to retrain the neural network model. The method is evaluated on a set of experiment, showing promising accuracy at time steps up to 20 times larger than the “breaking” time step, as well as in a simple medical application. Such an approach could pave the way to very fast but accurate acceleration strategies for computational biomechanics.
CRIS-Team Scopus Importer:2019-10-08
Keywords: Data-driven computational modeling; Deep learning; Finite element method; Soft tissue biomechanics; Total Lagrangian Explicit Dynamics

Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans - Denzinger, Felix; Wels, Michael; Breininger, Katharina; Reidelshöfer, Anika; Eckert, Joachim; Sühling, Michael; Schmermund, Axel; Maier, Andreas (BVM Workshop 2020, 2020)
Analysing coronary artery plaque segments with respect to their functional significance and therefore their influence to patient management in a non-invasive setup is an important subject of current research. In this work we compare and improve three deep learning algorithms for this task: A 3D recurrent convolutional neural network (RCNN), a 2D multi-view ensemble approach based on texture analysis, and a newly proposed 2.5D approach. Current state of the art methods utilising fluid dynamics based fractional flow reserve (FFR) simulation reach an AUC of up to 0.93 for the task of predicting an abnormal invasive FFR value. For the comparable task of predicting revascularisation decision, we are able to improve the performance in terms of AUC of both existing approaches with the proposed modifications, specifically from 0.80 to 0.90 for the 3D-RCNN, and from 0.85 to 0.90 for the multi-view texture-based ensemble. The newly proposed 2.5D approach achieves comparable results with an AUC of 0.90.<br

Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region - Aubreville, Marc; Bertram, Christof A.; Marzahl, Christian; Gurtner, Corinne; Dettwiler, Martina; Schmidt, Anja; Bartenschlager, Florian; Merz, Sophie; Fragoso, Marco; Kershaw, Olivia; Klopfleisch, Robert; Maier, Andreas (Scientific Reports, 2020)
Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963–0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count.
CRIS-Team Scopus Importer:2020-10-16

Deep Learning-based Adaptive Image Combination for Signal-Dropout Suppression in Liver DWI - Gadjimuradov, Fasil; Benkert, Thomas; Nickel, Marcel Dominik; Maier, Andreas (ISMRM & SMRT Annual Conference & Exhibition, 2021)

Deep Learning-Based Anonymization of Chest Radiographs: A Utility-Preserving Measure for Patient Privacy - Packhäuser, Kai; Gündel, Sebastian; Thamm, Florian; Denzinger, Felix; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, 2023)

Deep Learning based Attribute Representation in Ancient Vase Paintings - Madhu, Prathmesh; Kosti, Ronak Vijaypal; Bendschus, Torsten; Reinhardt, Corinna; Bell, Peter; Christlein, Vincent; Maier, Andreas (Digital Humanities 2020 - Intersections/Carrefours, 2020)
The understanding of iconography and visual narration in ancient imagery is one of the main foci in the field of Classical Archaeology, e.g. in Attic vase paintings of the fifth century B.C. In order to depict the situations and actions of a narrative as well as to characterise its protagonists, ancient Greek artists made use of a broad variety of often similar image elements [1]. The interaction and meaningful relationship of the protagonists is depicted with significant postures and gestures (schemata) in order to illustrate key aspects of the storyline [2, 3]. These schemes are not restricted to a certain iconography, so that visual links between different images occur. Being familiar with these relationships the ancient viewer could detect the specific narration and understand the meaning of the image. For example, the scheme of leading the bride in Attic vase paintings is characterised by a significant leading-gesture (χεῖρ’ ἐπὶ καρπῷ – hand on wrist / hand on hand) that relates bride and bridegroom in non-mythological wedding scenes, thereby expressing a hierarchy of the two figures in active or passive parts respectively. Both protagonists are connected in a communicative way defined by meaningful postures and gestures. Besides these descriptive schemes there are narrative elements, like the surrounding, specific attributes of gods or other objects, that contribute to the viewer’s understanding of narrative contexts. These other characteristic attributes are needed in order to determine the depiction as a specific mythological one. Scenes of Helen and Menelaos (dressed as warrior and threatening her with weapons), thus, make use of the leading the bride scheme to show that Menelaos leads Helen home from Troy, like a bride. Only by combining the formal schemes and narrative elements is it possible to determine the depiction as a certain mythological story and to understand its cultural meaning. We, therefore, need to recognize the narrative elements of the pictorial stories and to decode their formal key structures by comparing a high number of ancient images and by discovering visual similarities. It is necessary to target the understanding of complex semantic relationships and cultural references in a mixed-initiative-interaction between researcher and machine, and involve computer vision as a fourth Bildwissenschaft [4]. We approach this problem using state-of-the-art object recognition algorithms [5, 6] in computer vision. We present a novel approach using convolutional neural networks which maps the attributes occurring in Classical Archaeology into a deep representational space, through deep learning techniques. This representation maps the inherent variations in the representation of any particular attribute into a common space. A further novel method is developed that uses this representation space along with the context of the narrative for a better understanding of the depicted scene in the artwork. We also analyse the model performance with abstract classes where we combine multiple similar attributes into a single abstract class. This new approach has important applications, including the retrieval and contextual understanding of artworks [7] in Classical Archaeology. References: [1] Giuliani, L., 2003. Bild und Mythos. Geschichte der Bilderzählung in der griechischen Kunst, München: Beck 2003. [2] McNiven, T. J., 1982. Gestures in Attic Vase Painting: Use and Meaning, 550–450 B.C., Diss. Ann Arbor: University of Michigan. [3] Catoni, M. L., 2008. La comunicazione non verbale nella Grecia antica : gli schemata nella danza, nell'arte, nella vita, Turin: Bollati Boringhieri. [4] Panofsky, E., 2006. Ikonographie und Ikonologie: Bildinterpretation nach dem Dreistufenmodell, Cologne: DuMont. [5] Lin, T.Y., Goyal, P., Girshick, R., He, K. and Dollár, P., 2017. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988). [6] Redmon, J., Divvala, S., Girshick, R. and Farhadi, A., 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788). [7] Garcia, N., & Vogiatzis, G. (2019). How to read paintings: Semantic art understanding with multi-modal retrieval. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11130 LNCS, 676–691. https://doi.org/10.1007/978-3-030-11012-3_
Keywords: Vase Paintings; Machine Learning; Computer Vision

Deep Learning-based Cell Detection in Microscopy Images - Marzahl, Christian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2022)
In recent years, classical pathology has been in a constant state of change towards digital pathology. Digitisation forms the basis for supporting the work of pathologists in many ways by utilising computer-assisted methods. Among other things, modern methods of pattern recognition can accelerate previously time-consuming quantitative analyses, support reporting, and thus standardise and improve the results of pathological examinations. Digitalisation also offers new possibilities and ways of interdisciplinary cooperation and coordination between experts at an international level. This work presents novel methods and software that can be used to support both the analysis of image data and the cooperation between users. For these purposes, species-independent cell detection methods are developed and analysed, allowing the automatic quantification of pulmonary haemorrhage and asthma on cytological image data. The basis for developing these methods is qualitatively, and quantitatively comprehensive data sets created online and interdisciplinary with the annotation tool EXACT developed in this thesis. For cell detection, an established deep learning-based method of object recognition is optimised regarding resource consumption and training pipeline to meet the specific requirements of pathological problems such as image size and unbalanced annotation distribution. Employing the developed detection methods, algorithmically, pre-labelled images can be presented to the annotators for verification and thus, can efficiently increase existing data sets in quantity and quality. In this context, the thesis also investigates whether humans adopt algorithmic errors. The generalisation of algorithms across species and scanner boundaries is of great scientific and economic interest. Therefore, as an initial step for this dissertation, a dataset with human, feline and horse cell samples on the question of pulmonary haemorrhage is created which is the first of its kind in terms of both the number of annotated cells and the number of species. A registration method is developed in a second step that abstracts the pyramid representation of pathological images using a quadtree. In contrast to established methods, this approach does not require pre-segmentation or images with lower resolution and is the only one that can be applied natively to images with cytological questions. The findings and algorithms from the developments and studies described in this thesis are incorporated into the realisation of the online annotation tool EXACT in close cooperation with interdisciplinary experts. In contrast to established tools, EXACT places its focus on the traceability of annotation creation and various visualisation techniques to increase the quality of annotations. The presented methods for object recognition achieve accuracy that is comparable to the one achieved by human expert while at the same time increase the efficiency and reproducibility of grading cytological images. Thus, these methods form the basis for creating and publishing qualitatively and quantitatively comprehensive multi-species datasets using the annotation tool developed in this thesis. While the routine use of the developed algorithms still requires extensive cross-institutional studies that independently prove the added value of these procedures, the online annotation tool EXACT is already successfully used in several companies and research projects for the creation and registration of a broad range of dat

Deep Learning-Based Classification of Inflammatory Arthritis by Identification of Joint Shape Patterns—How Neural Networks Can Tell Us Where to “Deep Dive” Clinically - Folle, Lukas; Simon, David; Tascilar, Koray; Krönke, Gerhard; Liphardt, Anna-Maria; Maier, Andreas; Schett, Georg; Kleyer, Arnd (Frontiers in Medicine, 2022)
Keywords: artificial intelligence; arthritis; joint; bone; deep learning

Deep learning-based denoising of mammographic images using physics-driven data augmentation - Eckert, Dominik; Vesal, Sulaiman; Ritschl, Ludwig; Kappler, Steffen; Maier, Andreas (Informatik aktuell, 2020)
Mammography is using low-energy X-rays to screen the human breast and is utilized by radiologists to detect breast cancer. Typically radiologists require a mammogram with impeccable image quality for an accurate diagnosis. In this study, we propose a deep learning method based on Convolutional Neural Networks (CNNs) for mammogram denoising to improve the image quality. We first enhance the noise level and employ Anscombe Transformation (AT) to transform Poisson noise to white Gaussian noise. With this data augmentation, a deep residual network is trained to learn the noise map of the noisy images. We show, that the proposed method can remove not only simulated but also real noise. Furthermore, we also compare our results with state-of-the-art denoising methods, such as BM3D and DNCNN. In an early investigation, we achieved qualitatively better mammogram denoising results.
CRIS-Team Scopus Importer:2020-04-21

Deep learning based denoising of mammographic x-ray images: an investigation of loss functions and their detail-preserving properties - Eckert, Dominik; Ritschl, Ludwig; Herbst, Magdalena; Wicklein, Julia; Vesal, Sulaiman; Kappler, Steffen; Maier, Andreas; Stober, Sebastian (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2022)
Digital Breast Tomosynthesis (DBT) is becoming increasingly popular for breast cancer screening because of its high depth resolution. It uses a set of low-dose x-ray images called raw projections to reconstruct an arbitrary number of planes. These are typically used in further processing steps like backprojection to generate DBT slices or synthetic mammography images. Because of their low x-ray dose, a high amount of noise is present in the projections. In this study, the possibility of using deep learning for the removal of noise in raw projections is investigated. The impact of loss functions on the detail preservation is analized in particular. For that purpose, training data is augmented following the physics driven approach of Eckert et al.1 In this method, an x-ray dose reduction is simulated. First pixel intensities are converted to the number of photons at the detector. Secondly, Poisson noise is enhanced in the x-ray image by simulating a decrease in the mean photon arrival rate. The Anscombe Transformation2 is then applied to construct signal independent white Gaussian noise. The augmented data is then used to train a neural network to estimate the noise. For training several loss functions are considered including the mean square error (MSE), the structural similarity index (SSIM)3 and the perceptual loss.4 Furthermore the ReLU-Loss1 is investigated, which is especially designed for mammogram denoising and prevents the network from noise overestimation. The denoising performance is then compared with respect to the preservation of small microcalcifications. Based on our current measurements, we demonstrate that the ReLU-Loss in combination with SSIM improves the denoising results.
CRIS-Team Scopus Importer:2022-06-10
Keywords: Anscombe Transformation; Breast Cancer; DBT; Deep Learning; Denoising; Digital Breast Tomosynthesis; Loss Functions; Mammo-gram; X-Ray

Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images. - Aubreville, Marc; Stöve, Maike; Oetter, Nicolai; Goncalves, Miguel; Knipfer, Christian; Neumann, Helmut; Bohr, Christopher; Stelzle, Florian; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2019)
Probe-based confocal laser endomicroscopy (pCLE) is a subcellular in vivo imaging technique capable of producing images that enable diagnosis of malign structural modifications in epithelial tissue. Images acquired with pCLE are, however, often tainted by significant artifacts that impair diagnosis. This is especially detrimental for automated image analysis, which is why said images are often excluded from recognition pipelines. We present an approach for the automatic detection of motion artifacts in pCLE images and apply this methodology to a data set of 15 thousand images of epithelial tissue acquired in the oral cavity and the vocal folds. The approach is based on transfer learning from intermediate endpoints within a pre-trained Inception v3 network with tailored preprocessing. For detection within the non-rectangular pCLE images, we perform pooling within the activation maps of the network and evaluate this at different network depths. We achieved area under the ROC curve values of 0.92 with the proposed method, compared to 0.80 for the best feature-based machine learning approach. Our overall accuracy with the presented approach is 94.8%. Over traditional machine learning approaches with state-of-the-art features, we achieved significantly improved overall performance. PURPOSE METHODS RESULTS CONCLUSION

Deep learning-based ECG-free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging - Hoppe, Elisabeth; Wetzl, Jens; Yoon, Seung Su; Bacher, Mario; Roser, Philipp; Stimpel, Bernhard; Preuhs, Alexander; Maier, Andreas (IEEE Transactions on Medical Imaging, 2021)
For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is used. To further detect e.g., pathological tissue, multiple imaging contrasts enable a thorough diagnosis using magnetic resonance imaging (MRI). For this purpose, timecontinous and multi-contrast imaging protocols were proposed. The acquired signals are binned using navigation approaches for a motion-resolved reconstruction. Mostly, external sensors such as electrocardiograms (ECG) are used for navigation, leading to additional workflow efforts. Recent sensor-free approaches are based on pipelines requiring prior knowledge, e.g., typical heart rates. We present a sensor-free, deep learning-based navigation that diminishes the need for manual feature engineering or the necessity of prior knowledge compared to previous works. A classifier is trained to estimate the R-wave timepoints in the scan directly from the imaging data. Our approach is evaluated on 3-D protocols for continuous cardiac MRI, acquired in-vivo and free-breathing with single or multiple imaging contrasts. We achieve an accuracy of >98% on previously unseen subjects, and a well comparable image quality with the state-of-the-art ECG-based reconstruction. Our method enables an ECG-free workflow for continuous cardiac scans with simultaneous anatomic and functional imaging with multiple contrasts. It can be potentially integrated without adapting the sampling scheme to other continuous sequences by using the imaging data for navigation and reconstructio

Deep Learning-based Extended Field of View Computed Tomography Image Reconstruction: Influence of Network Design on Image Estimation Outside the Scan Field of View - Singh Khural, Bhupinder; Baer-Beck, Matthias; Fournie, Eric; Stierstorfer, Karl; Huang, Yixing; Maier, Andreas (Biomedical Physics and Engineering Express, 2022)
The problem of data truncation in Computed Tomography (CT) is caused by the missing data when the patient exceeds the Scan Field of View (SFOV) of a CT scanner. The reconstruction of a truncated scan produces severe truncation artifacts both inside and outside the SFOV. We have employed a deep learning-based approach to extend the field of view and suppress truncation artifacts. Thereby, our aim is to generate a good estimate of the real patient data and not to provide a perfect and diagnostic image even in regions beyond the SFOV of the CT scanner. This estimate could then be used as an input to higher order reconstruction algorithms [1]. To evaluate the influence of the network structure and layout on the results, three convolutional neural networks (CNNs), in particular a general CNN called ConvNet, an autoencoder, and the U-Net architecture have been investigated in this paper. Additionally, the impact of L1, L2, structural dissimilarity and perceptual loss functions on the neural network’s learning have been assessed and evaluated. The evaluation of data set comprising 12 truncated test patients demonstrated that the U-Net in combination with the structural dissimilarity loss showed the best performance in terms of image restoration in regions beyond the SFOV of the CT scanner. Moreover, this network produced the best mean absolute error, L1, L2, and structural dissimilarity evaluation measures on the test set compared to other applied networks. Therefore, it is possible to achieve truncation artifact removal using deep learning techniques.<br
Keywords: computed tomography; convolutional neural network; U-Net; autoencoder; extended field of view; truncation artifacts

Deep learning based hybrid OCT-OCTA segmentation of Bruch's membrane in pathology - Schottenhamml, Julia; Ploner, Stefan; Moult, Eric M.; Duker, Jay S.; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2019)
CRIS-Team WoS Importer:2019-11-01

Deep Learning-based Inpainting for Virtual DSA - Unberath, Mathias; Hajek, Jonas; Geimer, Tobias; Schebesch, Frank; Amrehn, Mario; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.deeple_3

Deep Learning Based Metal Inpainting in the Projection Domain: Initial Results - Gottschalk, Tristan; Kreher, Björn W.; Kunze, Holger; Maier, Andreas (Machine Learning for Medical Image Reconstruction, 2019)

Deep Learning Based Metal Inpainting in the Projection Domain using Additional Neighboring Projection Information - Gottschalk, Tristan; Kreher, Björn W.; Maier, Andreas (Proceedings of The 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020)

Deep Learning based Model Observers for Multi - Modal Imaging - Naderi Boldaji, Hamidreza; Patwari, Mayank; Reymann, Maximilian; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (16th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2021)
Model Observers (MO) are useful tools for assessing task-based image quality in medical imaging systems. Detection and characterization of anatomical structures in clinical imaging are some of the most significant tasks of MOs. Various linear model observers have been widely applied for these tasks with great success. However, we are unable to deploy such observers without a high prior knowledge of the task, the signal, and the background. Additionally, most model observers currently used in medical imaging research are used to optimize single modality systems such as standalone CT or PET scanners. In this paper, we aim to solve these problem by means of a Convolutional Neural Network (CNN). We employ supervised learning with CNNs to approximate the Human Observer (HO) for a signal-known-exactly and background-known-statistically (SKE/BKS) signal detection task. We use this observer in a novel multi - modality imaging setting. First, we propose a CNN-based anthropomorphic model observer to predict human observer detection performance for PET images. Then, we suggest another CNN-based MO for multi - modal PET and CT images. We trained both networks using 160 sets of joint PET/CT images of a dataset from the MICCAI2020 HECKTOR challenge, and tested on 41 patients from the same dataset. Our CNN - MOs have high accuracy in detecting the lesions present in the image (AUC = 0.95). Furthermore, our MOs degrade monotonically with increase in noise levels, similar to how a HO would. Including both modalities into an MO results in improved lesion detection performance, especially in the presence of image noise, compared to single modality CNN - MOs.<br
Keywords: CNN; Deep Learning; Model Observers; CT; PET

Deep learning-based motion quantification from k-space for fast model-based magnetic resonance imaging motion correction - Hoßbach, Julian; Splitthoff, Daniel Nicolas; Cauley, Stephen; Clifford, Bryan; Polak, Daniel; Lo, Wei-Ching; Meyer, Heiko; Maier, Andreas (Medical Physics, 2022)
BackgroundIntra-scan rigid-body motion is a costly and ubiquitous problem in clinical magnetic resonance imaging (MRI) of the head. PurposeState-of-the-art methods for retrospective motion correction in MRI are often computationally expensive or in the case of image-to-image deep learning (DL) based methods can be prone to undesired alterations of the image (hallucinations'). In this work we introduce a novel rigid-body motion correction method which combines the advantages of classical model-driven and data-consistency (DC) preserving approaches with a novel DL algorithm, to provide fast and robust retrospective motion correction. MethodsThe proposed Motion Parameter Estimating Densenet (MoPED) retrospectively estimates subject head motion during MRI acquisitions using a DL network with DenseBlocks and multitask learning. It quantifies the 2D rigid in-plane motion parameters slice-wise for each echo train (ET) of a Cartesian T2-weighted 2D Turbo-Spin-Echo sequence. The network receives a center patch of the motion corrupted k-space as well as an additional motion-free low-resolution reference scan to provide the ground truth orientation. The supervised training utilizes motion simulations based on 28 acquisitions with subject-wise training, validation, and test data splits of 70%, 23%, and 7%. During inference, MoPED is embedded in an iterative DC-driven motion correction algorithm which alternatingly updates estimates of the motion parameters and motion-corrected low-resolution k-space data. The estimated motion parameters are then used to reconstruct the final motion corrected image.The mean absolute/squared error and the Pearson correlation coefficient were used to analyze the motion parameter estimation quality on in-silico data in a quantitative evaluation. Structural similarity (SSIM), DC error and root mean squared error (RMSE) were used as metrics of image quality improvement. Furthermore, the generalization capability of the network was analyzed on two in-vivo motion volumes with 28 slices each and on one simulated T1-weighted volume. ResultsThe motion estimation achieves a Pearson correlation of 0.968 to the simulated ground-truth of the 2433 test data slices used. In-silico results indicate that MoPED decreases the time for the optimization by a factor of around 27 compared to a conventional method and is able to reduce the RMSE of the reconstructions and average DC error by more than a factor of two compared to uncorrected images. In-vivo experiments show a decrease in computation time by a factor of around 20, a RMSE decrease from 0.055 to 0.033 and an SSIM increase from 0.795 to 0.862. Furthermore, contrast independence is demonstrated as MoPED is also able to correct T1-weighted images in simulations without retraining. Due to the model-based correction, no hallucinations were observed. ConclusionsIncorporating DL in a model-based motion correction algorithm shows great benefit on the optimization and computation time. The k-space-based estimation also allows a data consistent correction and therefore avoids the risk of hallucinations of image-to-image approaches.
CRIS-Team WoS Importer:2022-12-30

Deep Learning-based Partial Fourier Reconstruction for Improved Prostate DWI - Gadjimuradov, Fasil; Yoon, Seung Su; Benkert, Thomas; Nickel, Marcel Dominik; Engelhard, Karl; Maier, Andreas (International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition, 2020)

Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data - Packhäuser, Kai; Gündel, Sebastian; Münster, Nicolas; Syben, Christopher; Christlein, Vincent; Maier, Andreas (Scientific Reports, 2022)
With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains sensitive patient-related information and is therefore usually anonymized by removing patient identifiers, e.g., patient names before publication. To the best of our knowledge, we are the first to show that a well-trained deep learning system is able to recover the patient identity from chest X-ray data. We demonstrate this using the publicly available large-scale ChestX-ray14 dataset, a collection of 112,120 frontal-view chest X-ray images from 30,805 unique patients. Our verification system is able to identify whether two frontal chest X-ray images are from the same person with an AUC of 0.9940 and a classification accuracy of 95.55%. We further highlight that the proposed system is able to reveal the same person even ten and more years after the initial scan. When pursuing a retrieval approach, we observe an mAP@R of 0.9748 and a precision@1 of 0.9963. Furthermore, we achieve an AUC of up to 0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks on external datasets such as CheXpert and the COVID-19 Image Data Collection. Based on this high identification rate, a potential attacker may leak patient-related information and additionally cross-reference images to obtain more information. Thus, there is a great risk of sensitive content falling into unauthorized hands or being disseminated against the will of the concerned patients. Especially during the COVID-19 pandemic, numerous chest X-ray datasets have been published to advance research. Therefore, such data may be vulnerable to potential attacks by deep learning-based re-identification algorithms.<br

Deep Learning-based Pipeline for Module Power Prediction from EL Measurements - Hoffmann, Mathis; Buerhop-Lutz, Claudia; Reeb, Luca; Pickel, Tobias; Winkler, Thilo; Doll, Bernd; Würfl, Tobias; Peters, Ian Marius; Brabec, Christoph; Maier, Andreas; Christlein, Vincent (Progress in Photovoltaics: Research and Applications, 2021)
Automated inspection plays an important role in monitoring large-scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of defects on solar modules but have not been used to determine the power of a module. However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string. By now, this is commonly determined by measurements that require to discontact or even dismount the module, rendering a regular inspection of individual modules infeasible. In this work, we bridge the gap between electroluminescense measurements and the power determination of a module. We compile a large dataset of 719 electroluminescense measurementsof modules at various stages of degradation, especially cell cracks and fractures, and the corresponding power at maximum power point. Here,we focus on inactive regions and cracks as the predominant type of defect. We set up a baseline regression model to predict the power from electroluminescense measurements with a mean absolute error of 9.0+/-3.7W (4.0+/-8.4%). Then, we show that deep-learning can be used to train a model that performs significantly better (7.3+/-2.7W or 3.2+/-6.5%). With this work, we aim to open a new research topic. Therefore, we publicly release the dataset, the code and trained models to empower other researchers to compare against our results. Finally, we present a thorough evaluation of certain boundary conditions like the dataset size and an automated preprocessing pipeline for on-site measurements showing multiple modules at once.

Deep Learning based Prediction of Glioma Growth by using "Growing Neural Cellular Automata" - Huang, Yixing; Maier, Andreas; Sommer, Philipp; Fischer, Stefan; Srinivasan, Aishwarya Lakshmi; Fietkau, Rainer; Bert, Christoph; Putz, Florian (STRAHLENTHERAPIE UND ONKOLOGIE, 2022)
CRIS-Team WoS Importer:2022-06-03

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides - Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Stayt, Jason; Jasensky, Anne Katherine; Bartenschlager, Florian; Fragoso-Garcia, Marco; Barton, Ann K.; Elsemann, Svenja; Jabari, Samir; Krauth, Jens; Madhu, Prathmesh; et al. (Scientific Reports, 2020)
Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter- and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss’ kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter- and intra-rater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI.
CRIS-Team Scopus Importer:2020-08-14

Deep Learning-Based Respiratory Motion Correction in Free-Breathing Abodiminal Diffusion-Weighted Imaging - Kim, Jinho; Gadjimuradov, Fasil; Benkert, Thomas; Vahle, Thomas; Maier, Andreas (Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, 2022)

Deep Learning based T2 distribution estimation for bone marrow characterization in inflammation and aging - Brock, Luise; Ben-Atya, Hadas; Saar, Galit; Folle, Lukas; Maier, Andreas; Freiman, Moti; Vandoorne, Katrien (European Molecular Imaging Meeting (EMIM), 2023)

Deep Learning basierte Vorhersage von Gliomwachstum mittels Growing Neural Cellular Automata - Huang, Yixing; Maier, Andreas; Sommer, Phillip; Fischer, Stefan; Srinivasan, Aishwarya Lakshmi; Fietkau, Rainer; Bert, Christoph; Putz, Florian (Strahlentherapie und Onkologie, 2022)
Keywords: deep learning; cellular automata; tumor growth

Deep Learning Compatible Differentiable X-ray Projections for Inverse Rendering - Shetty, Karthik; Birkhold, Annette; Strobel, Norbert; Jaganathan, Srikrishna; Kowarschik, Markus; Maier, Andreas; Egger, Bernhard (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Deep Learning Computed Tomography - Würfl, Tobias; Ghesu, Florin-Cristian; Christlein, Vincent; Maier, Andreas (Medical Image Computing and Computer-Assisted Intervention MICCAI 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.deeple

Deep Learning Computed Tomography: Learning Projection-Domain Weights from Image Domain in Limited Angle Problems - Würfl, Tobias; Hoffmann, Mathis; Christlein, Vincent; Breininger, Katharina; Huang, Yixing; Unberath, Mathias; Maier, Andreas (IEEE Transactions on Medical Imaging, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.deeple
Keywords: Image reconstruction; Neural networks; Machine learning; Geometry; Reconstruction algorithms; Iterative methods; Computed tomography

Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy - Weissmann, Thomas; Huang, Yixing; Fischer, Stefan; Roesch, Johannes; Mansoorian, Sina; Gaona, Horacio Ayala; Gostian, Antoniu-Oreste; Hecht, Markus; Lettmaier, Sebastian; Deloch, Lisa; Frey, Benjamin; Gaipl, Udo; et al. (Frontiers in Oncology, 2023)
Background: Deep learning-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. In particular, there is no publicly available open-source solution for large-scale autosegmentation of HN_LNL in the research setting. Methods: An expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n = 20). In a completely blinded evaluation, 3 clinical experts rated the quality of deep learning autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average deep learning autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect of autocontour consistency with CT slice plane orientation on geometric accuracy and expert rating was investigated. Results: Blinded expert ratings for deep learning segmentations and expert-created contours were not significantly different. Deep learning segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6, p = 0.185) and deep learning segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6, p = 0.167) than manually drawn contours. In a head-to-head comparison, deep learning segmentations with CT slice plane adjustment were rated significantly better than deep learning contours without slice plane adjustment (81.0 vs. 77.2, p = 0.004). Geometric accuracy of deep learning segmentations was not different from intraobserver variability (mean Dice per level, 0.76 vs. 0.77, p = 0.307). Clinical significance of contour consistency with CT slice plane orientation was not represented by geometric accuracy metrics (volumetric Dice, 0.78 vs. 0.78, p = 0.703). Conclusions: We show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting. Geometric accuracy metrics are only an imperfect surrogate for blinded expert rating. <
Keywords: Deep learning; lymph node level; segmentation

Deep learning for brain metastasis detection and segmentation in longitudinal MRI data - Huang, Yixing; Bert, Christoph; Sommer, Philipp; Frey, Benjamin; Gaipl, Udo; Distel, Luitpold; Weissmann, Thomas; Uder, Michael; Schmidt, Manuel; Dörfler, Arnd; Maier, Andreas; Fietkau, Rainer; et al. (Medical Physics, 2022)
Keywords: Deep learning; brain metastasis; MRI; loss function; sensitivity specificity

Deep Learning for Magnetic Resonance Fingerprinting: Accelerating the Reconstruction of Quantitative Relaxation Maps - Hoppe, Elisabeth; Körzdorfer, Gregor; Nitka, Mathias; Würfl, Tobias; Wetzl, Jens; Lugauer, Felix; Schneider, Manuel; Pfeuffer, Josef; Maier, Andreas (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (26th Annual Meeting & Exhibition), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.deeple_0
Keywords: Quantitative Magnetic Resonance Imaging; Neural Networks; Convolutional Neural Networks

Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series - Hoppe, Elisabeth; Körzdorfer, Gregor; Würfl, Tobias; Wetzl, Jens; Lugauer, Felix; Pfeuffer, Josef; Maier, Andreas (German Medical Data Sciences: Visions and Bridges, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.deeple_2
Keywords: Deep Learning; Magnetic Resonance Imaging

Deep Learning for Orca Call Type Identification – A Fully Unsupervised Approach - Bergler, Christian; Schmitt, Manuel; Cheng, Rachael Xi; Maier, Andreas; Barth, Volker; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019)
Call type classification is an important instrument in bioacoustic investigating group-specific vocal repertoire, behavioral patterns, and cultures of different animal groups. There is a need using robust machine-based techniques to replace classification due to its advantages in handling large delivering consistent results, removing perceptual-based classification, and minimizing human errors. The current work is the first adopting a two-stage fully unsupervised approach on previous machine-segmented orca data to identify orca sound types using deep learning together with one of the largest bioacoustic datasets – the Orchive. The proposed methods include: (1) unsupervised feature learning using an undercomplete ResNet18-autoencoder trained on machine-annotated data, and (2) spectral clustering utilizing compressed orca feature representations. An existing human-labeled orca dataset was clustered, including 514 signals distributed over 12 classes. This two-stage fully unsupervised approach is an initial study to (1) examine machine-generated clusters against human-identified orca call type classes, (2) compare supervised call type classification versus unsupervised call type clustering, and (3) verify the general feasibility of a completely unsupervised approach based on machine-labeled orca data resulting in a major progress within the research field of animal linguistics, by deriving a much deeper understanding and facilitating totally new insights and opportunities.
CRIS-Team Scopus Importer:2019-11-19
Keywords: orca; call type; unsupervised; deep learning; clustering

Deep learning for perspective deformation correction in X-ray imaging - Huang, Yixing; Maier, Andreas; Fan, Fuxin; Kreher, Björn W.; Huang, Xiaolin; Fietkau, Rainer; Bert, Christoph; Putz, Florian (Radiotherapy and Oncology, 2023)
Keywords: Deep learning; perspective deformation; X-ray imaging

Deep learning for terahertz image denoising in nondestructive historical document analysis - Dutta, Balaka; Root, Konstantin; Ullmann, Ingrid; Wagner, Fabian; Mayr, Martin; Seuret, Mathias; Thies, Mareike; Stromer, Daniel; Christlein, Vincent; Schür, Jan; Maier, Andreas; Huang, Yixing (Scientific Reports, 2022)
Historical documents contain essential information about the past, including places, people, or events. Many of these valuable cultural artifacts cannot be further examined due to aging or external influences, as they are too fragile to be opened or turned over, so their rich contents remain hidden. Terahertz (THz) imaging is a nondestructive 3D imaging technique that can be used to reveal the hidden contents without damaging the documents. As noise or imaging artifacts are predominantly present in reconstructed images processed by standard THz reconstruction algorithms, this work intends to improve THz image quality with deep learning. To overcome the data scarcity problem in training a supervised deep learning model, an unsupervised deep learning network (CycleGAN) is first applied to generate paired noisy THz images from clean images (clean images are generated by a handwriting generator). With such synthetic noisy-to-clean paired images, a supervised deep learning model using Pix2pixGAN is trained, which is effective to enhance real noisy THz images. After Pix2pixGAN denoising, 99% characters written on one-side of the Xuan paper can be clearly recognized, while 61% characters written on one-side of the standard paper are sufficiently recognized. The average perceptual indices of Pix2pixGAN processed images are 16,83, which is very close to the average perceptual index 16.19 of clean handwriting images. Our work has important value for THz-imaging-based nondestructive historical document analysis.<br
Keywords: Deep learning; Terahertz imaging; Historical document analysis; Nondestructive testing

Deep learning fundamentals - Breininger, Katharina; Christlein, Vincent; Würfl, Tobias; Maier, Andreas (Informatik aktuell, 2018)
Deep learning has received a lot of attention in the machine learning community. Successful applications from speech recognition or computer vision are already part of our daily life. Much effort has been devoted to transferring this success to medical image computing.
CRIS-Team Scopus Importer:2020-11-20

Deep Learning Glaucoma Classification in enface OCTA scans of the human macular region - Schottenhamml, Julia; Ploner, Stefan; Husvogt, Lennart; Mardin, Sophia; Hohberger, Bettina; Mardin, Christian Y.; Maier, Andreas (ARVO Annual Meeting 2021, 2021)
CRIS-Team WoS Importer:2021-09-24

Deep Learning-Guided Weighted Averaging for Signal Dropout Compensation in DWI of the Liver - Gadjimuradov, Fasil; Benkert, Thomas; Nickel, Marcel Dominik; Führes, Tobit; Saake, Marc; Maier, Andreas (Magnetic Resonance in Medicine, 2022)
Purpose To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal DWI resulting from cardiac motion. Methods Given a set of image repetitions for a slice, a locally adaptive weighted averaging is proposed that aims to suppress the contribution of image regions affected by signal dropouts. Corresponding weight maps were estimated by a sliding-window algorithm, which analyzed signal deviations from a patch-wise reference. In order to ensure the computation of a robust reference, repetitions were filtered by a classifier that was trained to detect images corrupted by signal dropouts. The proposed method, named Deep Learning-guided Adaptive Weighted Averaging (DLAWA), was evaluated in terms of dropout suppression capability, bias reduction in the ADC, and noise characteristics. Results In the case of uniform averaging, motion-related dropouts caused signal attenuation and ADC overestimation in parts of the liver, with the left lobe being affected particularly. Both effects could be substantially mitigated by DLAWA while preventing global penalties with respect to SNR due to local signal suppression. Performing evaluations on patient data, the capability to recover lesions concealed by signal dropouts was demonstrated as well. Further, DLAWA allowed for transparent control of the trade-off between SNR and signal dropout suppression by means of a few hyperparameters. Conclusion This work presents an effective and flexible method for the local compensation of signal dropouts resulting from motion and pulsation. Because DLAWA follows a retrospective approach, no changes to the acquisition are required.
CRIS-Team WoS Importer:2022-08-12

Deep Learning in Surgical Workflow Analysis: A Review of Phase and Step Recognition - Demir, Kubilay Can; Schieber, Hannah; Weise, Tobias; May, Matthias; Maier, Andreas; Yang, Seung Hee (IEEE Journal of Biomedical and Health Informatics, 2023)
Objective: In the last two decades, there has been a growing interest in exploring surgical procedures with statistical models to analyze operations at different semantic levels. This information is necessary for developing context-aware intelligent systems, which can assist the physicians during operations, eval-uate procedures afterward or help the management team to effec-tively utilize the operating room. The objective is to extract reliable patterns from surgical data for the robust estimation of surgical activities performed during operations. The purpose of this paper is to review the state-of-the-art deep learning methods that have been published after 2018 for analyzing surgical workflows, with a focus on phase and step recognition. Methods: Three databases, IEEE Xplore, Scopus, and PubMed were searched, and additional studies are added through a manual search. After the database search, 343 studies were screened and a total of 45 studies are selected for this review. Conclusion: The use of temporal information is essential for identifying the next surgical action. Contemporary methods used mainly RNNs, hierarchical CNNs, and Transformers to preserve long-distance temporal relations. The lack of large publicly available datasets for various procedures is a great challenge for the development of new and robust models. As supervised learning strategies are used to show proof-of-concept, self-supervised, semi-supervised, or active learning methods are used to mitigate dependency on annotated data. Significance: The present study provides a comprehensive review of recent methods in surgical workflow analysis, summarizes commonly used archi-tectures, datasets, and discusses challenges.
CRIS-Team Scopus Importer:2023-09-29
Keywords: Analytical models; Cataracts; Databases; Deep learning; deep learning; Laparoscopes; Recording; Surgery; surgical data science; surgical phase recognition; Surgical workflow analysis

Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density - Folle, Lukas; Meinderink, Timo; Simon, David; Liphardt, Anna-Maria; Krönke, Gerhard; Schett, Georg; Kleyer, Arnd; Maier, Andreas (Scientific Reports, 2021)
Arthritis patients develop hand bone loss, which leads to destruction and functional impairment of the affected joints. High resolution peripheral quantitative computed tomography (HR-pQCT) allows the quantification of volumetric bone mineral density (vBMD) and bone microstructure in vivo with an isotropic voxel size of 82 micrometres. However, image-processing to obtain bone characteristics is a time-consuming process as it requires semi-automatic segmentation of the bone. In this work, a fully automatic vBMD measurement pipeline for the metacarpal (MC) bone using deep learning methods is introduced. Based on a dataset of HR-pQCT volumes with MC measurements for 541 patients with arthritis, a segmentation network is trained. The best network achieves an intersection over union as high as 0.94 and a Dice similarity coefficient of 0.97 while taking only 33 s to process a whole patient yielding a speedup between 2.5 and 4.0 for the whole workflow. Strong correlation between the vBMD measurements of the expert and of the automatic pipeline are achieved for the average bone density with 0.999 (Pearson) and 0.996 (Spearman’s rank) with p < 0.001for all correlations. A qualitative assessment of the network predictions and the manual annotations yields a 65.9% probability that the expert favors the network predictions. Further, the steps to integrate the pipeline into the clinical workflow are shown. In order to make these workflow improvements available to others, we openly share the code of this work.
Keywords: Rheumatic diseases; segmentation; deep learning

Deep Multi-Frame Filtering for Hearing Aids - Schröter, Hendrik; Rosenkranz, Tobias; Escalante-B, Alberto N.; Maier, Andreas (INTERSPEECH 2023, 2023)
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep filtering (DF) recently demonstrated its capabilities for low-latency scenarios like hearing aids with its complex multi-frame (MF) filter. Alternatively, the complex filter can be estimated via an MF minimum variance distortionless response (MVDR), or MF Wiener filter (WF). Previous studies have shown that incorporating algorithm domain knowledge using an MVDR filter might be beneficial compared to the direct filter estimation via DF. In this work, we compare the usage of various multi-frame filters such as DF, MF-MVDR, or MF-WF for HAs. We assess different covariance estimation methods for both MF-MVDR and MF-WF and objectively demonstrate an improved performance compared to direct DF estimation, significantly outperforming related work while improving the runtime performance.
Keywords: hearing aids; speech enhancement; multi-frame filtering

Deepnapsi: Deep Learning for Nail Psoriasis Prediction - Folle, Lukas; Fenzl, Pauline; Fagni, Filippo; Thies, Mareike; Christlein, Vincent; Meder, Christine; Sticherling, Michael; Simon, David; Schett, Georg; Maier, Andreas; Kleyer, Arnd (Proceedings - International Symposium on Biomedical Imaging, 2023)
Nail psoriasis is a frequent condition that is associated with a severe course of rheumatic diseases. Thus, it may be used to adapt the therapy of patients according to the change of nail psoriasis. The nail psoriasis severity index (NAPSI) was developed to measure this condition. Nevertheless, there is a lack of application for the NAPSI, as its use in the clinic is too time-consuming. In this work, we propose the use of advancements in deep learning in a new domain by recording, annotating, and predicting the NAPSI based on photographs of the hand and term our approach DeepNAPSI. This allows not just the automated recording of the NAPSI in the clinic, but also patient self-assessment from home. Our method achieved an area-under-receiver-operator-characteristic curve (AUROC) of 0.83 and 0.86 for macro and micro averaging, respectively, and a mean absolute error of 0.55.
CRIS-Team Scopus Importer:2023-10-06
Keywords: Deep Learning; Mobile device; Nail psoriasis; Precision medicine

DeepNAPSI multi-reader nail psoriasis prediction using deep learning - Folle, Lukas; Fenzl, Pauline; Fagni, Filippo; Thies, Mareike; Christlein, Vincent; Meder, Christine; Simon, David; Minnopoulou, Ioanna; Sticherling, Michael; Schett, Georg; Maier, Andreas; Kleyer, Arnd (Nature Scientific Reports, 2023)
Nail psoriasis occurs in about every second psoriasis patient. Both, finger and toe nails can be affected and also severely destroyed. Furthermore, nail psoriasis is associated with a more severe course of the disease and the development of psoriatic arthritis. User independent quantification of nail psoriasis, however, is challenging due to the heterogeneous involvement of matrix and nail bed. For this purpose, the nail psoriasis severity index (NAPSI) has been developed. Experts grade pathological changes of each nail of the patient leading to a maximum score of 80 for all nails of the hands. Application in clinical practice, however, is not feasible due to the time-intensive manual grading process especially if more nails are involved. In this work we aimed to automatically quantify the modified NAPSI (mNAPSI) of patients using neuronal networks retrospectively. First, we performed photographs of the hands of patients with psoriasis, psoriatic arthritis, and rheumatoid arthritis. In a second step, we collected and annotated the mNAPSI scores of 1154 nail photos. Followingly, we extracted each nail automatically using an automatic key-point-detection system. The agreement among the three readers with a Cronbach’s alpha of 94% was very high. With the nail images individually available, we trained a transformer-based neural network (BEiT) to predict the mNAPSI score. The network reached a good performance with an area-under-receiver-operator-curve of 88% and an area-under precision-recall-curve (PR-AUC) of 63%. We could compare the results with the human annotations and achieved a very high positive Pearson correlation of 90% by aggregating the predictions of the network on the test set to the patient-level. Lastly, we provided open access to the whole system enabling the use of the mNAPSI in clinical practice.

Deep Neural Networks for Motion Estimation in k-space: Applications and Design - Hoßbach, Julian; Splitthoff, Daniel; Haskell, Melissa; Cauley, Stephen; Meyer, Heiko; Pfeuffer, Josef; Maier, Andreas (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, 2019)

Deep OCT angiography image generation for motion artifact suppression - Hoßbach, Julian; Husvogt, Lennart; Kraus, Martin; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2020)
Eye movements, blinking and other motion during the acquisition of optical coherence tomography (OCT) can lead to artifacts, when processed to OCT angiography (OCTA) images. Affected scans emerge as high intensity (white) or missing (black) regions, resulting in lost information. The aim of this research is to fill these gaps using a deep generative model for OCT to OCTA image translation relying on a single intact OCT scan. Therefore, a U-Net is trained to extract the angiographic information from OCT patches. At inference, a detection algorithm finds outlier OCTA scans based on their surroundings, which are then replaced by the trained network. We show that generative models can augment the missing scans. The augmented volumes could then be used for 3-D segmentation or increase the diagnostic value.
CRIS-Team Scopus Importer:2020-04-21

Deep Recurrent Partial Fourier Reconstruction in Diffusion MRI - Gadjimuradov, Fasil; Benkert, Thomas; Nickel, Marcel Dominik; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Partial Fourier (PF) acquisition schemes are often employed to increase the inherently low SNR in diffusion-weighted (DW) images. The resulting ill-posed reconstruction problem can be tackled by an iterative Projection Onto Convex Sets (POCS). By relaxing the data constraint and replacing the heuristically chosen regularization by learned convolutional filters, we arrive at an unrolled recurrent network architecture which circumvents weaknesses of the conventional POCS. Further, knowledge on the pixel-wise noise level of MR images is incorporated into data consistency operations within the reconstruction network. We are able to demonstrate on DW images of the pelvis that the proposed model quantitatively and qualitatively outperforms conventional methods as well as a U-Net representing a direct image-to-image mapping.
CRIS-Team Scopus Importer:2020-12-04
Keywords: Deep learning; Diffusion MRI; Partial Fourier

Deep Representation Learning for Orca Call Type Classification - Bergler, Christian; Schmitt, Manuel; Cheng, Rachael Xi; Schröter, Hendrik; Maier, Andreas; Barth, Volker; Weber, Michael; Nöth, Elmar (Text, Speech, and Dialogue, 22nd International Conference, TSD 2019, Ljubljana, Slovenia, September 11–13, 2019, Proceedings, 2019)
Marine mammals produce a wide variety of vocalizations. There is a growing need for robust automatic classification methods especially in noisy underwater environments in order to access large amounts of bioacoustic signals and to replace tedious and error prone human perceptual classification. In case of the northern resident killer whale (Orcinus orca), echolocation clicks, whistles, and pulsed calls make up its vocal repertoire. Pulsed calls are the most intensively studied type of vocalization. In this study we propose a hybrid call type classification approach outperforming our previous work on supervised call type classification consisting of two components: (1) deep representation learning of killer whale sounds by investigating various autoencoder architectures and data corpora and (2) subsequent supervised training of a ResNet18 call type classifier on a much smaller dataset by using the pre-trained representations. The best semi-supervised trained classification model achieved a test accuracy of 96% and a mean test accuracy of 94% outperforming our previous work by 7% points.
CRIS-Team Scopus Importer:2019-10-15
Keywords: Bioacoustics; Call type; Classification; Deep learning; Killer whale; Orca; Representation learning

Deep Scatter Splines: Learning-Based Medical X-ray Scatter Estimation Using B-splines - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (The 6th International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2020)

Defining a baseline identification of artifacts in confocal laser endomicroscopy in head and neck cancer imaging - Pan, Zhaoya; Breininger, Katharina; Aubreville, Marc; Stelzle, Florian; Maier, Andreas; Oetter, Nicolai; Mantsopoulos, Konstantinos; Iro, Heinrich; Goncalves, Miguel; Sievert, Matti (American Journal of Otolaryngology, 2023)
Purpose: Probe-based confocal laser endomicroscopy (pCLE) allows imaging of the laryngeal mucosa in a thousand-fold magnification. However, the existence of multiple nondiagnostic frames might hinder the performance of automated systems, especially in post-processing and analysis algorithms. This study aims to investigate the feasibility of building a baseline for the identification of diagnostic frames and to evaluate the influence of sequence information in this task. Materials and methods: We included eight patients with squamous cell carcinoma (SCC) and planned total laryngectomy between October 2020 and February 2021. One hundred frames were randomly selected out of 21,272 pCLE frames of the tumor and healthy mucosa during surgery. Three experts were asked to classify these frames into diagnostic and nondiagnostic ones with different subtypes under two settings, i.e., isolated frames or short video sequences. Inter-rater and intra-rater consistency were analyzed with Cohen's kappa coefficients. Results: Among all raters, regarding the classification of diagnostic frames, an average agreement of κ = 0.79 has been achieved. The overall consistency for the subtypes of nondiagnostic frames is κ = 0.75 and κ = 0.57, respectively. Conclusions: The high level of consistency regarding the identification of diagnostic frames shows the reliability of the classification results and builds the foundation for future automatic algorithms.
CRIS-Team Scopus Importer:2023-01-06
Keywords: Classification system; Confocal laser endomicroscopy; Head and neck cancer; Larynx; Non-invasive histological imaging; Pharynx

Deformable Respiratory Motion Correction for Hepatic Rotational Angiography - Klugmann, Alexandra; Bier, Bastian; Müller, Kerstin; Maier, Andreas; Unberath, Mathias (Computerized Medical Imaging and Graphics, 2018)
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.deform
Keywords: Discrete optimization; Image reconstruction; Interventional imaging; Motion modeling

Degenerating u-net on retinal vessel segmentation: What do we really need? - Fu, Weilin; Breininger, Katharina; Pan, Zhaoya; Maier, Andreas (Informatik aktuell, 2020)
Retinal vessel segmentation is an essential step for fundus image analysis. With the recent advances of deep learning technologies, many convolutional neural networks have been applied in this field, including the successful U-Net. In this work, we firstly modify the U-Net with functional blocks aiming to pursue higher performance. The absence of the expected performance boost then lead us to dig into the opposite direction of shrinking the U-Net and exploring the extreme conditions such that its segmentation performance is maintained. Experiment series to simplify the network structure, reduce the network size and restrict the training conditions are designed. Results show that for retinal vessel segmentation on DRIVE database, U-Net does not degenerate until surprisingly acute conditions: one level, one filter in convolutional layers, and one training sample. This experimental discovery is both counter-intuitive and worthwhile. Not only are the extremes of the U-Net explored on a well-studied application, but also one intriguing warning is raised for the research methodology which seeks for marginal performance enhancement regardless of the resource cost.
CRIS-Team Scopus Importer:2020-04-21

Denoising and artefact reduction in dynamic flat detector CT perfusion imaging using high speed acquisition: first experimental and clinical results - Manhart, Michael; Aichert, André; Struffert, Tobias; Deuerling-Zheng, Yu; Kowarschik, Markus; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Physics in Medicine and Biology, 2014)
Flat detector CT perfusion (FD-CTP) is a novel technique using C-arm angiography systems for interventional dynamic tissue perfusion measurement with high potential benefits for catheter-guided treatment of stroke. However, FD-CTP is challenging since C-arms rotate slower than conventional CT systems. Furthermore, noise and artefacts affect the measurement of contrast agent flow in tissue. Recent robotic C-arms are able to use high speed protocols (HSP), which allow sampling of the contrast agent flow with improved temporal resolution. However, low angular sampling of projection images leads to streak artefacts, which are translated to the perfusion maps. We recently introduced the FDK-JBF denoising technique based on Feldkamp (FDK) reconstruction followed by joint bilateral filtering (JBF). As this edge-preserving noise reduction preserves streak artefacts, an empirical streak reduction (SR) technique is presented in this work. The SR method exploits spatial and temporal information in the form of total variation and time-curve analysis to detect and remove streaks. The novel approach is evaluated in a numerical brain phantom and a patient study. An improved noise and artefact reduction compared to existing post-processing methods and faster computation speed compared to an algebraic reconstruction method are achieved. © 2014 Institute of Physics and Engineering in Medicine.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.denois_8
Keywords: 3D reconstruction; flat detector CT; noise reduction; perfusion imaging; stroke treatment

Depression assessment in people with Parkinson's disease: The combination of acoustic features and natural language processing - Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Klumpp, Philipp; Vásquez-Correa, Juan Camilo; Schuster, Maria; Nöth, Elmar; Orozco Arroyave, Juan Rafael (Speech Communication, 2022)
Parkinson's disease produces motor impairments such as bradykinesia, rigidity, and different speech impairments, as same as non-motor symptoms like cognitive decline and depression disturbances. Most studies are focused on the analysis of motor symptoms, and only few works study non-motor impairments. Depression is one of the typical non-motor symptoms developed by many Parkinson's patients. Impairments in speech production together with depression produce negative effects in the communication capabilities and social interaction of patients. This study proposes a combination of speech analysis and natural language processing methods to extract features from spontaneous speech utterances and their transcripts. We consider state-of-the-art word-embedding methods like Bidirectional Encoder Representations from Transformer (BERT) to process the transcripts, and traditional acoustic features such as Bark band energies and Mel frequency cepstral coefficients to model the speech signals. The features are processed with supervectors generated by Gaussian Mixture Model-Universal Background Model (GMM-UBM) and Support Vector Machine (SVM) classifiers. The dataset consists of 60 Parkinson's patients divided into two classes according to the depression item of the MDS-UPDRS. The automatic classification of depressed and non-depressed Parkinson's patients showed F-scores of up to 0.77, which confirms that acoustic and linguistic information embedded in language production can be used for depression analysis in Parkinson's patients. We present one of the few studies that evaluates depression in Parkinson's patients considering the combination of acoustic and linguistic information.
CRIS-Team Scopus Importer:2022-10-28
Keywords: Acoustics analysis; Depression; Natural language processing; Parkinson's disease

Deriving Neural Network Architectures Using Precision Learning: Parallel-to-Fan Beam Conversion - Syben-Leisner, Christopher; Stimpel, Bernhard; Lommen, Jonathan; Würfl, Tobias; Dörfler, Arnd; Maier, Andreas (40th German Conference on Pattern Recognition, GCPR 2018, 2019)
Keywords: Hybrid MRI/X-ray imaging; Precision learning; Machine learning

Design and evaluation of a portable intra-operative unified-planning-and-guidance framework applied to distal radius fracture surgery - Magaraggia, Jessica; Wei, Wei; Weiten, Markus; Kleinszig, Gerhard; Vetter, Sven; Franke, Jochen; John, Adrian; Egli, Adrian; Barth, Karl; Angelopoulou, Elli; Hornegger, Joachim (International Journal of Computer Assisted Radiology and Surgery, 2017)
Purpose: During a standard fracture reduction and fixation procedure of the distal radius, only fluoroscopic images are available for planning of the screw placement and monitoring of the drill bit trajectory. Our prototype intra-operative framework integrates planning and drill guidance for a simplified and improved planning transfer. Methods: Guidance information is extracted using a video camera mounted onto a surgical drill. Real-time feedback of the drill bit position is provided using an augmented view of the planning X-rays. We evaluate the accuracy of the placed screws on plastic bones and on healthy and fractured forearm specimens. We also investigate the difference in accuracy between guided screw placement versus freehand. Moreover, the accuracy of the real-time position feedback of the drill bit is evaluated. Results: A total of 166 screws were placed. On 37 plastic bones, our obtained accuracy was 1.01±0.56 mm, 3.74∘±4.39∘ and 1.70∘±1.35∘ in tip position and orientation (azimuth and elevation), respectively. On the three healthy forearm specimens, our obtained accuracy was 1.63 ± 0.91 mm, 5. 85 ± 4. 93 and 3. 48 ± 3. 07 . On the two fractured specimens, we attained: 1.39 ± 0.47 mm, 2. 93 ± 1. 83 and 2. 14 ± 1. 84 . When screw plans were applied freehand (without our guidance system), the achieved accuracy was 1.73 ± 0.82 mm, 6.01∘±4.94∘and3.52∘±2.48∘, while when they were transferred under guidance, we obtained 0.89 ± 0.37 mm, 2.85∘±2.57∘and1.49∘±1.17∘. Conclusions: Our results show that our framework is expected to increase the accuracy in screw positioning and to improve robustness w.r.t. freehand placement.
Keywords: Intra-operative guidance; Intra-operative planning; Orthopedic surgery; Trauma surgery

Designing partially-connected, multilayer perceptron neural nets through information gain - Rodriguez Salas, Dalia; Gómez-Gil, Pilar; Olvera-López, Arturo (The 2013 International Joint Conference on Neural Networks (IJCNN), 2013)
An adequate number of hidden neurons and connection structure of a multi-layer perceptron network (MLP) are usually determined by experimentation. In this paper, we propose a scheme to define an appropriate structure and number of neurons of a partially connected MLP when used for classification. Rules for designing the network are based on a decision tree previously built using information gain. Our structure, called IG Net, is inspired by the Entropy Net [1], but contains fewer layers and connections than such network or than a fully-connected neural network and holds equivalent classification power. We tested the classification performance of our network using 10 databases from the UCI Machine Learning Repository. The performance obtained by IG Net using such databases showed to be statistically equivalent to the one obtained by an Entropy Net or by a fully-connected MLP, using fewer computational resources than the compared models. © 2013 IEEE.
UnivIS-Import:2017-12-18:Pub.2013.tech.IMMD.IMMD5.design_4
Keywords: Multilayer Perceptron; Network Architecture; Decision Tree

Design of a portable wide field of view GPU-accelerated multiphoton imaging system for real-time imaging of breast surgical specimens - Giacomelli, Michael; Yoshitake, Tadayuki; Husvogt, Lennart; Cahill, Lucas; Ahsen, Osman; Vardeh, Hilde; Sheykin, Yury; Faulkner-Jones, Beverly E.; Hornegger, Joachim; Brooker, Jeff; Cable, Alex E.; Connolly, James L.; et al. (Multiphoton Microscopy in the Biomedical Sciences XVI, 2016)
We present a portable multiphoton system designed for evaluating centimeter-scale surgical margins on surgical breast specimens in a clinical setting. The system is designed to produce large field of view images at a high frame rate, while using GPU processing to render low latency, video-rate virtual H&E images for real-time assessment. The imaging system and virtual H&E rendering algorithm are demonstrated by imaging unfixed human breast tissue in a clinical setting.<br
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.design
Keywords: H&E; histology; multiphoton; surgical pathology

Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees - Bier, Bastian; Aschoff, Katharina; Syben-Leisner, Christopher; Unberath, Mathias; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (MACHINE LEARNING FOR MEDICAL IMAGE RECONSTRUCTION, MLMIR 2018, 2018)
CRIS-Team WoS Importer:2019-08-09

Detecting and Measuring Surface Area of Skin Lesions - Mirzaalian Dastjerdi, Houman; Töpfer, Dominique; Bangemann, Michael; Maier, Andreas (Bildverarbeitung für die Medizin 2018, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.detect

Detecting Large Vessel Occlusions using Graph Deep Learning - Kassam, Jad; Thamm, Florian; Rist, Leonhard; Taubmann, Oliver; Maier, Andreas (GeoMedIA 2022 Workshop Submission, 2022)
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Detection and prediction of background parenchymal enhancement on breast MRI using deep learning - Das, Badhan Kumar; Kapsner, Lorenz; Ohlmeyer, Sabine; Laun, Frederik Bernd; Maier, Andreas; Uder, Michael; Wenkel, Evelyn; Bickelhaupt, Sebastian; Liebert, Andrzej (Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, 2023)
The purpose of this work was to automatically classify BPE using T1-weighted subtraction volumes and diffusion-weighted imaging volumes in breast MRI. The dataset consisted of 621 routine breast MRI examination acquired at University Hospital Erlangen. 2D MIP and 3D T1-subtraction volumes were used for the automatic detection of BPE classes. Multi-b-value DWI (up to1500s/mm2) DWI images were used for automatic prediction. ResNet and DenseNet models were used for 2D and 3D data respectively. The study demonstrated an AUROC of 0.8107 on the test set using the T1-subtraction volumes. With DWI volumes, a slightly decreased AuROC of 0.78 was achieved.<br

Detection of femoropopliteal arterial steno-occlusion at MR angiography: initial experience with artificial intelligence - Nguyen, Tri Thien; Folle, Lukas; Bayer, Thomas (European Radiology Experimental, 2024)
Background: This study evaluated a deep learning (DL) algorithm for detecting vessel steno-occlusions in patients with peripheral arterial disease (PAD). It utilised a private dataset, which was acquired and annotated by the authors through their institution and subsequently validated by two blinded readers. Methods: A single-centre retrospective study analysed 105 magnetic resonance angiography (MRA) images using an EfficientNet B0 DL model. Initially, inter-reader variability was assessed using the complete dataset. For a subset of these images (29 from the left side and 35 from the right side) where digital subtraction angiography (DSA) data was available as the ground truth, the model’s accuracy and the area under the curve at receiver operating characteristics analysis (ROC-AUC) were evaluated. Results: A total of 105 patient examinations (mean age, 75 years ±12 [mean ± standard deviation], 61 men) were evaluated. Radiologist-DL model agreement had a quadratic weighted Cohen κ ≥ 0.72 (left side) and ≥ 0.66 (right side). Radiologist inter-reader agreement was ≥ 0.90 (left side) and ≥ 0.87 (right side). The DL model achieved a 0.897 accuracy and a 0.913 ROC-AUC (left side) and 0.743 and 0.830 (right side). Radiologists achieved 0.931 and 0.862 accuracies, with 0.930 and 0.861 ROC-AUCs (left side), and 0.800 and 0.799 accuracies, with 0.771 ROC-AUCs (right side). Conclusion: The DL model provided valid results in identifying arterial steno-occlusion in the superficial femoral and popliteal arteries on MRA among PAD patients. However, it did not reach the inter-reader agreement of two radiologists. Relevance statement: The tested DL model is a promising tool for assisting in the detection of arterial steno-occlusion in patients with PAD, but further optimisation is necessary to provide radiologists with useful support in their daily routine diagnostics. Key points: • This study focused on the application of DL for arterial steno-occlusion detection in lower extremities on MRA. • A previously developed DL model was tested for accuracy and inter-reader agreement. • While the model showed promising results, it does not yet replace human expertise in detecting arterial steno-occlusion on MRA. Graphical Abstract: (Figure presented.)
CRIS-Team Scopus Importer:2024-03-22
Keywords: Artificial intelligence; Deep learning; Digital subtraction angiography; Magnetic resonance angiography; Peripheral arterial disease

Detection of Large Vessel Occlusions using Deep Learning by Deforming Vessel Tree Segmentations - Thamm, Florian; Taubmann, Oliver; Jürgens, Markus; Ditt, Hendrik; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
Computed Tomography Angiography is a key modality providing insights into the cerebrovascular vessel tree that are crucial for the diagnosis and treatment of ischemic strokes, in particular in cases of large vessel occlusions (LVO). Thus, the clinical workflow greatly benefits from an automated detection of patients suffering from LVOs. This work uses convolutional neural networks for case-level classification trained with elastic deformation of the vessel tree segmentation masks to artificially augment training data. Using only masks as the input to our model uniquely allows us to apply such deformations much more aggressively than one could with conventional image volumes while retaining sample realism. The neural network classifies the presence of an LVO and the affected hemisphere. In a 5-fold cross validated ablation study, we demonstrate that the use of the suggested augmentation enables us to train robust models even from few data sets. Training the EfficientNetB1 architecture on 100 data sets, the proposed augmentation scheme was able to raise the ROC AUC to 0.85 from a baseline value of 0.57 using no augmentation. The best performance was achieved using a 3D-DenseNet yielding an AUC of 0.88. The augmentation had positive impact in classification of the affected hemisphere as well, where the 3D-DenseNet reached an AUC of 0.93 on both sides.<br
Keywords: Computed Tomography Angiography; Large Vessel Occlusions; Deep Learning

Detection of Pulmonary Embolisms in NCCT Data Using nnDetection - Vorberg, Linda; Thamm, Florian; Ditt, Hendrik; Horger, Marius; Hagen, Florian; Maier, Andreas (Informatik aktuell, 2023)
Pulmonary embolism (PE) is a life-threatening disease for which a prompt diagnosis is important and challenging as its symptoms are mostly nonspecific. In the clinical workflow of diagnosing PE, computed tomographic pulmonary angiography (CTPA) has become the gold standard imaging tool. As the performance of a CT scan with contrast agent sometimes can be contraindicated and is associated with high costs, identifying the embolism with a non-contrast CT (NCCT) scan is desirable. We automated the detection of PE in NCCT scans with the use of deep learning, in order to guide the physician and speed up the clinical workflow. We used nnDetection, designed for medical object detection, to accomplish this task. nnDetection gets informed by additional channels which are used besides the NCCT scan. These are segmentation masks of the lung lobes and vessels which are also used for post-processing. In a study with 99 patients that all presented with PE, nnDetection was shown to detect a PE in 71% of the cases when considering the first 10 boxes with the highest probability containing a PE.
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Detection of Unseen Low-Contrast Signals Using Classic and Novel Model Observers - Xu, Yang; Schebesch, Frank; Ravikumar, Nishant; Maier, Andreas (Informatik aktuell, 2019)
Automatic task-based image quality assessment has been of importance in various clinical and research applications. In this paper, we propose a neural network model observer, a novel concept which has recently been investigated. It is trained and tested on simulated images with different contrast levels, with the aim of trying to distinguish images based on their quality/contrast. Our model shows promising properties that its output is sensitive to image contrast, and generalizes well to unseen low-contrast signals. We also compare the results of the proposed approach with those of a channelized hotelling observer (CHO), on the same simulated dataset.
CRIS-Team Scopus Importer:2019-05-14

Determination of Forming Limits in Sheet Metal Forming Using Deep Learning - Jaremenko, Christian; Ravikumar, Nishant; Affronti, Emanuela; Merklein, Marion; Maier, Andreas (Materials, 2019)
The forming limit curve (FLC) is used to model the onset of sheet metal instability during forming processes e.g., in the area of finite element analysis, and is usually determined by evaluation of strain distributions, derived with optical measurement systems during Nakajima tests. Current methods comprise of the standardized DIN EN ISO 12004-2 or time-dependent approaches that heuristically limit the evaluation area to a fraction of the available information and show weaknesses in the context of brittle materials without a pronounced necking phase. To address these limitations, supervised and unsupervised pattern recognition methods were introduced recently. However, these approaches are still dependent on prior knowledge, time, and localization information. This study overcomes these limitations by adopting a Siamese convolutional neural network (CNN), as a feature extractor. Suitable features are automatically learned using the extreme cases of the homogeneous and inhomogeneous forming phase in a supervised setup. Using robust Student's t mixture models, the learned features are clustered into three distributions in an unsupervised manner that cover the complete forming process. Due to the location and time independency of the method, the knowledge learned from formed specimen up until fracture can be transferred on to other forming processes that were prematurely stopped and assessed using metallographic examinations, enabling probabilistic cluster membership assignments for each frame of the forming sequence. The generalization of the method to unseen materials is evaluated in multiple experiments, and additionally tested on an aluminum alloy AA5182, which is characterized by Portevin-LE Chatlier effects.
CRIS-Team WoS Importer:2019-05-07

Development and validation of a classification and scoring system for the diagnosis of oral squamous cell carcinomas through confocal laser endomicroscopy - Oetter, Nicolai; Knipfer, Christian; Rohde, Maximilian; von Wilmowsky, Cornelius; Maier, Andreas; Brunner, Kathrin; Adler, Werner; Neukam, Friedrich Wilhelm; Neumann, Helmut; Stelzle, Florian (Journal of Translational Medicine, 2016)
Confocal laser endomicroscopy (CLE) is an optical biopsy method allowing in vivo microscopic imaging at 1000-fold magnification. It was the aim to evaluate CLE in the human oral cavity for the differentiation of physiological/carcinomatous mucosa and to establish and validate, for the first time, a scoring system to facilitate CLE assessment.The study consisted of 4 phases: (1) CLE-imaging (in vivo) was performed after the intravenous injection of fluorescein in patients with histologically confirmed carcinomatous oral mucosa; (2) CLE-experts (n = 3) verified the applicability of CLE in the oral cavity for the differentiation between physiological and cancerous tissue compared to the gold standard of histopathological assessment; (3) based on specific patterns of tissue changes, CLE-experts (n = 3) developed a classification and scoring system (DOC-Score) to simplify the diagnosis of oral squamous cell carcinomas; (4) validation of the newly developed DOC-Score by non-CLE-experts (n = 3); final statistical evaluation of their classification performance (comparison to the results of CLE-experts and the histopathological analyses).Experts acquired and edited 45 sequences (260 s) of physiological and 50 sequences (518 s) of carcinomatous mucosa (total: 95 sequences/778 s). All sequences were evaluated independently by experts and non-experts (based on the newly proposed classification system). Sensitivity (0.953) and specificity (0.889) of the diagnoses by experts as well as sensitivity (0.973) and specificity (0.881) of the non-expert ratings correlated well with the results of the present gold standard of tissue histopathology. Experts had a positive predictive value (PPV) of 0.905 and a negative predictive value (NPV) of 0.945. Non-experts reached a PPV of 0.901 and a NPV of 0.967 with the help of the DOC-Score. Inter-rater reliability (Fleiss` kappa) was 0.73 for experts and 0.814 for non-experts. The intra-rater reliability (Cronbach's alpha) of the experts was 0.989 and 0.884 for non-experts.CLE is a suitable and valid method for experts to diagnose oral cancer. Using the DOC-Score system, an accurate chair-side diagnosis of oral cancer is feasible with comparable results to the gold standard of histopathology-even in daily clinical practice for non-experienced raters.
EVALuna2:2055

Diaphragm Tracking for Respiratory Motion Compensated Cardiac C-Arm CT - Bögel, Marco; Maier, Andreas; Hofmann, Hannes; Hornegger, Joachim; Fahrig, Rebecca (Proceedings of the second international conference on image formation in x-ray computed tomography, 2012)

Diaphragm Tracking in Cardiac C-Arm Projection Data - Bögel, Marco; Maier, Andreas; Hofmann, Hannes; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin 2011, 2012)

Die Nürnberger Briefbücher II. 1408/1409 - Bub, Anna; Krenz, Julian; Mayr, Martin; Neumeier, Katharina (Selbstverlag des Stadtarchivs Nürnberg, 2024)

Die Nürnberger Briefbücher III. 1409-1412 - Bürcky, Simon; Krenz, Julian; Mayr, Martin; Neumeier, Katharina (Selbstverlag des Stadtarchivs Nürnberg, 2024)

Diffeomorphic Image Registration for CINE Cardio MR images using deep learning - Amsel, Daniel; Yoon, Seung Su; Wetzl, Jens; Maier, Andreas (Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, 2022)

Differential diagnosis of RA and PsA using neural networks on 3D bone shape of finger joints - Folle, Lukas; Liu, Chang; Simon, David; Meinderink, Timo; Liphardt, Anna-Maria; Krönke, Gerhard; Schett, Georg; Maier, Andreas; Kleyer, Arnd (Annals of the Rheumatic Diseases 2021, 2021)
Keywords: Deep Learning; Rheumatoid arthritis; psoriatic arthritis

Differential SART for sub-Nyquist Tomographic Reconstruction in Presence of Misalignments - Römer, Florian; Grossmann, Marcus; Schön, Tobias; Gruber, Roland; Jung, Alexander; Oeckl, Steven; Del Galdo, Giovanni (2017)

Differential Tomography: Influence of Sensitivity Direction and Noise-suppressing Windows - Käppler, Sebastian; Maier, Andreas; Riess, Christian (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography (CT-Meeting), 2018)
Keywords: X-ray Phase Contrast; Tomography; Noise; Grating orientation

Digital health in smart cities: Rethinking the remote health monitoring architecture on combining edge, fog, and cloud - Rodrigues, Vinicius Facco; da Rosa Righi, Rodrigo; da Costa, Cristiano André; Zeiser, Felipe Andre; Eskofier, Björn; Maier, Andreas; Kim, Daeyoung (Health and Technology, 2023)
Purpose: Smart cities that support the execution of health services are more and more in evidence today. Here, it is mainstream to use IoT-based vital sign data to serve a multi-tier architecture. The state-of-the-art proposes the combination of edge, fog, and cloud computing to support critical health applications efficiently. However, to the best of our knowledge, initiatives typically present the architectures, not bringing adaptation and execution optimizations to address health demands fully. Methods: This article introduces the VitalSense model, which provides a hierarchical multi-tier remote health monitoring architecture in smart cities by combining edge, fog, and cloud computing. Results: Although using a traditional composition, our contributions appear in handling each infrastructure level. We explore adaptive data compression and homomorphic encryption at the edge, a multi-tier notification mechanism, low latency health traceability with data sharding, a Serverless execution engine to support multiple fog layers, and an offloading mechanism based on service and person computing priorities. Conclusions: This article details the rationale behind these topics, describing VitalSense use cases for disruptive healthcare services and preliminary insights regarding prototype evaluation.
CRIS-Team Scopus Importer:2023-05-05
Keywords: Digital health; Edge computing; Fog computing; Internet of things; Remote health monitoring; Smart city

Digital Twinning of Dynamic Anthropomorphic Population for Assessment of Data Driven Respiratory Gating Methods in SPECT - Reymann, Maximilian; Massanes, Francesc; Kuwert, Torsten; Vija, Hans; Maier, Andreas (IEEE Nuclear Science Symposium and Medical Imaging Conference, 2022)
Data Driven Gating (DDG) can be used to improve quantification in Myocardial Perfusion Imaging (MPI), given a reliable estimate of the respiratory motion. Current algorithms for extracting a respiratory surrogate have shown large variances on patient data, but it remains unclear what causes this reduced accuracy for most patients. We propose a method for creating large sets of digital twins that can be used to assess the performance of DDG algorithms, and give an example application of our method. We simulate a set of XCAT phantoms enabled with respiratory motion recorded from an Anzai belt and perform Monte Carlo Simulation (MCS) using SIMIND for each time frame. By performing high-count simulations of different regions of the phantoms separately, we are able to create different uptake ratios and count levels of the phantoms. Using this data set we assess the effect of a static-high uptake region in the projection data, represented by the gall bladder, and show the influence on the Laplacian Eigenmaps (LE) and Center of Light (COL) DDG method. The presented method enables the assessment of many factors on DDG and we are planning on performing further experiments.<br

Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI - Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas (STACOM 2018: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges, 2019)
Segmentation of the left atrial chamber and assessing its morphology, are essential for improving our understanding of atrial fibrillation, the most common type of cardiac arrhythmia. Automation of this process in 3D gadolinium enhanced-MRI (GE-MRI) data is desirable, as manual delineation is time-consuming, challenging and observer-dependent. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. However, it is difficult to incorporate local and global information without using contracting (pooling) layers, which in turn reduces segmentation accuracy for smaller structures. In this paper, we propose a 3D CNN for volumetric segmentation of the left atrial chamber in LGE-MRI. Our network is based on the well known U-Net architecture. We employ a 3D fully convolutional network, with dilated convolutions in the lowest level of the network, and residual connections between encoder blocks to incorporate local and global knowledge. The results show that including global context through the use of dilated convolutions, helps in domain adaptation, and the overall segmentation accuracy is improved in comparison to a 3D U-Net.
CRIS-Team Scopus Importer:2019-05-02

Dilated deeply supervised networks for hippocampus segmentation in MRI - Folle, Lukas; Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas (Informatik aktuell, 2019)
Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimers Disease (AD). The shape and structure of the hippocampus are important factors in terms of early AD diagnosis and prognosis by clinicians. However, manual segmentation of such subcortical structures in MR studies is a challenging and subjective task. In this paper, we investigate variants of the well known 3D U-Net, a type of convolution neural network (CNN) for semantic segmentation tasks.We propose an alternative form of the 3D U-Net, which uses dilated convolutions and deep supervision to incorporate multi-scale information into the model. The proposed method is evaluated on the task of hippocampus head and body segmentation in an MRI dataset, provided as part of the MICCAI 2018 segmentation decathlon challenge. The experimental results show that our approach outperforms other conventional methods in terms of different segmentation accuracy metrics.
CRIS-Team Scopus Importer:2019-05-14

Direct and indirect image rotation estimation methods of orthopedic x-ray images - Kunze, Holger; Kordon, Florian Johannes; Maier, Andreas; Breininger, Katharina (Medical Imaging 2022: Image Processing, 2022)
For many medical questions, X-ray imaging belongs to the gold standard for diagnosis, treatment planning, treatment guidance, and surgery assessment. To improve the reading performance, standardized image rotation is an important step. We propose a new algorithm to estimate the correct image rotation. For many body regions, one line can be defined that is aligned with the upright orientation of the X-ray image. This line can be, for example, the shaft axis of a long bone or the axis of the spine. In this paper, we propose a strategy to automatically align X-ray images with their standard orientation. In a first step, the heatmap of this line is determined using the segmentation network D-LinkNet. The rotation direction, up to a top-down flip, is obtained by computing the orientation of the main axis of this heatmap. For the orientation computation, we compare PCA and Hu moments. While the PCA requires to threshold the heatmap, Hu moments can be used directly on the output values of the network, preserving the (un)certainty of the segmentation. We compare these two methods with a ResNet-18 for the direct estimation of the image rotation on 220 X-ray images from the MURA dataset showing the wrist in the AP view. With the heatmap-based approach followed by Hu moments analysis, the median absolute error for the angle estimation can be reduced to 0.7° compared to 1.7° by a direct estimation method. PCA suffers from noisy heatmaps for images of bad quality degrading the overall performance of this approach.
Keywords: Medial Image Analysis; Rotation Estimation; X-ray Images; Orthopedics

Discrete Estimation of Data Completeness for 3D Scan Trajectories with Detector Offset - Maier, Andreas; Kugler, Patrick; Lauritsch, Günter; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.discre_2
Keywords: Data Completeness; Computed Tomography; Tuy's Condition

Disentangled Latent Speech Representation for Automatic Pathological Intelligibility Assessment - Weise, Tobias; Maier, Andreas; Nöth, Elmar; Heismann, Bjoern; Schuster, Maria; Yang, Seung Hee (Proceedings of INTERSPEECH 2022, 2022)
Speech intelligibility assessment plays an important role in the therapy of patients suffering from pathological speech disorders. Automatic and objective measures are desirable to assist therapists in their traditionally subjective and labor-intensive assessments. In this work, we investigate a novel approach for obtaining such a measure using the divergence in disentangled latent speech representations of a parallel utterance pair, obtained from a healthy reference and a pathological speaker. Experiments on an English database of Cerebral Palsy patients, using all available utterances per speaker, show high and significant correlation values (R = −0.9) with subjective intelligibility measures, while having only minimal deviation (±0.01) across four different reference speaker pairs. We also demonstrate the robustness of the proposed method (R = −0.89 deviating ±0.02 over 1000 iterations) by considering a significantly smaller amount of utterances per speaker. Our results are among the first to show that disentangled speech representations can be used for automatic pathological speech intelligibility assessment, resulting in a reference speaker pair invariant method, applicable in scenarios with only few utterances available.<br
Keywords: auto-encoder; speech disentanglement; DTW; pathological speech intelligibility

Dissecting Glaciers - Can an Automated Bio-Medical Image Segmentation Tool also Segment Glaciers? - Gourmelon, Nora; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (EGU General Assembly 2022, 2022)

Distance error correction for time-of-flight cameras - Fürsattel, Peter; Schaller, Christian; Maier, Andreas; Riess, Christian (Automated Visual Inspection and Machine Vision II, 2017)
Keywords: Time-of-Flight; Systematic Errors; Random Forests

Distinguishing Progression and Radionecrosis in large progressive Brain Metastases after Stereotaxy by integrating Radiomics, Dose Radiomics and clinical Risk Factors - Sommer, Phillip; Huang, Yixing; Srinivasan, Aishwarya Lakshmi; Fischer, Stefan; Dörfler, Arnd; Schmidt, Manuel; Frey, Benjamin; Maier, Andreas; Fietkau, Rainer; Bert, Christoph; Putz, Florian (STRAHLENTHERAPIE UND ONKOLOGIE, 2022)
CRIS-Team WoS Importer:2022-06-03

DL-based inpainting for metal artifact reduction for cone beam CT using metal path length information - Gottschalk, Tristan; Maier, Andreas; Kordon, Florian Johannes; Kreher, Björn W. (Medical Physics, 2022)
Background: Metallic implants, which are inserted into the patient's body during trauma interventions, are the main cause of heavy artifacts in 3D X-ray acquisitions. These artifacts then hinder the evaluation of the correct implant's positioning, thus leading to a disturbed patient's healing process and increased revision rates. Purpose: This problem is tackled by so-called metal artifact reduction (MAR) methods. This paper examines possible advances in the inpainting process of such MAR methods to decrease disruptive artifacts while simultaneously preserving important anatomical structures adjacent to the inserted implants. Methods: In this paper, a learning-based inpainting method for cone-beam computed tomography is proposed that couples a convolutional neural network (CNN) with an estimated metal path length as prior knowledge. Further, the proposed method is solely trained and evaluated on real measured data. Results: The proposed inpainting approach shows advantages over the inpainting method used by the currently clinically approved frequency split metal artifact reduction (fsMAR) method as well as the learning-based state-of-the-art (SOTA) method PConv-Net. The major improvement of the proposed inpainting method lies in the ability to correctly preserve important anatomical structures in those regions adjacent to the metal implants. Especially these regions are highly important for a correct implant's positioning in an intraoperative setup. Using the proposed inpainting, the corresponding MAR volumes reach a mean structural similarity index measure (SSIM) score of 0.9974 and outperform the other methods by up to 6 dB on single slices regarding the peak signal-to-noise ratio (PSNR) score. Furthermore, it can be shown that the proposed method can generalize to clinical cases at hand. Conclusions: In this paper, a learning-based inpainting network is proposed that leverages prior knowledge about the metal path length of the inserted implant. Evaluations on real measured data reveal an increased overall MAR performance, especially regarding the preservation of anatomical structures adjacent to the inserted implants. Further evaluations suggest the ability of the proposed approach to generalize to clinical cases.
Created from Fastlane, Scopus look-up
Keywords: deep learning; metal artifact reduction; trauma interventions

DNN-based Speed-of-Sound Reconstruction for Automated Breast Ultrasound - Khun Jush, Farnaz; Biele, Markus; Dueppenbecker, Peter Michael; Schmidt, Oliver; Maier, Andreas (2020 IEEE International Ultrasonics Symposium (IUS), 2020)
The gold-standard for breast cancer screening is xray mammography. Alongside, ultrasound scans are being used as additional source of information for patients with dense breast tissue. However, conventional ultrasound imaging is a qualitative approach and is prone to errors. Quantitative approaches can provide valuable information about tissue properties, e.g. the speed-of-sound in the tissue can be used as a biomarker for breast tissue malignancy. Recent studies showed the possibility of speed-of-sound reconstruction from ultrasound raw data using Deep Neural Networks (DNNs). In this study, we investigate the feasibility of DNN-based speed-of-sound reconstruction for automated breast ultrasound with simulated and real data. We set up a DNN for speed-of-sound reconstruction. The network is fully trained on simulated data. Simulations are based on the LightABVS transducer, a linear transducer with 192 active channels. The input of the network is raw channel data from a single plane-wave acquisition. The output of the network is a speed-of-sound map with a resolution of 0.1 mm. We achieved Mean Absolute Percentage Error of 0.39 ± 0.03% and Root-Mean-Square Error of 14.85 ± 0.52 m/s on simulated dataset and promising results on real dataset which demonstrates great potential of this method for integration in conventional ultrasound systems.<br
Keywords: Speed-of-Sound; Deep Neural Networks; Breast Ultrasound

Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge - Wilm, Frauke; Marzahl, Christian; Breininger, Katharina; Aubreville, Marc (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
Assessing the mitotic count has a known high degree of intra- and inter-rater variability. Computer-aided systems have proven to decrease this variability and reduce labeling time. These systems, however, are generally highly dependent on their training domain and show poor applicability to unseen domains. In histopathology, these domain shifts can result from various sources, including different slide scanning systems used to digitize histologic samples. The MItosis DOmain Generalization challenge focused on this specific domain shift for the task of mitotic figure detection. This work presents a mitotic figure detection algorithm developed as a baseline for the challenge, based on domain adversarial training. On the challenge’s test set, the algorithm scored an F 1 score of 0.7183. The corresponding network weights and code for implementing the network are made publicly available.
CRIS-Team Scopus Importer:2022-03-25
Keywords: Domain Shift; Histopathology; MIDOG; Mitotic Count; Object Detection

Dose Reduction Achieved by Dynamically Collimating the Redundant Rays in Fan-beam and Cone-beam CT - Xia, Yan; Berger, Martin; Riess, Christian; Hornegger, Joachim; Maier, Andreas (Proceedings of the IEEE NSS/MIC 2013, 2013)
In X-ray computed tomography (CT), short-scan acquisition that acquires data only over a range of π plus the fan angle, rather than a full range of 2π, is commonly used in circular fan-beam and cone-beam geometry. Such scan aims to reduce acquisition time and radiation dose during data acquisition. However, during a partial circle scan some data are measured once, while other measurements are observed twice. Traditionally, the redundant data are weighted by a smooth function (e.g. the Parker weights) before filtering. In this paper, we present an algorithmic setup that employs dynamic collimation to shield the redundant rays and propose two algorithms to correct the resulting truncation. This approach is able to potentially decrease the dose of 10% for a C-arm CT with fan angle of 10° and of 23% for a diagnostic CT with fan angle of 50°. Evaluation shows that the reconstruction results are of comparable accuracy to the one from standard short-scan FDK, with less dose to the patient. © 2013 IEE
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.dosere_7
Keywords: GRK-1773

Dose Reduction for Historical Books Digitization by 3-D X-Ray CT - Stromer, Daniel; Christlein, Vincent; Huang, Yixing; Zippert, Patrick; Helmecke, Eric; Hausotte, Tino; Maier, Andreas (Proceedings of 8th Conference on Industrial Computed Tomography (iCT 2018), 2018)
UnivIS-Import:2018-02-22:Pub.2018.tech.IMMD.IMMD5.dosere_1

Dose reduction in sequence scanning 4D CT imaging through respiratory signal-guided tube current modulation: A feasibility study - Schwarz, Annette; Werner, René; Wimmert, Lukas; Vornehm, Marc; Gauer, Tobias; Hofmann, Christian (Medical Physics, 2023)
Background: Respiratory signal-guided 4D CT sequence scanning such as the recently introduced Intelligent 4D CT (i4DCT) approach reduces image artifacts compared to conventional 4D CT, especially for irregular breathing. i4DCT selects beam-on periods during scanning such that data sufficiency conditions are fulfilled for each couch position. However, covering entire breathing cycles during beam-on periods leads to redundant projection data and unnecessary dose to the patient during long exhalation phases. Purpose: We propose and evaluate the feasibility of respiratory signal-guided dose modulation (i.e., temporary reduction of the CT tube current) to reduce the i4DCT imaging dose while maintaining high projection data coverage for image reconstruction. Methods: The study is designed as an in-silico feasibility study. Dose down- and up-regulation criteria were defined based on the patients’ breathing signals and their representative breathing cycle learned before and during scanning. The evaluation (including an analysis of the impact of the dose modulation criteria parameters) was based on 510 clinical 4D CT breathing curves. Dose reduction was determined as the fraction of the downregulated dose delivery time to the overall beam-on time. Furthermore, under the assumption of a 10-phase 4D CT and amplitude-based reconstruction, beam-on periods were considered negatively affected by dose modulation if the downregulation period covered an entire phase-specific amplitude range for a specific breathing phase (i.e., no appropriate reconstruction of the phase image possible for this specific beam-on period). Corresponding phase-specific amplitude bins are subsequently denoted as compromised bins. Results: Dose modulation resulted in a median dose reduction of 10.4% (lower quartile: 7.4%, upper quartile: 13.8%, maximum: 28.6%; all values corresponding to a default parameterization of the dose modulation criteria). Compromised bins were observed in 1.0% of the beam-on periods (72 / 7370 periods) and affected 10.6% of the curves (54/510 curves). The extent of possible dose modulation depends strongly on the individual breathing patterns and is weakly correlated with the median breathing cycle length (Spearman correlation coefficient 0.22, p < 0.001). Moreover, the fraction of beam-on periods with compromised bins is weakly anti-correlated with the patient's median breathing cycle length (Spearman correlation coefficient -0.24; p < 0.001). Among the curves with the 17% longest average breathing cycles, no negatively affected beam-on periods were observed. Conclusion: Respiratory signal-guided dose modulation for i4DCT imaging is feasible and promises to significantly reduce the imaging dose with little impact on projection data coverage. However, the impact on image quality remains to be investigated in a follow-up study.
Created from Fastlane, Scopus look-up
Keywords: 4D CT; dose reduction

Double Your Views: Exploiting Symmetry in Transmission Imaging - Preuhs, Alexander; Maier, Andreas; Manhart, Michael; Fotouhi, Javad; Navab, Nassir; Unberath, Mathias (Medical Image Computing and Computer Assisted Intervention, 2018)
CRIS-Team Scopus Importer:2019-05-14

Dual-Energy C-arm CT in the Angiographic Suite - Datta, Sanjit; Choi, Jang-Hwan; Niebler, Christine; Maier, Andreas; Fahrig, Rebecca; Müller, Kerstin (2015 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.dualen

Dual-Mode Training with Style Control and Quality Enhancement for Road Image Domain Adaptation - Venator, Moritz; Shen, Fengyi; Aklanoglu, Selcuk; Bruns, Erich; Diepold, Klaus; Maier, Andreas (2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020)
Dealing properly with different viewing conditions remains a key challenge for computer vision in autonomous driving. Domain adaptation has opened new possibilities for data augmentation, translating arbitrary road scene images into different environmental conditions. Although multimodal concepts have demonstrated the capability to separate content and style, we find that existing methods fail to reproduce scenes in the exact appearance given by a reference image. In this paper, we address the aforementioned problem by introducing a style alignment loss between output and reference image. We integrate this concept into a multimodal unsupervised image-to-image translation model with a novel dual-mode training process and additional adversarial losses. Focusing on road scene images, we evaluate our model in various aspects including visual quality and feature matching. Our experiments reveal that we are able to significantly improve both style alignment and image quality in different viewing conditions. Adapting concepts from neural style transfer, our new training approach allows to control the output of multimodal domain adaptation, making it possible to generate arbitrary scenes and viewing conditions for data augmentation.

Dynamic 2-D/3-D Rigid Registration Framework Using Point-To-Plane Correspondence Model - Wang, Jian; Schaffert, Roman; Borsdorf, Anja; Heigl, Benno; Huang, Xiaolin; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Medical Imaging, 2017)
In image-guided interventional procedures, live 2-D X-ray images can be augmented with preoperative 3-D computed tomography or MRI images to provide planning landmarks and enhanced spatial perception. An accurate alignment between the 3-D and 2-D images is a prerequisite for fusion applications. This paper presents a dynamic rigid 2-D/3-D registration framework, which measures the local 3-D-to-2-D misalignment and efficiently constrains the update of both planar and non-planar 3-D rigid transformations using a novel point-to-plane correspondence model. In the simulation evaluation, the proposed method achieved a mean 3-D accuracy of 0.07 mm for the head phantom and 0.05 mm for the thorax phantom using single-view X-ray images. In the evaluation on dynamic motion compensation, our method significantly increases the accuracy comparing with the baseline method. The proposed method is also evaluated on a publicly-available clinical angiogram data set with 'gold-standard' registrations. The proposed method achieved a mean 3-D accuracy below 0.8 mm and a mean 2-D accuracy below 0.3 mm using single-view X-ray images. It outperformed the state-of-the-art methods in both accuracy and robustness in single-view registration. The proposed method is intuitive, generic, and suitable for both initial and dynamic registration scenarios.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.dynami
Keywords: dynamic registration; point-to-plane correspondence model; Rigid 2-D/3-D registration

Dynamic detector offsets for field of view extension in C-arm computed tomography with application to weight-bearing imaging - Herbst, Magdalena; Schebesch, Frank; Berger, Martin; Choi, Jang-Hwan; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Medical Physics, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.dynami
Keywords: GRK-1773

Dynamic Iterative Reconstruction for Interventional 4-D C-Arm CT Perfusion Imaging - Manhart, Michael; Kowarschik, Markus; Fieselmann, Andreas; Deuerling-Zheng, Yu; Royalty, Kevin; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2013)

Dynamic Pixel Binning allows Spatial and Angular Resolution Tradeoffs to improve Image Quality in X-Ray C-Arm CT - Steg, Alexander; Reichenbach, Marc; Söll, Christopher; Shi, Lan; Maier, Andreas; Riess, Christian (IEEE International Symposium on Biomedical Imaging (ISBI), 2016)
When designing an acquisition protocol for a C-arm CT acquisition, a tradeoff has to be made between scan time and the amount of acquired data. If the scan is increased, data of spatially and angular higher resolution can be acquired. If the scan time is fixed, one has to tradeoff spatial and angular resolution, which may either lead to oversmoothing or artifacts from angular undersampling. In this work, we investigate the viability of smart cameras for pushing the boundary for this bottleneck considerably further. The detector pixels are enhanced by processing units that are able to perform a limited amount of local operations. This allows an online, dynamical adaptation of the pixel binning, depending on whether a local neighborhood is smooth or not. We demonstrate on phantom data that this approach allows to save about 75% of the data, obtaining a reconstruction quality comparable to 1 x 1 binning at a data rate comparable to 2 x 2 pixel binning.
elib2cris::1512001600,steg2016
Keywords: Pixel Binning; X-ray detector; C-arm reconstruction; Dynamic Pixel Binning; X-ray C-arm CT

Dynamic Reconstruction with Statistical Ray Weighting for C-Arm CT Perfusion Imaging - Manhart, Michael; Fieselmann, Andreas; Deuerling-Zheng, Yu; Maier, Andreas; Kowarschik, Markus (Proc. 12th Fully 3D, 2013)

Echo time dependence of biexponential and triexponential intravoxel incoherent motion parameters in the liver - Führes, Tobit; Riexinger, Andreas; Loh, Martin; Martin, Jan; Wetscherek, Andreas; Kuder, Tristan Anselm; Uder, Michael; Hensel, Bernhard; Laun, Frederik Bernd (Magnetic Resonance in Medicine, 2021)
Purpose Intravoxel incoherent motion (IVIM) studies are performed with different acquisition protocols. Comparing them requires knowledge of echo time (TE) dependencies. The TE-dependence of the biexponential perfusion fraction f is well-documented, unlike that of its triexponential counterparts f(1) and f(2) and the biexponential and triexponential pseudodiffusion coefficients D*, D1*, and D2*. The purpose was to investigate the TE-dependence of these parameters and to check whether the triexponential pseudodiffusion compartments are associated with arterial and venous blood. Methods Fifteen healthy volunteers (19-58 y; mean: 24.7 y) underwent diffusion-weighted imaging of the abdomen with 24 b-values (0.2-800 s/mm(2)) at TEs of 45, 60, 75, and 90 ms. Regions of interest (ROIs) were manually drawn in the liver. One set of bi- and triexponential IVIM parameters per volunteer and TE was determined. The TE-dependence was assessed with the Kruskal-Wallis test. Results TE-dependence was observed for f (P < .001), f(1) (P = .001), and f(2) (P < .001). Their median values at the four measured TEs were: f: 0.198/0.240/0.274/0.359, f(1): 0.113/0.139/0.146/0.205, f(2): 0.115/0.155/0.182/0.194. D, D*, D1*, and D2* showed no significant TE-dependence. Their values were: diffusion coefficient D (10(-4) mm(2)/s): 9.45/9.63/9.75/9.41, biexponential D* (10(-2) mm(2)/s): 5.26/5.52/6.13/5.82, triexponential D1* (10(-2) mm(2)/s): 1.73/2.91/2.25/2.51, triexponential D2* (mm(2)/s): 0.478/1.385/0.616/0.846. Conclusion f(1) and f(2) show similar TE-dependence as f, ie, increase with rising TE; an effect that must be accounted for when comparing different studies. The diffusion and pseudodiffusion coefficients might be compared without TE correction. Because of the similar TE-dependence of f(1) and f(2), the triexponential pseudodiffusion compartments are most probably not associated to venous and arterial blood.
CRIS-Team WoS Importer:2021-09-10

Edge-Preserving Bilateral Filtering for Images Containing Dense Objects in CT - Yang, Qiao; Maier, Andreas; Maass, Nicole; Hornegger, Joachim (2013 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2013)
In computed tomography (CT), dense objects such as bone, metal implants, and contrast medium, induce cupping and streaking artifacts. To reduce those artifacts, various approaches have a common strategy, which is segmenting datasets into different materials and correcting them separately according to their own physical characteristics. However, in most cases, the severe artifacts hinder the primary segmentation which results in low efficiency of artifact reduction approaches. When taking noise under consideration, the accuracy of segmentation gets even worse. In this work, we applied an edge preserving step based on bilateral filtering between reconstruction and segmentation. A traditional bilateral filter performs noise reduction, and a bilateral edge detector exploits the structural edge information. By incorporating the edge information with noise reduced reconstruction images, a more sophisticated segmentation approach is proposed. Quantitative evaluations of noise reduction and segmentation performance are carried out using simulated and real CT datasets. The results show that our approach can reduce streak artifacts at a primary level, which significantly improves segmentation. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.edgepr_0
Keywords: Beam Hardening Correction; Computed Tomography; Image Segmentation; Noise Reduction

Editorial: Auditory perception and phantom perception in brains, minds and machines - Schilling, Achim; Schaette, Roland; Sedley, William; Gerum, Richard Carl; Maier, Andreas; Krauß, Patrick (Frontiers in Neuroscience, 2023)
CRIS-Team Scopus Importer:2023-11-03
Keywords: artificial intelligence; auditory neuroscience; auditory phantom perception; cognitive computational neuroscience; deep neural networks; hearing loss; neuroimaging; tinnitus

Editorial for the Special Issue on MICCAI 2015 - Navab, Nassir; Frangi, Alejandro F.; Wells, William; Maier, Andreas (Medical Image Analysis, 2016)
CRIS-Team Scopus Importer:2022-08-05

Effectiveness of Smart Meters and AI for Energy Transition – a Use Case for District Heating Networks - Schüller, Katharina; Bayer, Siming; Sieber, Sarah (2022)
In this project, we develop and validate a novel data-driven prototype that optimizes district heating networks regardless of the respective network topology, consequently increases the operational efficiency of the network, and therefore contributes positively to climate protection. Our framework utilizes smart meter data and is developed based on artificial intelligence (AI) on a basic level. In comparison to other approaches, our framework can be interpreted and explained straightforward. An intuitive and user-friendly web application is implemented, so that any utility company case easily integrate it into their daily operation
Keywords: Climate Change; District Heating; AI

Effective One Step-iterative Fiducial Marker-based Compensation for Involuntary Motion in Weight-bearing C-arm Cone-beam CT Scanning of Knees - Choi, Jang-Hwan; Maier, Andreas; Berger, Martin; Fahrig, Rebecca (Proc. SPIE Medical Imaging 2014, 2014)
We previously introduced three different fiducial marker-based correction methods (2D projection shifting, 2D projection warping, and 3D image warping) for patientsâ€™involuntary motion in the lower body during weight-bearing Carm CT scanning. The 3D warping method performed better than 2D methods since it could more accurately take into account the lower body motion in 3D. However, as the 3D warping method applies different rotational and translational movement to the reconstructed image for each projection frame, distance-related weightings were slightly twisted and thus result in overlaying background noise over the entire image. In order to suppress background noise and artifacts (e.g. metallic marker-caused streaks), the 3D warping method has been improved by incorporating bilateral filtering and a Landwebertype iteration in one step. A series of projection images of five healthy volunteers standing at various flexion angles were acquired using a C-arm cone-beam CT system with a flat panel. A horizontal scanning trajectory of the C-arm was calibrated to generate projection matrices. Using the projection matrices, the static reference marker coordinates in 3D were estimated and used for the improved 3D warping method. The improved 3D warping method effectively reduced background noise down below the noise level of 2D methods and also eliminated metal-generated streaks. Thus, improved visibility of soft tissue structures (e.g. fat and muscle) was achieved while maintaining sharp edges at bone-tissue interfaces. Any high resolution weight-bearing cone-beam CT system can apply this method for motion compensation. © 2014 SPIE.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.effect
Keywords: GRK-1773

Effect of Data-Driven Respiratory Gating on Lu-177 SPECT/CT Scans - Sanders, James Chester; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas; Ritt, Philipp (29th Annual EANM Congress of the European Association of Nuclear Medicine 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.effect_2

Effect of High-Uptake Regions with Inconsistent Motion on Data Driven Respiratory Gating in SPECT Myocardial Perfusion Imaging using Digital Phantoms - Reymann, M.; Massanes, F.; Kuwert, Torsten; Vija, A. H.; Maier, Andreas (EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2022)
CRIS-Team WoS Importer:2022-10-28

Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning - Bhandary Panambur, Adarsh; Madhu, Prathmesh; Maier, Andreas (German Workshop on Medical Image Computing, 2022, 2022)

Effects of functional endoscopic sinus surgery on the acoustics of the sinonasal tract - Havel, Miriam; Becker, Sven; Schuster, Maria; Johnson, Thorsten; Maier, Andreas; Sundberg, Johan (Rhinology, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.eectso
Keywords: 3D replica; Cadaveric study; Functional endoscopic sinus surgery; Paranasal sinuses; Resonance frequency; Sinonasal tract

Effects of Tissue Material Properties on X-Ray Image, Scatter and Patient Dose A Monte Carlo Simulation - Roser, Philipp; Birkhold, Annette; Zhong, Xia; Stepina, Elizaveta; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik aktuell, 2019)
With increasing patient and staff X-ray radiation awareness, many efforts have been made to develop accurate patient dose estimation methods. To date, Monte Carlo (MC) simulations are considered golden standard to simulate the interaction of X-ray radiation with matter. However, sensitivity of MC simulation results to variations in the experimental or clinical setup of image guided interventional procedures are only limited studied. In particular, the impact of patient material compositions is poorly investigated. This is mainly due to the fact, that these methods are commonly validated in phantom studies utilizing a single anthropomorphic phantom. In this study, we therefore investigate the impact of patient material parameters mapping on the outcome of MC X-ray dose simulations. A computation phantom geometry is constructed and three different commonly used material composition mappings are applied. We used the MC toolkit Geant4 to simulate X-ray radiation in an interventional setup and compared the differences in dose deposition, scatter distributions and resulting X-ray images. The evaluation shows a discrepancy between different material composition mapping up to 20% concerning directly irradiated organs. These results highlight the need for standardization of material composition mapping for MC simulations in a clinical setup.
CRIS-Team Scopus Importer:2019-05-14

Effects of Vocal Aging on Fundamental Frequency and Formants - Mwangi, Samuel; Spiegl, Werner; Hönig, Florian Thomas; Haderlein, Tino; Maier, Andreas; Nöth, Elmar (Proceedings of the International Conference on Acoustics NAG/DAGA 2009, 2009)

Efficient 2D Filtering for Cone-beam VOI Reconstruction - Xia, Yan; Maier, Andreas; Dennerlein, Frank; Hofmann, Hannes; Hornegger, Joachim (2012 Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference, 2012)

Efficient and high accuracy 3-D OCT angiography motion correction in pathology - Ploner, Stefan; Kraus, Martin; Moult, Eric M.; Husvogt, Lennart; Schottenhamml, Julia; Yasin Alibhai, A.; Waheed, Nadia K.; Duker, Jay S.; Fujimoto, James G.; Maier, Andreas (Biomedical Optics Express, 2021)
We describe a novel method for non-rigid 3-D motion correction of orthogonally raster-scanned optical coherence tomography angiography volumes. This is the first approach that aligns predominantly axial structural features such as retinal layers as well as transverse angiographic vascular features in a joint optimization. Combined with orthogonal scanning and favorization of kinematically more plausible displacements, subpixel alignment and micrometer-scale distortion correction is achieved in all 3 dimensions. As no specific structures are segmented, the method is by design robust to pathologic changes. Furthermore, the method is designed for highly parallel implementation and short runtime, allowing its integration into clinical workflow even for high density or wide-field scans. We evaluated the algorithm with metrics related to clinically relevant features in an extensive quantitative evaluation based on 204 volumetric scans of 17 subjects, including patients with diverse pathologies and healthy controls. Using this method, we achieve state-of-the-art axial motion correction and show significant advances in both transverse co-alignment and distortion correction, especially in the subgroup with pathology.
CRIS-Team Scopus Importer:2021-01-08

Efficient Annotation of Medieval Charters - Nicolaou, Anguelos; Luger, Daniel; Decker, Franziska; Renet, Nicolas; Christlein, Vincent; Vogeler, Georg (Document Analysis and Recognition – ICDAR 2023 Workshops., 2023)
Diplomatics, the analysis of medieval charters, is a major field of research in which paleography is applied. Annotating data, if performed by laymen, needs validation and correction by experts. In this paper, we propose an effective and efficient annotation approach for charter segmentation, essentially reducing it to object detection. This approach allows for a much more efficient use of the paleographer’s time and produces results that can compete and even outperform pixel-level segmentation in some use cases. Further experiments shed light on how to design a class ontology in order to make the best use of annotators’ time and effort. Exploiting the presence of calibration cards in the image, we further annotate the data with the physical length in pixels and train regression neural networks to predict it from image patches.<br
CRIS-Team Scopus Importer:2023-10-13
Keywords: Diplomatics; Paleography; Object detection; Resolution

Efficient Epipolar Consistency - Aichert, André; Breininger, Katharina; Köhler, Thomas; Maier, Andreas (Proceedings of the 4th CT Meeting, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.effici_3

Efficient Exploitation of Image Repetitions in MR Reconstruction - Gadjimuradov, Fasil; Benkert, Thomas; Nickel, Marcel Dominik; Maier, Andreas (Medical Imaging with Deep Learning, 2022)

Efficient Labeling of Optical Coherence Tomography Angiography Data using Eye Tracking - Husvogt, Lennart; Moult, Eric M.; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Bildverarbeitung für die Medizin 2018, 2018)
We implemented an approach for the efficient labeling of structural and angiographic optical coherence tomography (OCT) data using eye tracking. OCT is a non-invasive imaging technology, which provides volumetric data from scattering tissues in micrometer resolution. Due to its widespread use and noninvasiveness, clinicians acquire large amounts of volumetric ophthalmic data on a daily basis.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.effici_1
Keywords: labeling; eye tracking

Einflussfaktoren auf Sprechstoerungen bei T1 und T2 Plattenepithelcarcinomen der Mundhoehle - Schuster, Maria; Maier, Andreas; Kohler, Regina; Nöth, Elmar; Rosanowski, Frank; Eysholdt, Ulrich; Nkenke, Emeka (Aktuelle phoniatrische Aspekte, 2007)

Ein klinisch anwendbares Neuronales Netzwerk zur Klassifikation suspekter Läsionen in der Mamma-MRT - Ellmann, Stephan; Bielowski, Christian; Wenkel, Evelyn; Vesal, Sulaiman; Maier, Andreas; Uder, Michael; Bäuerle, Tobias (Fortschritte auf dem Gebiete der Röntgenstrahlen, 2018)
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.einkli

Electrophysiology Catheter Detection and Reconstruction from Two Views in Fluoroscopic Images - Hoffmann, Matthias; Brost, Alexander; Koch, Martin; Bourier, Felix; Maier, Andreas; Kurzidim, Klaus; Strobel, Norbert; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2015)
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.IMMD5.electr

EMOTIC: Emotions in Context Dataset - Kosti, Ronak Vijaypal; et al. (30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017, 2017)

Emotion recognition in context - Kosti, Ronak Vijaypal; et al. (30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017)

Empirical Scatter Correction using the Epipolar Consistency Condition - Hoffmann, Mathis; Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Aichert, André; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2018)
Scatter affects every computed tomography (CT) image. Calibration-free software scatter reduction methods have not been used extensively in practice. Recently, consistency conditions have been applied successfully to other artifact reduction problems in CT imaging. We propose a scatter reduction method, that uses an epipolar consistency condition (ECC) to estimate parameters of an additive scatter model. We evaluate our approach by comparing it with an image-based empirical scatter correction method (ESC) that uses the same scatter model. We show that it performs equally well onsimulated data. Further, ECC outperforms ESC regarding the computational load for the determination of the parameters, because ECC is formulated in projection domain such that no image reconstruction is necessary. While some restrictions might apply for the stability of ECC on measured data, no prior information needs to be formulated regarding the reconstructed image, like it is required with ES
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.empiri

Employing Graph Representations for Cell-Level Characterization of Melanoma MELC Samples - Rivera Monroy, Luis; Rist, Leonhard; Eberhardt, Martin; Ostalecki, Christian; Baur, Andreas; Vera, Julio; Breininger, Katharina; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023)
Histopathology imaging is crucial for the diagnosis and treatment of skin diseases. For this reason, computer-assisted approaches have gained popularity and shown promising results in tasks such as segmentation and classification of skin disorders. However, collecting essential data and sufficiently high-quality annotations is a challenge. This work describes a pipeline that uses suspected melanoma samples that have been characterized using Multi-Epitope-Ligand Cartography (MELC). This cellular level characterization of the tissue is then represented as a graph and finally used to train a graph neural network. This imaging technology, combined with the methodology proposed in this work, achieves a classification accuracy of 87 %, outperforming existing approaches by 10 %.<br

Enabling Geometry Aware Learning Through Differentiable Epipolar View Translation - Rohleder, Maximilian; Pradel, Charlotte; Wagner, Fabian; Thies, Mareike; Maul, Noah; Denzinger, Felix; Maier, Andreas; Kreher, Bjoern (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Epipolar geometry is exploited in several applications in the field of Cone-Beam Computed Tomography (CBCT) imaging. By leveraging consistency conditions between multiple views of the same scene, motion artifacts can be minimized, the effects of beam hardening can be reduced, and segmentation masks can be refined. In this work, we explore the idea of enabling deep learning models to access the known geometrical relations between views. This implicit 3D information can potentially enhance various projection domain algorithms such as segmentation, detection, or inpainting. We introduce a differentiable feature translation operator, which uses available projection matrices to calculate and integrate over the epipolar line in a second view. As an example application, we evaluate the effects of the operator on the task of projection domain metal segmentation. By re-sampling a stack of projections into orthogonal view pairs, we segment each projection image jointly with a second view acquired roughly 90 ^° apart. The comparison with an equivalent single-view segmentation model reveals an improved segmentation performance of 0.95 over 0.91 measured by the dice coefficient. By providing an implementation of this operator as an open-access differentiable layer, we seek to enable future research.
CRIS-Team Scopus Importer:2023-11-03
Keywords: Cone-Beam Computed Tomography; Epipolar Geometry; Operator Learning

Enabling machine learning in X-ray-based procedures via realistic simulation of image formation - Unberath, Mathias; Zaech, Jan-Nico; Gao, Cong; Bier, Bastian; Goldmann, Florian; Lee, Sing Chun; Fotouhi, Javad; Taylor, Russell; Armand, Mehran; Navab, Nassir (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose: Machine learning-based approaches now outperform competing methods in most disciplines relevant to diagnostic radiology. Image-guided procedures, however, have not yet benefited substantially from the advent of deep learning, in particular because images for procedural guidance are not archived and thus unavailable for learning, and even if they were available, annotations would be a severe challenge due to the vast amounts of data. In silico simulation of X-ray images from 3D CT is an interesting alternative to using true clinical radiographs since labeling is comparably easy and potentially readily available. Methods: We extend our framework for fast and realistic simulation of fluoroscopy from high-resolution CT, called DeepDRR, with tool modeling capabilities. The framework is publicly available, open source, and tightly integrated with the software platforms native to deep learning, i.e., Python, PyTorch, and PyCuda. DeepDRR relies on machine learning for material decomposition and scatter estimation in 3D and 2D, respectively, but uses analytic forward projection and noise injection to ensure acceptable computation times. On two X-ray image analysis tasks, namely (1) anatomical landmark detection and (2) segmentation and localization of robot end-effectors, we demonstrate that convolutional neural networks (ConvNets) trained on DeepDRRs generalize well to real data without re-training or domain adaptation. To this end, we use the exact same training protocol to train ConvNets on naïve and DeepDRRs and compare their performance on data of cadaveric specimens acquired using a clinical C-arm X-ray system. Results: Our findings are consistent across both considered tasks. All ConvNets performed similarly well when evaluated on the respective synthetic testing set. However, when applied to real radiographs of cadaveric anatomy, ConvNets trained on DeepDRRs significantly outperformed ConvNets trained on naïve DRRs (p< 0.01). Conclusion: Our findings for both tasks are positive and promising. Combined with complementary approaches, such as image style transfer, the proposed framework for fast and realistic simulation of fluoroscopy from CT contributes to promoting the implementation of machine learning in X-ray-guided procedures. This paradigm shift has the potential to revolutionize intra-operative image analysis to simplify surgical workflows.
CRIS-Team Scopus Importer:2023-03-08
Keywords: Artificial intelligence; Computer assisted surgery; Image guidance; Monte Carlo simulation; Robotic surgery; Segmentation

Encoding CNN Activations for Writer Recognition - Christlein, Vincent; Maier, Andreas (2018 13th IAPR International Workshop on Document Analysis Systems (DAS), 2018)
The encoding of local features is an essential part for writer identification and writer retrieval. While CNN activations have already been used as local features in related works, the encoding of these features has attracted little attention so far. In this work, we compare the established VLAD encoding with triangulation embedding. We further investigate generalized max pooling as an alternative to sum pooling and the impact of decorrelation and Exemplar SVMs. With these techniques, we set new standards on two publicly available datasets (ICDAR13, KHATT).
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.encodi
Keywords: CNN activation features; encoding; generalized max-pooling; triangulation embedding; writer identification; writer retrieval

Endoscopy - Haase, Sven; Maier, Andreas (Springer, 2018)
UnivIS-Import:2019-03-21:Pub.2018.tech.IMMD.IMMD5.endosc

Enhanced Crack Segmentation (eCS): A Reference Algorithm for Segmenting Cracks in Multicrystalline Silicon Solar Cells - Stromer, Daniel; Vetter, Andreas; Özkan, Hasan Can; Probst, Christian; Maier, Andreas (IEEE Journal of Photovoltaics, 2019)
The annually produced quantity of solar modules has steadily increased over the past decades. Rising production speeds and the associated high throughput of wafers, cells, and modules will make an automatized quality inspection mandatory. In the case of visual optical inspection, automatized quality control by using machine vision is already possible. To localize cracks in solar cells, luminescence imaging is used, where several approaches for an automatized inspection exist, but a standard solution for an automatized inspection algorithm is not yet available. This is, in particular, true for multicrystalline solar cells, where the grainy structures in the luminescence images are hard to distinguish from small cracks. Another obstacle in automatic crack analysis is that reference segmentation algorithms are generally not publicly available. Accordingly, a new algorithm can hardly be compared by ranking it to an existing standard. In this paper, we adapted the vesselness algorithm for automatic processing of electroluminescence images of multicrystalline silicon solar cells. Segmentation of cracks in multicrystalline solar cells with the proposed enhanced crack segmentation algorithm shows very promising results on the used database compared with three different commonly used approaches. Furthermore, the segmentation code is made publicly available, and we propose that this algorithm may serve as a reference algorithm, sparking further progress in automatized crack segmentation for multicrystalline silicon solar cells.
CRIS-Team Scopus Importer:2019-05-07
Keywords: Crack detection; crack segmentation; electroluminescence (EL) imaging; multicrystalline solar cell imaging; photovoltaic (PV)

Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks - Denck, Jonas; Guehring, Jens; Maier, Andreas; Rothgang, Eva (Journal of Imaging, 2021)

Enhancing Collaborative Road Scene Reconstruction with Unsupervised Domain Alignment - Venator, Moritz; Aklanoglu, Selcuk; Bruns, Erich; Maier, Andreas (Machine Vision and Applications, 2021)
Scene reconstruction and visual localization in dynamic environments such as street scenes are a challenge due to the lack of distinctive, stable keypoints. While learned convolutional features have proven to be robust to changes in viewing conditions, hand-crafted features still have advantages in distinctiveness and accuracy when applied to Structure from Motion. For collaborative reconstruction of road sections by a car fleet, we apply multimodal domain adaptation as a preprocessing step to align images in their appearance and enhance keypoint matching across viewing conditions while preserving the advantages of hand-crafted features. Training a Generative Adversarial Network for translations between different illumination and weather conditions, we evaluate both qualitative and quantitative aspects of domain adaptation and its impact on feature correspondences. Combined with a multi-feature discriminator, the model is optimized for synthesis of images which do not only improve feature matching but also exhibit a high visual quality. Evaluating the approach with a challenging multi-domain image dataset recorded in various road scenes on multiple test drives, we show that our approach outperforms other traditional and learning-based methods by improving completeness or accuracy of Structure from Motion with multimodal two-domain image collections in eight out of ten test scene
Keywords: Domain adaptation; Image-to-image translation; Structure from Motion; Scene reconstruction; Visual localization

Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning - Tayebi Arasteh, Soroosh; Kuhl, Christiane K.; Saehn, Marwin-Jonathan; Isfort, Peter; Truhn, Daniel; Nebelung, Sven (Scientific Reports, 2023)
Developing robust artificial intelligence (AI) models that generalize well to unseen datasets is challenging and usually requires large and variable datasets, preferably from multiple institutions. In federated learning (FL), a model is trained collaboratively at numerous sites that hold local datasets without exchanging them. So far, the impact of training strategy, i.e., local versus collaborative, on the diagnostic on-domain and off-domain performance of AI models interpreting chest radiographs has not been assessed. Consequently, using 610,000 chest radiographs from five institutions across the globe, we assessed diagnostic performance as a function of training strategy (i.e., local vs. collaborative), network architecture (i.e., convolutional vs. transformer-based), single versus cross-institutional performance (i.e., on-domain vs. off-domain), imaging finding (i.e., cardiomegaly, pleural effusion, pneumonia, atelectasis, consolidation, pneumothorax, and no abnormality), dataset size (i.e., from n = 18,000 to 213,921 radiographs), and dataset diversity. Large datasets not only showed minimal performance gains with FL but, in some instances, even exhibited decreases. In contrast, smaller datasets revealed marked improvements. Thus, on-domain performance was mainly driven by training data size. However, off-domain performance leaned more on training diversity. When trained collaboratively across diverse external institutions, AI models consistently surpassed models trained locally for off-domain tasks, emphasizing FL’s potential in leveraging data diversity. In conclusion, FL can bolster diagnostic privacy, reproducibility, and off-domain reliability of AI models and, potentially, optimize healthcare outcomes.

Enhancing downstream classification of breast abnormalities in contrast enhanced spectral mammography using a neighborhood representation loss - Bhandary Panambur, Adarsh; Madhu, Prathmesh; Bayer, Siming; Maier, Andreas (Medical Imaging 2024: Computer-Aided Diagnosis, 2024)
Contrast-enhanced spectral mammography (CESM) has proven to be a beneficial tool for additional management and diagnosis of breast cancer in patients. Deep learning-based automated tools for analyzing CESM images and detecting abnormalities can be crucial in reducing the workload of radiologists. Even though self-supervised learning (SSL) can significantly influence the quality of learned representations resulting in increased classification performances, it requires large amounts of data often unavailable in the CESM domain. In this work, we propose a novel loss function named Difficulty-Weighted Neighborhood Representation (DWNR) utilizing the SSL-based representations designed explicitly for the downstream classification of the region of interest (ROI) in CESM images in a small dataset setting. We propose utilizing the neighborhood representations extracted from an SSL backbone to guide the loss function in learning robust representations of the dataset’s normal, benign, and malignant ROIs. We formulate the DWNR loss using a combination of a neighborhood representation-guided triplet (NR-triplet) loss and a difficulty-weighted cross-entropy (DWCE) loss. The NR-triplet loss leverages the nearest and farthest neighbor feature representations, extracted from a self-supervised backbone, as positive and negative samples. Meanwhile, the DWCE loss exploits the class information from the nearest neighbors to perform per-sample difficulty weighting. The experimental results on the publicly available CDD-CESM dataset demonstrate that the proposed DWNR loss outperforms traditional loss functions such as Cross-Entropy (CE) and Focal loss by a significant gain of more than 2% absolute mean F1-scores for both SSL-based and ImageNet-SSL-based transfer learning. In addition, we demonstrate the efficacy of employing a DWNR-based ROI classifier for comprehensive CESM whole image analysis in BI-RADS scoring and cancer diagnosis. The results reveal a gain in mean F1-scores of more than 1% in both tasks when using the DWNR loss models for CNN initialization, indicating the significance of the proposed method to whole image analysis. Based on our analysis, incorporating DWNR loss during transfer learning could positively impact automated breast cancer diagnosis using the CESM imaging modality. This novel loss function opens new possibilities for leveraging deep learning in CESM, particularly in scenarios with limited labeled data availability.<br
Keywords: Machine learning; Feature extraction; Image classification; Deep learning; Breast; Breast cancer; Image analysis

Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer - Kulyabin, Mikhail; Zhdanov, Aleksei; Dolganov, Anton; Ronkin, Mikhail; Borisov, Vasilii; Maier, Andreas (Sensors, 2023)

Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer Learning - Madhu, Prathmesh; Villar-Corrales, Angel; Kosti, Ronak Vijaypal; Bendschus, Torsten; Reinhardt, Corinna; Bell, Peter; Maier, Andreas; Christlein, Vincent (Journal of Computing and Cultural Heritage, 2022)
Human pose estimation (HPE) is a central part of understanding the visual narration and body movements of characters depicted in artwork collections, such as Greek vase paintings. Unfortunately, existing HPE methods do not generalise well across domains resulting in poorly recognised poses. Therefore, we propose a two step approach: (1) adapting a dataset of natural images of known person and pose annotations to the style of Greek vase paintings by means of image style-transfer. We introduce a perceptually-grounded style transfer training to enforce perceptual consistency. Then, we fine-tune the base model with this newly created dataset. We show that using style-transfer learning significantly improves the SOTA performance on unlabelled data by more than 6% mean average precision (mAP) as well as mean average recall (mAR). (2) To improve the already strong results further, we created a small dataset (ClassArch) consisting of ancient Greek vase paintings from the 6–5th century BCE with person and pose annotations. We show that fine-tuning on this data with a style-transferred model improves the performance further. In a thorough ablation study, we give a targeted analysis of the influence of style intensities, revealing that the model learns generic domain styles. Additionally, we provide a pose-based image retrieval to demonstrate the effectiveness of our method. The code and pretrained models can be found at https://github.com/angelvillar96/STLPose.<br

Environmental Adaptation with a Small Data Set of the Target Domain - Maier, Andreas; Haderlein, Tino; Nöth, Elmar (Text, Speech and Dialogue, 2006)

Epipolar consistency conditions for motion correction in weight-bearing imaging - Bier, Bastian; Aichert, André; Felsner, Lina; Unberath, Mathias; Levenstone, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin 2017 Algorithmen Systeme Anwendungen (Bildverarbeitung für die Medizin 2017), 2017)

Epipolar Consistency Guided Beam Hardening Reduction - ECC² - Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Huang, Xiaolin; Maier, Andreas (Proceedings of the 14th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.epipol_8

Epipolar Consistency in Fluoroscopy for Image-Based Tracking - Aichert, André; Wang, Jian; Schaffert, Roman; Dörfler, Arnd; Hornegger, Joachim; Maier, Andreas (Proceedings of the British Machine Vision Conference 2015, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.epipol_3

Epipolar Consistency in Transmission Imaging - Aichert, André; Berger, Martin; Wang, Jian; Maass, Nicole; Dörfler, Arnd; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Medical Imaging, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.epipol
Keywords: X-rays; motion estimation; geometry; stereo vision; X-ray tomography.

Epithelial Cell Detection in Endomicroscopy Images of the Vocal Folds - Mualla, Firas; Schöll, Simon; Bohr, Christopher; Neumann, Helmut; Maier, Andreas (Springer Proceedings in Physics 154, Proceedings of InterM, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.epithe

Epoch-wise label attacks for robustness against label noise - Gündel, Sebastian; Maier, Andreas (Bildverarbeitung für die Medizin 2020, 2020)
The current accessibility to large medical datasets for training convolutional neural networks is tremendously high. The associated dataset labels are always considered to be the real "ground truth". However, the labeling procedures often seem to be inaccurate and many wrong labels are integrated. This may have fatal consequences on the performance of both training and evaluation. In this paper, we show the impact of label noise in the training set on a specific medical problem based on chest X-ray images. With a simple one-class problem, the classification of tuberculosis, we measure the performance on a clean evaluation set when training with label-corrupt data. We develop a method to compete with incorrectly labeled data during training by randomly attacking labels on individual epochs. The network tends to be robust when flipping correct labels for a single epoch and initiates a good step to the optimal minimum on the error surface when flipping noisy labels. On a baseline with an AUC (Area under Curve) score of 0.924, the performance drops to 0.809 when 30% of our training data is misclassified. With our approach the baseline performance could almost be maintained, the performance raised to 0.918.<br

Erlangen-CLP: A Large Annotated Corpus of Speech from Children with Cleft Lip and Palate - Bocklet, Tobias; Maier, Andreas; Riedhammer, Korbinian Thomas; Eysholdt, Ulrich; Nöth, Elmar (2014)
In this paper we describe Erlangen-CLP, a large speech database of children with Cleft Lip and Palate. More than 800 German children with CLP (most of them between 4 and 18 years old) and 380 age matched control speakers spoke the semi-standardized PLAKSS test that consists of words with all German phonemes in different positions. So far 250 CLP speakers were manually transcribed, 120 of these were analyzed by a speech therapist and 27 of them by four additional therapists. The tharapists marked 6 different processes/criteria like pharyngeal backing and hypernasality which typically occur in speech of people with CLP. We present detailed statistics about the the marked processes and the inter-rater agreement.
Keywords: Cleft lip and palate; pathologic speech; Children's speech

Erlernbarkeitsstudie eines vibrotaktilen Armbands für assistive Navigation - Calim, Hakan; Maier, Andreas (Informatik aktuell, 2020)
CRIS-Team Scopus Importer:2020-04-21

Estimate, Compensate, Iterate: Joint Motion Estimation and Compensation in 4-D Cardiac C-arm Computed Tomography - Taubmann, Oliver; Lauritsch, Günter; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim (Medical Image Computing and Computer-Assisted Intervention MICCAI 2015, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.estima_9

Estimating follow-up CTs from geometric deformations of catheter implants in interstitial breast brachytherapy: A feasibility study using electromagnetic tracking - Dürrbeck, Christopher; Schulz, Moritz; Pflaum, Leonie; Kallis, Karoline; Geimer, Tobias; Abu-Hossin, Nadin; Strnad, Vratislav; Maier, Andreas; Fietkau, Rainer; Bert, Christoph (Medical Physics, 2023)
Background: Electromagnetic tracking (EMT) systems have been shown to provide valuable information on the geometry of catheter implants in breast cancer patients undergoing interstitial brachytherapy (iBT). In the context of an extended patient-specific, pre-treatment verification, EMT can play a key role in determining the potential need and, if applicable, the appropriate time for treatment adaptation. To detect dosimetric shortcomings the relative position between catheters, and target volume and critical structures must be known. Since EMT cannot provide the anatomical context and standard imaging techniques such as cone-beam CT are not yet available in most brachytherapy suites, it is not possible to detect anatomic changes on a daily or fraction basis, so the need for adaptive planning cannot be identified. Purpose: The aim of this feasibility study is to develop and evaluate a technique capable of estimating follow-up CTs at any time based on the initial treatment planning CT (PCT) and surrogate information about changes of the implant geometry from an EMT system. Methods: A deformation vector field is calculated from two different implant reconstructions acquired in treatment position through EMT, the first immediately after the PCT and the second at another time point during the course of treatment. The calculation is based on discrete displacement vectors of pairs of control and target points. These are extrapolated by means of different radial basis functions in order to cover the entire CT volume. The adequate parameters for the calculation of the deformation field were identified. By warping the PCT according to the deformation field, one obtains an estimated CT (ECT) that reflects the geometric changes. For the proof of concept, ECTs were computed for the time point of the clinical follow-up CT (FCT) that is embedded in the treatment workflow after the fourth fraction. Results: ECT and clinical FCTs of 20 patients were compared to each other quantitatively in terms of absolute Hounsfield unit differences in the planning target volume (PTV) and in a convex hull (CH) enclosing the catheters. The median differences were 31.2 and 29.5 HU for the CH and the PTV, respectively. Conclusion: The proposed ECT approach was able to approximate the “anatomy of the day” and therefore, in principle, allows a dosimetric appraisal of the treatment plan quality before each fraction. In this way, it can contribute to a more detailed patient-specific quality assurance in iBT of the breast and help to identify the timing for a potential treatment adaptation.
CRIS-Team Scopus Importer:2023-08-25
Keywords: assurance; breast cancer; electromagnetic tracking; interstitial brachytherapy

Estimating the Fundamental Matrix Without Point Correspondences WithApplication to Transmission Imaging - Würfl, Tobias; Aichert, André; Maass, Nicole; Dennerlein, Frank; Maier, Andreas (Proceedings of the IEEE International Conference on Computer Vision, 2019)
We present a general method to estimate the fundamental matrix from a pair of images under perspective projection without the need for image point correspondences. Our method is particularly well-suited for transmission imaging, where state-of-the-art feature detection and matching approaches generally do not perform well. Estimation of the fundamental matrix plays a central role in auto-calibration methods for reflection imaging. Such methods are currently not applicable to transmission imaging. Furthermore, our method extends an existing technique proposed for reflection imaging which potentially avoids the outlier-prone feature matching step from an orthographic projection model to a perspective model. Our method exploits the idea that under a linear attenuation model line integrals along corresponding epipolar lines are equal if we compute their derivatives in orthogonal direction to their common epipolar plane. We use the fundamental matrix to parametrize this equality. Our method estimates the matrix by formulating a non-convex optimization problem, minimizing an error in our measurement of this equality. We believe this technique will enable the application of the large body of work on image-based camera pose estimation to transmission imaging leading to more accurate and more general motion compensation and auto-calibration algorithms, particularly in medical X-ray and Computed Tomography imaging
Keywords: Structure from Motion; CT reconstruction

Estimation of convolution kernels for X-ray scatter signal correction Schätzung von Faltungskernen zur Röntgen-Streusignalkorrektur - Luckner, Christoph; Maier, Andreas; Dennerlein, Frank (Informatik aktuell, 2014)
CRIS-Team Scopus Importer:2022-08-05

Estimation of femoral artery access location for anatomic deformation correction - Breininger, Katharina; Pfister, Marcus; Koutouzi, Giasemi; Kowarschik, Markus; Maier, Andreas (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.estima

Estimation of missing fan-beam projections using frequency consistency conditions - Pohlmann, Marcel; Berger, Martin; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Proceedings of the third international conference on image formation in x-ray computed tomography, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.estima
Keywords: GRK-1773

Estimation of statistical weights for model-based iterative ct reconstruction - Haase, Viktor; Stierstorfer, K.; Hahn, K.; Schöndube, H.; Maier, Andreas; Noo, F. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2021)
Since the introduction of model-based iterative reconstruction for computed tomography (CT) by Thibault et al. in 2007, statistical weights play an important role in the problem formulation with the objective to improve image quality. Statistical weights depend on the variance of measurements. However, this variance is not known and therefore weights must be estimated. So far, the literature neither discusses how statistical weights should be estimated nor how accurate the estimation needs to be. Our submission aims at filling this gap in the literature. Specifically, we propose an estimation procedure for statistical weights and assess this procedure with real CT data. The estimated weights are compared against (clinically unpractical) sample weights obtained from repeated scans. The results show that the estimation procedure delivers reliable results for the rays that pass through the scanned object. Four imaging scenarios are considered; in each case the root mean square difference between the estimated and sample weights is below 5% of the maximum statistical weight value. When used for reconstruction, these differences are seen to have little impact: all voxel values within soft tissue (low contrast) regions differ by less than 1 HU. Our results demonstrate that the statistical weights can be sufficiently well estimated to closely approach the result that would be obtained if the weights were known.
CRIS-Team Scopus Importer:2021-04-16
Keywords: Computed tomography; Image quality; Model-based iterative reconstruction; Statistical weights

Estimation of the Source-Detector Alignment of Cone-Beam X-ray Systems using Collimator Edge Tracking - Luckner, Christoph; Mertelmeier, Thomas; Maier, Andreas; Ritschl, Ludwig (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.estima

Evaluation of geometry and size dependence for a polynomial water precorrection approach in C-arm computed tomography - Schönke, Daniel; Lietzmann, Florian; Maier, Andreas; Schad, Lothar R. (ECR 2015 Book of Abstracts - C - Scientific and Educational Exhibits, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.evalua
Keywords: Quality assurance; Computer Applications-General; Cone beam CT; Radiation physics

Evaluation of Interpolation Methods for Motion Compensated Tomographic Reconstruction for Cardiac Angiographic C-arm Data - Müller, Kerstin; Zheng, Yefeng; Lauritsch, Günter; Rohkohl, Christopher; Schwemmer, Chris; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim (Proceedings of the second international conference on image formation in x-ray computed tomography, 2012)
http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2012/Mueller12-EOI.pdf

Evaluation of interpolation methods for surface-based motion compensated tomographic reconstruction for cardiac angiographic C-arm data - Müller, Kerstin; Schwemmer, Chris; Hornegger, Joachim; Zheng, Yefeng; Wang, Yang; Lauritsch, Günter; Rohkohl, Christopher; Maier, Andreas; Schultz, Carl; Fahrig, Rebecca (Medical Physics, 2013)
Purpose: For interventional cardiac procedures, anatomical and functional information about the cardiac chambers is of major interest. With the technology of angiographic C-arm systems it is possible to reconstruct intraprocedural three-dimensional (3D) images from 2D rotational angiographic projection data (C-arm CT). However, 3D reconstruction of a dynamic object is a fundamental problem in C-arm CT reconstruction. The 2D projections are acquired over a scan time of several seconds, thus the projection data show different states of the heart. A standard FDK reconstruction algorithm would use all acquired data for a filtered backprojection and result in a motion-blurred image. In this approach, a motion compensated reconstruction algorithm requiring knowledge of the 3D heart motion is used. The motion is estimated from a previously presented 3D dynamic surface model. This dynamic surface model results in a sparse motion vector field (MVF) defined at control points. In order to perform a motion compensated reconstruction, a dense motion vector field is required. The dense MVF is generated by interpolation of the sparse MVF. Therefore, the influence of different motion interpolation methods on the reconstructed image quality is evaluated. Methods: Four different interpolation methods, thin-plate splines (TPS), Shepard's method, a smoothed weighting function, and a simple averaging, were evaluated. The reconstruction quality was measured on phantom data, a porcine model as well as on in vivo clinical data sets. As a quality index, the 2D overlap of the forward projected motion compensated reconstructed ventricle and the segmented 2D ventricle blood pool was quantitatively measured with the Dice similarity coefficient and the mean deviation between extracted ventricle contours. For the phantom data set, the normalized root mean square error (nRMSE) and the universal quality index (UQI) were also evaluated in 3D image space. Results: The quantitative evaluation of all experiments showed that TPS interpolation provided the best results. The quantitative results in the phantom experiments showed comparable nRMSE of ≈0.047 ± 0.004 for the TPS and Shepard's method. Only slightly inferior results for the smoothed weighting function and the linear approach were achieved. The UQI resulted in a value of ≈ 99% for all four interpolation methods. On clinical human data sets, the best results were clearly obtained with the TPS interpolation. The mean contour deviation between the TPS reconstruction and the standard FDK reconstruction improved in the three human cases by 1.52, 1.34, and 1.55 mm. The Dice coefficient showed less sensitivity with respect to variations in the ventricle boundary. Conclusions: In this work, the influence of different motion interpolation methods on left ventricle motion compensated tomographic reconstructions was investigated. The best quantitative reconstruction results of a phantom, a porcine, and human clinical data sets were achieved with the TPS approach. In general, the framework of motion estimation using a surface model and motion interpolation to a dense MVF provides the ability for tomographic reconstruction using a motion compensation technique. © 2013 American Association of Physicists in Medicine.

Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge - Xiao, Yiming; Maier, Andreas; Wein, Wolfgang; Shams, Roozbeh; Kadoury, Samuel; Drobny, David; Modat, Marc; Reinertsen, Ingerid; Rivaz, Hassan; Chabanas, Matthieu; Fortin, Maryse; MacHado, Ines; et al. (IEEE transactions on medical imaging, 2020)
In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.
CRIS-Team Scopus Importer:2020-03-27
Keywords: brain; brain shift; MRI; Registration; tumor; ultrasound

Evaluation of Spectrum Mismatching using Spectrum Binning Approach for Statistical Polychromatic Reconstruction in CT - Yang, Qiao; Wu, Meng; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.evalua_9

Evaluation of spectrum mismatching using spectrum binning for statistical polychromatic reconstruction in CT - Yang, Qiao; Wu, Meng; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Informatik aktuell, 2014)
In CT, the nonlinear attenuation characteristics of polychromatic X-rays cause beam hardening artifacts in the reconstructed images. Statistical algorithms can effectively correct beam hardening artifacts while providing the benefit of noise reduction. In practice, a big challenge for CT is the difficulty at acquiring accurate energy spectrum information, which hinders the efficiency of beam hardening correction approaches that require the spectrum as prior knowledge such as the statistical methods. In this paper, we used proposed energy spectrum binning approach for reducing prior knowledge from full spectrum to three energy bins to compare the results when applying parameters optimized for one spectrum to data measured using a different spectrum.
CRIS-Team Scopus Importer:2022-08-05

Evaluation of speech intelligibility for children with cleft lip and palate by means of automatic speech recognition - Schuster, Maria; Maier, Andreas; Haderlein, Tino; Nkenke, Emeka; Wohlleben, Ulrike; Rosanowski, Frank; Eysholdt, Ulrich; Nöth, Elmar (International Journal of Pediatric Otorhinolaryngology, 2006)

Evaluation of state-of-the-art hardware architectures for fast cone-beam CT reconstruction - Scherl, Holger; Kowarschik, Markus; Hofmann, Hannes; Keck, Benjamin; Hornegger, Joachim (Parallel Computing, 2012)
We present an evaluation of state-of-the-art computer hardware architectures for implementing the FDK method, which solves the 3-D image reconstruction task in cone-beam computed tomography (CT). The computational complexity of the FDK method prohibits its use for many clinical applications unless appropriate hardware acceleration is employed. Today's most powerful hardware architectures for high-performance computing applications are based on standard multi-core processors, off-the-shelf graphics boards, the Cell Broadband Engine Architecture (CBEA), or customized accelerator platforms (e.g., FPGA-based computer components). For each hardware platform under consideration, we describe a thoroughly optimized implementation of the most time-consuming parts of the FDK algorithm; the filtering step as well as the subsequent back-projection step. We further explain the required code transformations to parallelize the algorithm for the respective target architecture. We compare both the implementation complexity and the resulting performance of all architectures under consideration using the same two medical datasets which have been acquired using a standard C-arm device. Our optimized back-projection implementations achieve at least a speedup of 6.5 (CBEA, two processors), 22.0 (GPU, single board), and 35.8 (FPGA, 9 chips) compared to a standard workstation equipped with a quad-core processor. © 2011 Elsevier B.V. All rights reserved.