Geometric Adjustment of X-ray Tomosynthesis - Grulich, Tobias; Holub, Wolfgang; Haßler, Ulf; Aichert, André; Maier, Andreas (Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.geomet

Geometrical jitter correction in computed tomography - Maass, Nicole; Dennerlein, Frank; Aichert, André; Maier, Andreas (Proceedings of the third international conference on image formation in X-ray computed tomography, 2014)
UnivIS-Import:2018-09-06:Pub.2014.tech.IMMD.IMMD5.geomet_0

Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography - Wagner, Fabian; Thies, Mareike; Maul, Noah; Pfaff, Laura; Aust, Oliver; Pechmann, Sabrina; Syben, Christopher; Maier, Andreas (17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2023)
The diagnostic quality of computed tomography (CT) scans is usually restricted by the induced patient dose, scan speed, and image quality. Sparse-angle tomographic scans reduce radiation exposure and accelerate data acquisition, but suffer from image artifacts and noise. Existing image processing algorithms can restore CT reconstruction quality but often require large training data sets or can not be used for truncated objects. This work presents a self-supervised projection inpainting method that allows optimizing missing projective views via gradient-based optimization. By reconstructing independent stacks of projection data, a self-supervised loss is calculated in the CT image domain and used to directly optimize projection image intensities to match the missing tomographic views constrained by the projection geometry. Our experiments on real X-ray microscope (XRM) tomographic mouse tibia bone scans show that our method improves reconstructions by 3.1-7.4%/7.7-17.6% in terms of PSNR/SSIM with respect to the interpolation baseline. Our approach is applicable as a flexible self-supervised projection inpainting tool for tomographic applications.<br

Geometric modeling of the aortic inner and outer vessel wall from CTA for aortic dissection analysis - Eigen, Katharina; Maier, Andreas; Dohle, Daniel-Sebastian; Sühling, Michael; Wels, Michael (Proc. SPIE, 2018)
In this paper, we present a novel method for modeling both layers of the aortic walls in cases of aortic dissections for analysis from Computed Tomography Angiography. It involves a fast initialization of the associated physiological and pathological lumina and further editing on non-linearly formatted and cross-sectional views. Fast and accurate derivation of 3D models of these inner and outer vessel walls is crucial to analyze true and false lumen, to accelerate processing times in research studies, and to answer therapy questions. Since the aorta is a relatively large vessel, our system makes use of a point-based surface interpolation with compactly supported radial basis functions requiring only few surface constraints. Where possible, we use a semi-Automatic approach to segment the vessel walls using an Active Contour Model, which detects the contours in the vessel's cross-sectional planes, stating the constraints for interpolation. After initialization, editing on non-linearly formatted and crosssectional views is possible due to handling user input through tangent frame bundles to dismiss contradictory surface samples before updating the models with the new constraints. Our proposed method was evaluated in a user study to measure processing times and achievable model accuracy with respect to an expert-defined ground truth. The users needed 19 minutes on average to derive one model (both walls) and attained a mean surface distance of about 1.0 mm for the outer vessel wall, respectively, 1.6 mm for the inner wall. Using our method instead of open source program for geometric modeling saves 26 minutes per dataset.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.geomet_0
Keywords: Active Contour Model; Aortic Dissection; Tangent Frame Bundles

Geometric primitive refinement for structured light cameras - Fürsattel, Peter; Placht, Simon; Maier, Andreas; Riess, Christian (Machine Vision and Applications, 2018)
Keywords: Geometric primitives; Structured light; Range imaging

Glacier Calving Front Segmentation Using Attention U-Net - Holzmann, Michael; Davari, Amirabbas; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (2021 IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), 2021)
An essential climate variable to determine the tidewater glacier status is the location of the calving front position and the separation of seasonal variability from long-term trends. Previous studies have proposed deep learning-based methods to semi-automatically delineate the calving fronts of tidewater glaciers. They used U-Net to segment the ice and non-ice regions and extracted the calving fronts in a post-processing step. In this work, we show a method to segment the glacier calving fronts from SAR images in an end-to-end fashion using Attention U-Net. The main objective is to investigate the attention mechanism in this application. Adding attention modules to the state-of-the-art U - N et network lets us analyze the learning process by extracting its attention maps. We use these maps as a tool to search for proper hyperparameters and loss functions in order to generate higher qualitative results. Our proposed attention U-Net performs comparably to the standard U-Net while providing additional insight into those regions on which the network learned to focus more. In the best case, the attention U-Net achieves a 1.5 % better Dice score compared to the canonical U-Net with a glacier front line prediction certainty of up to 237.12 meters.

Glaucoma classification in 3 x 3 mm en face macular scans using deep learning in a different plexus - Schottenhamml, Julia; Wuerfl, Tobias; Mardin, Sophia; Ploner, Stefan; Husvogt, Lennart; Hohberger, Bettina; Lämmer, Robert; Mardin, Christian; Maier, Andreas (Biomedical Optics Express, 2021)
Glaucoma is among the leading causes of irreversible blindness worldwide. If diagnosed and treated early enough, the disease progression can be stopped or slowed down. Therefore, it would be very valuable to detect early stages of glaucoma, which are mostly asymptomatic, by broad screening. This study examines different computational features that can be automatically deduced from images and their performance on the classification task of differentiating glaucoma patients and healthy controls. Data used for this study are 3 x 3 mm en face optical coherence tomography angiography (OCTA) images of different retinal projections (of the whole retina, the superficial vascular plexus (SVP), the intermediate capillary plexus (ICP) and the deep capillary plexus (DCP)) centered around the fovea. Our results show quantitatively that the automatically extracted features from convolutional neural networks (CNNs) perform similarly well or better than handcrafted ones when used to distinguish glaucoma patients from healthy controls. On the whole retina projection and the SVP projection, CNNs outperform the handcrafted features presented in the literature. Area under receiver operating characteristics (AUROC) on the SVP projection is 0.967, which is comparable to the best reported values in the literature. This is achieved despite using the small 3 x 3 mm field of view, which has been reported as disadvantageous for handcrafted vessel density features in previous works. A detailed analysis of our CNN method, using attention maps, suggests that this pertbrmance increase can be partially explained by the CNN automatically relying more on areas of higher relevance for feature extraction. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
CRIS-Team WoS Importer:2021-12-17

GMM-based Synthetic Samples for Classification of Hyperspectral Images with Limited Training Data - Davari, Amirabbas; Aptoula, Erchan; Yanikoglu, Berrin; Maier, Andreas; Riess, Christian (IEEE Geoscience and Remote Sensing Letters, 2018)
Keywords: hyperspectral remote sensing image classification; limited training data; synthetic data; EMAP

GMM Supervectors for Limited Training Data in Hyperspectral Remote Sensing Image Classification - Davari, Amirabbas; Christlein, Vincent; Vesal, Sulaiman; Maier, Andreas; Riess, Christian (Proceedings of the 17th International Conference on Computer Analysis of Images and Patterns Part II, 2017)

GPU Denoising for Computed Tomography - Maier, Andreas; Fahrig, Rebecca (Graphics Processing Unit-Based High Performance Computing in Radiation Therapy, 2015)
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.IMMD5.gpuden
Keywords: noise; denoising; GPU

Gradient-based geometry learning for fan-beam CT reconstruction - Thies, Mareike; Wagner, Fabian; Maul, Noah; Folle, Lukas; Meier, Manuela; Rohleder, Maximilian; Schneider, Linda-Sophie; Pfaff, Laura; Gu, Mingxuan; Utz, Jonas; Denzinger, Felix; Manhart, Michael; et al. (Physics in Medicine and Biology, 2023)
Objective. Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry. Approach. The CT fan-beam reconstruction is analytically derived with respect to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion-affected reconstruction alone. Main results. The algorithm improves the structural similarity index measure (SSIM) from 0.848 for the initial motion-affected reconstruction to 0.946 after compensation. It also generalizes to real fan-beam sinograms which are rebinned from a helical trajectory where the SSIM increases from 0.639 to 0.742. Significance. Using the proposed method, we are the first to optimize an autofocus-inspired algorithm based on analytical gradients. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep model
Keywords: computed tomography; projective geometry; differentiable programming; motion compensation

Gradient-Delay-Compensated Radial MRI for Fat Content and Fatty Acid Composition Quantification - Schneider, Manuel; Lugauer, Felix; Hoppe, Elisabeth; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the ISMRM Workshop on Quantitative Body Imaging, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.gradie_1

Graph Convolutional Regression of Cardiac Depolarization from Sparse Endocardial Maps - Meister, Felix; Passerini, Tiziano; Audigier, Chloé; Lluch, Èric; Mihalef, Viorel; Ashikaga, Hiroshi; Maier, Andreas; Halperin, Henry; Mansi, Tommaso (Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges, 2021)
Electroanatomic mapping as routinely acquired in ablation therapy of ventricular tachycardia is the gold standard method to identify the arrhythmogenic substrate. To reduce the acquisition time and still provide maps with high spatial resolution, we propose a novel deep learning method based on graph convolutional neural networks to estimate the depolarization time in the myocardium, given sparse catheter data on the left ventricular endocardium, ECG, and magnetic resonance images. The training set consists of data produced by a computational model of cardiac electrophysiology on a large cohort of synthetically generated geometries of ischemic hearts. The predicted depolarization pattern has good agreement with activation times computed by the cardiac electrophysiology model in a validation set of five swine heart geometries with complex scar and border zone morphologies. The mean absolute error hereby measures 8 ms on the entire myocardium when providing 50% of the endocardial ground truth in over 500 computed depolarization patterns. Furthermore, when considering a complete animal data set with high density electroanatomic mapping data as reference, the neural network can accurately reproduce the endocardial depolarization pattern, even when a small percentage of measurements are provided as input features (mean absolute error of 7 ms with 50% of input samples). The results show that the proposed method, trained on synthetically generated data, may generalize to real data.<br
Keywords: Cardiac computational modeling; Deep learning; Electroanatomical contact mapping

Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Calcium Scoring - Durlak, Felix; Wels, Michael; Schwemmer, Chris; Sühling, Michael; Steidl, Stefan; Maier, Andreas (Machine Learning in Medical Imaging, 2017)
UnivIS-Import:2017-11-06:Pub.2017.tech.IMMD.IMMD5.growin

Guest editorial of the IJCARS—BVM 2018 special issue - Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (International Journal of Computer Assisted Radiology and Surgery, 2019)
CRIS-Team Scopus Importer:2019-08-06

Guest editorial of the IJCARS MICCAI 2016 special issue - Ourselin, Sebastien; Sabuncu, Mert R.; Wells, William; Joskowicz, Leo; Unal, Gozde; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2017)
CRIS-Team Scopus Importer:2022-06-05

Guided Noise Reduction for Spectral CT with Energy-Selective Photon Counting Detectors - Manhart, Michael; Fahrig, Rebecca; Hornegger, Joachim; Dörfler, Arnd; Maier, Andreas (Proceedings of the Third CT Meeting, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.guided_6

Guided Noise Reduction with Streak Removal for High Speed Flat Detector CT Perfusion - Manhart, Michael; Aichert, André; Kowarschik, Markus; Deuerling-Zheng, Yu; Struffert, Tobias; Dörfler, Arnd; Maier, Andreas; Hornegger, Joachim (Proceedings NSS/MIC 2013, 2013)
Tissue perfusion measurement using C-arm angiography systems capable of CT-like imaging (flat detector CT (FD-CT)) is a novel technique with high potential benefit for catheter-guided treatment of stroke in the interventional suite. New high speed protocols (HSP) with increased C-arm rotation speed enable fast acquisitions of FD-CT volumes and allow for sampling the contrast flow with improved temporal resolution. However, the peak contrast attenuation values of brain tissue typically lie in a range of 5-30 HU. Thus perfusion imaging is very sensitive to noise. Recently we introduced the FDK-JBF denoising technique based on Feldkamp (FDK) reconstruction followed by denoising in volume space using joint bilateral filtering (JBF). In the evaluation FDK-JBF achieved comparable results to algebraic techniques, but is computationally less costly. Yet the angular sampling of the projection data in the HSP is coarse, which leads to streak artifacts in the reconstructed volumes. Mask volumes are subtracted from the contrast agent enhanced (bolus) volumes and the streak artifacts are subtracted out if the patient does not move during the acquisition. However, in case of motion the streak artifacts will not be identical in the mask and bolus volumes. We show that these streaks can lead to severe artifacts in the perfusion maps and describe a novel technique for streak removal (SR), which is based on streak detection by using time-contrast curve analysis.We evaluated the FDK-SR-JBF algorithm in a phantom and a patient study and show that noise and streaks can be reduced within a short computation time. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.guided_5
Keywords: Flat detector CT; noise reduction; Perfusion imaging; stroke treatment

Handling Label Uncertainty on the Example of Automatic Detection of Shepherd’s Crook RCA in Coronary CT Angiography - Denzinger, Felix; Wels, Michael; Taubmann, Oliver; Kordon, Florian Johannes; Wagner, Fabian; Mehltretter, Stephanie; Gülsün, Mehmet A.; Schöbinger, Max; André, Florian; Buss, Sebastian J.; Görich, Johannes; Sühling, Michael; et al. (Proceedings - International Symposium on Biomedical Imaging, 2023)
Coronary artery disease (CAD) is often treated minimally invasively with a catheter being inserted into the diseased coronary vessel. If a patient exhibits a Shepherd’s Crook (SC) Right Coronary Artery (RCA) – an anatomical norm variant of the coronary vasculature – the complexity of this procedure is increased. Automated reporting of this variant from coronary CT angiography screening would ease prior risk assessment. We propose a 1D convolutional neural network which leverages a sequence of residual dilated convolutions to automatically determine this norm variant from a prior extracted vessel centerline. As the SC RCA is not clearly defined with respect to concrete measurements, labeling also includes qualitative aspects. Therefore, 4.23 % samples in our dataset of 519 RCA centerlines were labeled as unsure SC RCAs, with 5.97 % being labeled as sure SC RCAs. We explore measures to handle this label uncertainty, namely global/model-wise random assignment, exclusion, and soft label assignment. Furthermore, we evaluate how this uncertainty can be leveraged for the determination of a rejection class. With our best configuration, we reach an area under the receiver operating characteristic curve (AUC) of 0.938 on confident labels. Moreover, we observe an increase of up to 0.020 AUC when rejecting 10 % of the data and leveraging the labeling uncertainty information in the exclusion process.<br
CRIS-Team Scopus Importer:2023-10-06
Keywords: Coronary CT Angiography; Label Uncertainty; Shepherd's Crook RCA

Handwritten Text Recognition Error Rate Reduction in Historical Documents using Naive Transcribers - Christlein, Vincent; Nicolaou, Anguelos; Schlauwitz, Thorsten; Späth, Sabrina; Herbers, Klaus; Maier, Andreas (2018)
Handwritten text recognition (HTR) is a difficult research problem. In particular for historical documents, this task is hard as handwriting style, orthography, and text quality pose significant challenges. Creation of a single multi-purpose HTR system seems to be out of reach for current state-of-the-art systems. Therefore, we are interested in fast creation of specialized HTR systems for a particular set of historical documents. Still manual annotation by historical experts is expensive and can often not be applied at a large scale. Instead, we use the transcripts of naive transcribers that may still contain a significant amount of errors. In this paper, we propose to fuse the recognized word-chain with naive transcribers that can be obtained in a cost-effective way. For the actual fusion, we rely on a word-level approach, the so-called Recognizer Output Voting Error Reduction (ROVER). Results indicate that we are able to reduce the Word Error Rate (WER) of an HTR system trained with only few pages from 2.6 % to 19.2% with two additional transcribers with 25.1% and 27.1% WER each. This performance is already close to current state-of-the-art systems trained with significantly more dat
Keywords: handwritten text recognition; line fusion; naive transcribers; Nürnberg; Briefbücher; 15. Jahrhundert

Hardware Failure Prediction on Imbalanced Times Series Data: Generation of Artificial Data Using Gaussian Process and Applying LSTMFCN to Predict Broken Hardware - Rücker, Nadine; Pflueger, Lea; Maier, Andreas (Journal of Digital Imaging, 2021)
Magnetic resonance imaging (MRI) systems and their continuous, failure-free operation is crucial for high-quality diagnostics and seamless workflows. One important hardware component is coils as they detect the magnetic signal. Before every MRI scan, several image features are captured which represent the used coil’s condition. These image features recorded over time are used to train machine learning models for classification of coils into normal and broken coils for faster and easier maintenance. The state-of-the-art techniques for classification of time series involve different kinds of neural networks. We leveraged sequential data and trained three models, long short-term memory (LSTM), fully convolutional network (FCN), and the combination of those called LSTMFCN as reported by Karim et al. (IEEE access 6:1662–1669, 2017). We found LSTMFCN to combine the benefits of LSTM and FCN. Thus, we achieved the highest F1-score of 87.45% and the highest accuracy of 99.35% using LSTMFCN. Furthermore, we tackled the high data imbalance of only 2.1% data collected from broken coils by training a Gaussian process (GP) regressor and adding predicted sequences as artificial samples to our broken labelled data. Adding 40 synthetic samples increased the classification results of LSTMFCN to an F1-score of 92.30% and accuracy of 99.83%. Thus, MRI head/neck coils can be classified normal or broken by training a LSTMFCN on image features, successfully. Augmenting the data using GP-generated samples can improve the performance even further.
CRIS-Team Scopus Importer:2021-01-15
Keywords: Gaussian process regression; Hardware failure prediction; Machine learning; Time series classification

Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble - Chatterjee, Satyaki; Ramachandran, Adithya; Neergaard, Thorkil F.B.; Maier, Andreas; Bayer, Siming (NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning, 2022)
One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an unprecedented advantage in realizing an optimised supply of thermal energies through proactive techniques such as load forecasting. In this paper, we propose a forecasting framework for heat demand based on neural networks where the time series are encoded as scalograms equipped with the capacity of embedding exogenous variables. Subsequently, CNNs are utilized to predict the heat load multi-step ahead. Finally, the proposed framework is compared with other state-of-the-art methods, such as SARIMAX and LSTM. The quantitative results from retrospective experiments show that the proposed framework consistently outperforms the state-of-the-art baseline method with real-world data acquired from Denmark. A minimal mean error of 7.54% for MAPE and 417 kW for RMSE is achieved with the proposed framework in comparison to all other methods.<br
Keywords: CNN; District Heating; Time Serie Forecasting

Helfen "Fallen" bei verrauschten Daten? - Spracherkennung mit TRAPs - Maier, Andreas; Hacker, Christian; Steidl, Stefan; Nöth, Elmar (Fortschritte der Akustik: Plenarvorträge und Fachbeiträge der 31. Deutschen Jahrestagung für Akustik DAGA 2005, München, 2005)

Helical Dark-field Fiber Reconstruction - Felsner, Lina; Syben-Leisner, Christopher; Maier, Andreas; Riess, Christian (International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2021)

High-resolution dynamic CE-MRA of the thorax enabled by iterative TWIST reconstruction - Wetzl, Jens; Forman, Christoph; Wintersperger, Bernd J.; D'Errico, Luigia; Schmidt, Michaela; Mailhé, Boris; Maier, Andreas; Stalder, Aurélien F. (Magnetic Resonance in Medicine, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.highre
Keywords: dynamic contrast-enhanced magnetic resonance angiography; iterative reconstruction; TWIST

High-speed slot-scanning radiography using small-angle tomosynthesis: Investigation of spatial resolution - Luckner, Christoph; Herbst, Magdalena; Weber, Thomas; Beister, Marcel; Ritschl, Ludwig; Kappler, Steffen; Maier, Andreas (Medical Physics, 2019)
Purpose This paper studies spatial resolution that is achievable with a fast slot-scanning tomosynthesis approach for orthopedic examinations. Hereby, we use parallel scanning motion implemented in a twin robotic x-ray system. Methods We have measured and analyzed the modulation transfer function (MTF) for various combinations of scanning speed, x-ray tube voltage, pulse length, the nominal focal spot size as well as source-to-object distances. Moreover, we present a theoretical model which describes the system in terms of the MTF. The system was equipped with newly developed linear trajectory prototypes for slot scanning. The acquired images form the basis for a small-angle tomosynthesis reconstruction. In total, three different scanning speeds (27, 14, 8 cm/s), pulse lengths (1, 2, 4 ms), tube voltages (80, 100, 120 kV), two nominal focal spot sizes (0.6, 1.0), and three source-to-object distances (950, 1050, 1150 mm) were investigated. To determine the resolution capabilities, we measured the MTF for the given parameter space. The results were then used to design a filter that yields a desired resolution in the reconstructed image. In addition, we also measured the noise power spectrum (NPS) to show the influence of the aforementioned filters on the noise distribution. Results We have shown that the presented model is in good agreement with the performed measurements. Scanning speed and pulse width have an impact on the MTF in the scanning direction. Up to a travel distance of 0.3 mm during an x-ray pulse, an isotropic resolution can be achieved. Longer pulse width or higher scanning speed cause anisotropic resolution. Moreover, it is shown that none of the investigated parameters have an influence on the MTF perpendicular to the scanning direction (slot direction). The 10% MTF value ranges between 9 and 18 lp/cm in the scanning direction and about 18 lp/cm in slot direction. Tube voltage, nominal focal spot size as well as the source-to-object distance showed no major impact on the system MTF. In terms of the anisotropic resolution capabilities, we have shown that limiting the resolution in the slot direction to obtain isotropic resolution is possible yet at the cost of an inhomogeneous noise pattern. However, maintaining the resolution in slot direction will provide a better edge response and a more homogeneous noise texture at the cost of an inhomogeneous image resolution. Conclusion We have demonstrated the feasibility of the slot-scanning technique using a twin-robotic x-ray system. Even the fastest scanning mode (27 cm/s) yields image resolution on a level that is sufficient for typical orthopedic examinations in terms of musculoskeletal (MSK) measurements. Moreover, it could be shown that the application of specifically designed target MTFs on two-dimensional x-ray images is feasible.
CRIS-Team WoS Importer:2019-11-05

Home Assistance System for Elderly People - Ott, Stefan; Spiegl, Werner; Soutschek, Stefan; Maier, Andreas; Steidl, Stefan; Nöth, Elmar (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

HOOREX: Higher Order Optimizers for 3D Recovery from X-Ray Images - Shetty, Karthik; Birkhold, Annette; Egger, Bernhard; Jaganathan, Srikrishna; Strobel, Norbert; Kowarschik, Markus; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024)
We propose a method to address the challenge of generating a 3D digital twin of a patient during an X-ray guided medical procedure from a single 2D X-ray projection image, a problem that is inherently ill-posed. To tackle this issue, we aim to infer the parameters of Bones, Organs and Skin Shape (BOSS) model, a deformable human shape and pose model. There are currently two main approaches for model-based estimation. Optimization-based methods try to iteratively fit a body model to 2D measurements, they produce accurate 2D alignments but are slow and sensitive to initialization. On the other hand, regression-based methods use neural networks to estimate the model parameters directly, resulting in faster predictions but often with misalignments. Our approach combines the benefits of both techniques by implementing a fully differentiable paradigm through the use of higher-order optimizers that only require the Jacobian, which can be determined implicitly. The network was trained on synthetic CT and real CBCT image data, ensuring view independence. We demonstrate the potential clinical applicability of our method by validating it on multiple datasets covering diverse anatomical regions, and achieving an error of 27.98 mm.
CRIS-Team Scopus Importer:2023-12-15

How Big is Big Enough?: A Large-Scale Histological Dataset of Mitotic Figures - Bertram, Christof A.; Aubreville, Marc; Marzahl, Christian; Maier, Andreas; Klopfleisch, Robert (Bildverarbeitung für die Medizin 2020, 2020)
Quantification of mitotic figures (MF) within the tumor areas of highest mi- totic density is the most important prognostic parameter for outcome assessment of many tumor types. However, high intra- and inter-rater variability results from difficulties in individual MF identification and region of interest (ROI) se- lection due to uneven MF distribution. Deep learning-based algorithms for MF detection and ROI selection are very promising methods to overcome these lim- itations. As of today, few datasets of human mammary carcinoma are available. They provide labels only in small image sections of the whole slide image (WSI) and include up to 1,552 MF annotations. Our research group has developed a large-scale, open access dataset with annotations for MF in 32 cases of canine cutaneous mast cell tumors. Entire WSI were completely labeled by two pathologists resulting in 44,800 MF anno- tations. Of those, 5.5% were initially missed by expert WSI screening and added through a deep learning-based pipeline for identification of potential candidates. For algorithmic validation, we used a two-stage approach (RetinaNet followed by cell classificator), which yielded a F1 score of 0.820. Through the algorith- m-aided completion of the dataset we were able to increase the F1 score by 3.4 percentage points. Influence of the size of the dataset was assessed by stepwise reduction of the number of WSI and size (in high power fields, HPF) of the image sections used for training. With the number of included images, the F1 score moderately increased (3 WSI: 0.772; 6 WSI: 0.804; 12 WSI: 0.817; 21 WSI: 0.820). The size of the tumor area in training (ROI selected by an expert) had significant effects on the F1 score (5 HPF: 0.583; 10 HPF: 0.676; 50 HPF: 0.770; complete WSI: 0.820), which was determined in entire WSI of the test set. We emphasize the benefit of appropriate dataset size and complete WSI labeling.
Keywords: mitotic figures; tumor diagnostics; open data; big data; canine cutaneous mast cell tumor

How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography - Denzinger, Felix; Wels, Michael; Breininger, Katharina; Taubmann, Oliver; Mühlberg, Alexander; Allmendinger, Thomas; Gülsün, Mehmet A.; Schöbinger, Max; André, Florian; Buss, Sebastian J.; Görich, Johannes; Sühling, Michael; et al. (Scientific Reports, 2023)
Recently, algorithms capable of assessing the severity of Coronary Artery Disease (CAD) in form of the Coronary Artery Disease-Reporting and Data System (CAD-RADS) grade from Coronary Computed Tomography Angiography (CCTA) scans using Deep Learning (DL) were proposed. Before considering to apply these algorithms in clinical practice, their robustness regarding different commonly used Computed Tomography (CT)-specific image formation parameters-including denoising strength, slab combination, and reconstruction kernel-needs to be evaluated. For this study, we reconstructed a data set of 500 patient CCTA scans under seven image formation parameter configurations. We select one default configuration and evaluate how varying individual parameters impacts the performance and stability of a typical algorithm for automated CAD assessment from CCTA. This algorithm consists of multiple preprocessing and a DL prediction step. We evaluate the influence of the parameter changes on the entire pipeline and additionally on only the DL step by propagating the centerline extraction results of the default configuration to all others. We consider the standard deviation of the CAD severity prediction grade difference between the default and variation configurations to assess the stability w.r.t. parameter changes. For the full pipeline we observe slight instability (± 0.226 CAD-RADS) for all variations. Predictions are more stable with centerlines propagated from the default to the variation configurations (± 0.122 CAD-RADS), especially for differing denoising strengths (± 0.046 CAD-RADS). However, stacking slabs with sharp boundaries instead of mixing slabs in overlapping regions (called true stack ± 0.313 CAD-RADS) and increasing the sharpness of the reconstruction kernel (± 0.150 CAD-RADS) leads to unstable predictions. Regarding the clinically relevant tasks of excluding CAD (called rule-out; AUC default 0.957, min 0.937) and excluding obstructive CAD (called hold-out; AUC default 0.971, min 0.964) the performance remains on a high level for all variations. Concluding, an influence of reconstruction parameters on the predictions is observed. Especially, scans reconstructed with the true stack parameter need to be treated with caution when using a DL-based method. Also, reconstruction kernels which are underrepresented in the training data increase the prediction uncertainty.
CRIS-Team Scopus Importer:2023-02-24

How to Get the Most Out of U-Net for Glacier Calving Front Segmentation - Periyasamy, Maniraman; Davari, Amirabbas; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022)
The melting of ice sheets and glaciers is one of the main contributors to global sea-level rise. Hence, continuous monitoring of glacier changes and in particular the mapping of positional changes of their calving front is of significant importance. This delineation process, in general, has been carried out manually, which is time-consuming and not feasible for the abundance of available data within the past decade. Automatic delineation of the glacier fronts in synthetic aperture radar (SAR) images can be performed using deep learning-based U-Net models. This article aims to study and survey the components of a U-Net model and optimize the model to get the most out of U-Net for glacier (calving front) segmentation. We trained the U-Net to segment the SAR images of Sjogren-Inlet and Dinsmoore-Bombardier-Edgworth glacier systems on the Antarctica Peninsula region taken by ERS-1/2, Envisat, RadarSAT-1, ALOS, TerraSARX, and TanDEM-X missions. The U-Net model was optimized in six aspects. The first two aspects, namely data pre-processing and data augmentation, enhanced the representation of information in the image. The remaining four aspects optimized the feature extraction of U-Net by finding the best-suited loss function, bottleneck, normalization technique, and dropouts for the glacier segmentation task. The optimized U-Net model achieves a dice coefficient score of 0.9378 with a 20% improvement over the baseline U-Net model, which achieved a score of 0.7377. This segmentation result is further post-processed to delineate the calving front. The optimized U-Net model shows 23% improvement in the glacier front delineation compared to the baseline model.
Created from Fastlane, Scopus look-up
Keywords: Antarctica; Deep learning; Glacier calving front segmentation; Ice; Image segmentation; Optimized U-Net; Radar polarimetry; Semantic Segmentation; Synthetic aperture radar; Training

Hybrid OCT-OCTA Vessel Visualization for Projection-Free Display of the Intermediate and Deep Retinal Plexuses - Ploner, Stefan; Moult, Eric M.; Schottenhamml, Julia; Husvogt, Lennart; Lu, Chen D.; Rebhun, Carl B.; Alibhai, A. Yasin; Duker, Jay S.; Waheed, Nadia K.; Maier, Andreas; Fujimoto, James G. (Investigative Ophthalmology & Visual Science, 2017)
Purpose : In optical coherence tomography angiography (OCTA), projection artifacts (also shadowing artifacts or decorrelation tails) cause superficial retinal vasculature to appear in the segmented OCTA images of the intermediate and deep retinal plexuses. The projections of these larger superficial vessels obfuscate the unique vascular patterning of the deeper layers. Several algorithms have been proposed to remove these shadows. However, by removing the projected vessels these approaches also decrease the contrast of, or fully eliminate, the underlying vasculature, thereby introducing discontinuities. Thus, in effect, one artifact is replaced with another. The purpose of this study is to develop a projection artifact removal scheme that overcomes this limitation and fully preserves the intermediate/deep retinal vasculature. Methods : Amplitude decorrelation based OCTA data was collected using a 1050nm swept source OCT system. A hybrid OCT-OCTA vessel visualization scheme (described in Figure 1) was developed. The key advantage of our proposed scheme is that the OCT signal is used to adaptively remove the OCTA signal only in the intercapillary regions. Results : The hybrid OCT-OCTA approach reduces projection artifacts to a negligible level in the intermediate and deep plexuses without introducing vessel discontinuity artifacts (Figure 2). Conclusions : Hybrid OCT-OCTA vessel visualization is a promising approach to visualize the unique patterning of the intermediate and deep plexuses, and is likely to be particularly important in diseases such as diabetic retinopathy where it is desirable to separately analyze the retinal plexuse
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.hybrid
Keywords: oct; octa; oct angiography; shadow artifact removal; projection artifact removal

Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings - Davari, Amirabbas; Sakaltras, Nikolaos; Haeberle, Armin; Vesal, Sulaiman; Christlein, Vincent; Maier, Andreas; Riess, Christian (International Conference on Image Processing (ICIP), 2018)
Old master drawings were mostly created step by step in several layers using different materials. To art historians and restorers, examination of these layers brings various insights into the artistic work process and helps to answer questions about the object, its attribution and its authenticity. However, these layers typically overlap and are oftentimes difficult to differentiate with the unaided eye. For example, a common layer combination is red chalk under ink. In this work, we propose an image processing pipeline that operates on hyperspectral images to separate such layers. In particular, we propose to use two descriptors in hyperspectral historical document analysis, namely hyper-hue and extended multi-attribute profile (EMAP). We show that hyperspectral images enable better layer separation than RGB images, and that spectral focus stacking is an important preprocessing step towards that goal. Our comparative results with other features underline the efficacy of the three proposed improvements.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.hyperh_9
Keywords: EMAP; Hyper-Hue; Layer Separation; Old Master Drawing; Spectral Focus Stacking

Hypernasality in Speech of Children with Cleft Lip and Palate: Automatic Evaluation - Maier, Andreas; Schuster, Maria; Haderlein, Tino; Nöth, Elmar (Proceedings of the 8th International Seminar on Speech Production 2008, 2008)

ICC++: Explainable Feature Learning for Art History using Image Compositions - Madhu, Prathmesh; Marquart, Tilman; Kosti, Ronak Vijaypal; Suckow, Dirk; Bell, Peter; Maier, Andreas; Christlein, Vincent (Pattern Recognition, 2022)
Image compositions are helpful in the study of image structures and assist in discovering the semantics of the underlying scene portrayed across art forms and styles. With the digitization of artworks in recent years, thousands of images of a particular scene or narrative could potentially be linked together. However, manually linking this data with consistent objectiveness can be a highly challenging and time-consuming task. In this work, we present a novel approach called Image Composition Canvas (ICC) to compare and retrieve images having similar compositional elements. ICC is an improvement over ICC, specializing in generating low and high-level features (compositional elements) motivated by Max Imdahl’s work. To this end, we present a rigorous quantitative and qualitative comparison of our approach with traditional and state-of-the-art (SOTA) methods showing that our proposed method outperforms all of them. In combination with deep features, our method outperforms the best deep learning-based method, opening the research direction for explainable machine learning for digital humanities. We will release the code and the data post-publication.<br

ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents - Christlein, Vincent; Nikolaou, Angelos; Seuret, Mathias; Stutzmann, Dominique; Maier, Andreas (2019 International Conference on Document Analysis and Recognition (ICDAR), 2019)
This competition investigates the performance of large-scale retrieval of historical document images based on writing style. Based on large image data sets provided by cultural heritage institutions and digital libraries, providing a total of 20 000 document images representing about 10 000 writers, divided in three types: writers of (i) manuscript books, (ii) letters, (iii) charters and legal documents. We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as writer retrieval. The most teams submitted traditional methods not using deep learning techniques. The competition results show that a combination of methods is outperforming single methods. Furthermore, letters are much more difficult to retrieve than manuscripts.

ICDAR 2021 Competition on Historical Document Classification - Seuret, Mathias; Nicolaou, Anguelos; Rodríguez-Salas, Dalia; Weichselbaumer, Nikolaus; Stutzmann, Dominique; Mayr, Martin; Maier, Andreas; Christlein, Vincent (Document Analysis and Recognition – ICDAR 2021, 2021)
This competition investigated the performance of historical document classification. The analysis of historical documents is a difficult challenge commonly solved by trained humanists. We provided three different classification tasks, which can be solved individually or jointly: font group/script type, location, date. The document images are provided by several institutions and are taken from handwritten and printed books as well as from charters. In contrast to previous competitions, all participants relied upon Deep Learning based approaches. Nevertheless, we saw a great performance variety of the different submitted systems. The easiest task seemed to be font group recognition while the script type classification and location classification were more challenging. In the dating task, the best system achieved a mean absolute error of about 22 years.
CRIS-Team Scopus Importer:2021-10-01
Keywords: Dating; Document analysis; Document classification; Historical document images

ICFHR 2020 Competition on Image Retrieval for Historical Handwritten Fragments - Seuret, Mathias; Nicolaou, Anguelos; Stutzmann, Dominique; Maier, Andreas; Christlein, Vincent (17th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2020)
This competition succeeds upon a line of competitions for writer and style analysis of historical document images. In particular, we investigate the performance of large-scale retrieval of historical document fragments in terms of style and writer identification. The analysis of historic fragments is a difficult challenge commonly solved by trained humanists. In comparison to previous competitions, we make the results more meaningful by addressing the issue of sample granularity and moving from writer to page fragment retrieval. The two approaches, style and author identification, provide information on what kind of information each method makes better use of and indirectly contribute to the interpretability of the participating method. Therefore, we created a large dataset consisting of more than 120 000 fragments. Although the most teams submitted methods based on convolutional neural networks, the winning entry achieves an mAP below 40 %.<br
Keywords: Shape; Training; Feature extraction; Measurement; Text analysis; Image retrieval; Image edge detection

Ideal Seed Point Location Approximation for GrowCut Interactive Image Segmentation - Amrehn, Mario; Strumia, Maddalena; Steidl, Stefan; Horz, Tim; Kowarschik, Markus; Maier, Andreas (Bildverarbeitung für die Medizin 2018, 2018)
The C-arm CT X-ray acquisition process is a common modality in medical imaging. After image formation, anatomical structures can be extracted via segmentation. Interactive segmentation methods bear the advantage of a dynamically adjustable trade-off between time and achieved segmentation quality for the object of interest w. r. t. fully automated approaches. The segmentation’s quality can be measured in terms of the Dice coefficient with the ground truth segmentation image. A user’s interaction traditionally consist of drawing pictorial hints on an overlay image to the acquired image data via a graphical user interface (UI). The quality of a segmentation utilizing a set of drawn seeds varies depending on the location of the seed points in the image. In this paper, we (1) investigate the influence of seed point location on segmentation quality and (2) propose an approximation framework for ideal seed placements utilizing an extension of the well established GrowCut segmentation algorithm and (3) introduce a user interface for the utilization of the suggested seed point locations. An extensive evaluation of the predictive power of seed importance is conducted from hepatic lesion input images. As a result, our approach suggests seed points with a median of 72.5% of the ideal seed points’ associated Dice scores, which is an increase of 8.4% points to sampling the seed location at random.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.ideals_4

Identification of High-risk Metastases in MRI Follow-up after Stereotaxic by using Radiomics and Delta Radiomics - Sommer, Phillip; Huang, Yixing; Fischer, Stefan; Srinivasan, Aishwarya Lakshmi; Dörfler, Arnd; Schmidt, M.; Frey, Benjamin; Maier, Andreas; Fietkau, Rainer; Bert, Christoph; Putz, Florian (STRAHLENTHERAPIE UND ONKOLOGIE, 2022)
CRIS-Team WoS Importer:2022-06-03

IJCARS: BVM 2019 special issue - Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (International Journal of Computer Assisted Radiology and Surgery, 2019)
CRIS-Team Scopus Importer:2019-11-19

IJCARS: BVM 2021 special issue - Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (International Journal of Computer Assisted Radiology and Surgery, 2021)
CRIS-Team Scopus Importer:2021-11-26

Image artefact propagation in motion estimation and reconstruction in interventional cardiac C-arm CT - Müller, Kerstin; Maier, Andreas; Schwemmer, Chris; Lauritsch, Günter; De Buck, Stijn; Wielandts, Jean-Yves; Hornegger, Joachim; Fahrig, Rebecca (Physics in Medicine and Biology, 2014)
The acquisition of data for cardiac imaging using a C-arm computed tomography system requires several seconds and multiple heartbeats. Hence, incorporation of motion correction in the reconstruction step may improve the resulting image quality. Cardiac motion can be estimated by deformable three-dimensional (3D)/3D registration performed on initial 3D images of different heart phases. This motion information can be used for a motion-compensated reconstruction allowing the use of all acquired data for image reconstruction. However, the result of the registration procedure and hence the estimated deformations are influenced by the quality of the initial 3D images. In this paper, the sensitivity of the 3D/3D registration step to the image quality of the initial images is studied. Different reconstruction algorithms are evaluated for a recently proposed cardiac C-arm CT acquisition protocol. The initial 3D images are all based on retrospective electrocardiogram (ECG)-gated data. ECG-gating of data from a single C-arm rotation provides only a few projections per heart phase for image reconstruction. This view sparsity leads to prominent streak artefacts and a poor signal to noise ratio. Five different initial image reconstructions are evaluated: (1) cone beam filtered-backprojection (FDK), (2) cone beam filtered-backprojection and an additional bilateral filter (FFDK), (3) removal of the shadow of dense objects (catheter, pacing electrode, etc) before reconstruction with a cone beam filtered-backprojection (cathFDK), (4) removal of the shadow of dense objects before reconstruction with a cone beam filtered-backprojection and a bilateral filter (cathFFDK). The last method (5) is an iterative few-view reconstruction (FV), the prior image constrained compressed sensing combined with the improved total variation algorithm. All reconstructions are investigated with respect to the final motion-compensated reconstruction quality. The algorithms were tested on a mathematical phantom data set with and without a catheter and on two porcine models using qualitative and quantitative measures. The quantitative results of the phantom experiments show that if no dense object is present within the scan field of view, the quality of the FDK initial images is sufficient for motion estimation via 3D/3D registration. When a catheter or pacing electrode is present, the shadow of these objects needs to be removed before the initial image reconstruction. An additional bilateral filter shows no major improvement with respect to the final motion-compensated reconstruction quality. The results with respect to image quality of the cathFDK, cathFFDK and FV images are comparable. In conclusion, in terms of computational complexity, the algorithm of choice is the cathFDK algorithm © 2014 Institute of Physics and Engineering in Medicine.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.imagea_5
Keywords: C-arm CT; cardiac imaging; dynamic imaging; motion compensation; motion-compensated tomographic reconstruction

Image Artifact Influence on Motion Compensated Tomographic Reconstruction in Cardiac C-arm CT - Müller, Kerstin; Schwemmer, Chris; Lauritsch, Günter; Rohkohl, Christopher; Maier, Andreas; Heidbüchel, Hein; De Buck, Stijn; Nuyens, Dieter; Kyriakou, Yiannis; Köhler, Christopher; Fahrig, Rebecca; Hornegger, Joachim (Proceedings of the 12th Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013)

Image-based Compensation for Involuntary Motion in Weight-bearing C-arm Cone-beam CT Scanning of Knees - Unberath, Mathias; Choi, Jang-Hwan; Berger, Martin; Maier, Andreas; Fahrig, Rebecca (Proc. SPIE Medical Imaging 2015, 2015)
We previously introduced four fiducial marker-based strategies to compensate for involuntary knee-joint motion during weight-bearing C-arm CT scanning of the lower body. 2D methods showed significant reduction of motion-related artifacts, but 3D methods worked best. However, previous methods led to increased examination times and patient discomfort caused by the marker attachment process. Moreover, sub-optimal marker placement may lead to decreased marker detectability and therefore unstable motion estimates. In order to reduce overall patient discomfort, we developed a new image-based 2D projection shifting method. A C-arm cone-beam CT system was used to acquire projection images of five healthy volunteers at various flexion angles. Projection matrices for the horizontal scanning trajectory were calibrated using the Siemens standard PDS-2 phantom. The initial reconstruction was forward projected using maximum-intensity projections (MIP), yielding an estimate of a static scan. This estimate was then used to obtain the 2D projection shifts via registration. For the scan with the most motion, the proposed method reproduced the marker-based results with a mean error of 2.90 mm +/-1.43 mm (compared to a mean error of 4.10 mm +/-3.03 mm in the uncorrected case). Bone contour surrounding modeling clay layer was improved. The proposed method is a first step towards automatic image-based, marker-free motion-compensation.
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.imageb
Keywords: GRK-1773

Image-based Detection of MRI Hardware Failures - Jain, Bhavya; Kuhnert, Nadine; deOliveira, Andre; Maier, Andreas (2019)

Image Preprocessing Pipeline for Bright-Field Miniature Live Cell Microscopy Prototypes - Schöll, Simon; Mualla, Firas; Sommerfeldt, Björn; Steidl, Stefan; Maier, Andreas (Springer Proceedings in Physics 154, Proceedings of InterM, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.imagep

Image Quality Analysis of Limited Angle Tomography Using the Shift-Variant Data Loss Model - Huang, Yixing; Lauritsch, Günter; Amrehn, Mario; Taubmann, Oliver; Haase, Viktor; Stromer, Daniel; Huang, Xiaolin; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.imageq_5

Image Quality Assessment for Extended-Volume C-arm CT Using a Multi-Turn Reverse Helix - Yu, Zhicong; Maier, Andreas; Lauritsch, Günter; Vogt, Florian; Schönborn, Manfred; Köhler, Christoph; Hornegger, Joachim; Noo, Frédéric (Proc. of IEEE NUCLEAR SCIENCE SYMPOSIUM & MEDICAL IMAGING CONFERENCE, 2014)
UnivIS-Import:2017-12-20:Pub.2014.tech.IMMD.IMMD5.imageq

Image Quality Assessment for Rigid Motion Compensation - Preuhs, Alexander; Manhart, Michael; Roser, Philipp; Stimpel, Bernhard; Syben-Leisner, Christopher; Psychogios, Marios; Kowarschik, Markus; Maier, Andreas (MedNeurips, 2019)

Image Reconstruction from Real Dual Energy CT Data Using an Analytical Energy Response Model - Haase, Viktor; Hahn, Katharina; Schondube, Harald; Stierstorfer, Karl; Maier, Andreas; Noo, Frederic (2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors, RTSD 2022, 2021)
Dual energy computed tomography (DECT) enables access to the energy-dependence of the linear attenuation coefficient of X-rays. Such information can be used to perform material decomposition; and to subsequently compute mono-energetic images for contrast improvement and reduction of beam hardening artifacts compared to images obtained from single energy computed tomography (CT) systems. Last year at IEEE MIC, we introduced an analytical energy response model with embedded patient-specific scatter correction and demonstrated that this model performs well in the context of single material beam hardening correction. Here, the value of the model is investigated for material decomposition of real dual energy CT data. Specifically, we demonstrate that the model enables robust material decomposition for the ACR phantom from a state-of-the-art diagnostic CT scanner, under the assumption that the two scans are acquired in the same geometry.
CRIS-Team Scopus Importer:2023-03-07

Image Reconstruction with Variational Networks: Application to Synchrotron Radiation Imaging - Rashed, Essam A.; Kudo, Hiroyuki; Maier, Andreas (2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings, 2018)
In computed tomography, image reconstructed from limited projection data is subject to strong noise and artifacts. Reducing the number of projection views in synchrotron radiation imaging has a great benefits in decreasing the computation cost for image formation. Moreover, it also prevents the damage of biological specimen caused by the x-ray radiation exposure. In this paper, a new procedure for image reconstruction from limited projection views with the assist of deep learning approach is proposed. A deep learning approach is employed to estimate an initial guess of the tomographic image followed by a fast row-action reconstruction to guarantee the restoration of any pattern missed in the first stage. Most of previous attempts that use deep learning in image reconstruction are implemented as a post-processing techniques applied on the reconstructed image. This may lead to anomalies in the final result due to the lack of consistency between the acquired projection data and the reconstructed image. The proposed procedure solves this problem by keeping the data consistency up to the final stage of image formation. This would reduce the possibility of losing image abnormal structures due to insufficient training in deep learning. Experimental results using synchrotron radiation data demonstrate the usefulness of the proposed framework.
CRIS-Team Scopus Importer:2022-06-05

Image Registration for the Alignment of Digitized Historical Documents - Davari, Amirabbas; Lindenberger, Tobias; Haeberle, Armin; Christlein, Vincent; Maier, Andreas; Riess, Christian (2017)

Imitation learning network for fundus image registration using a divide-and-conquer approach - Bayer, Siming; Zhong, Xia; Fu, Weilin; Ravikumar, Nishant; Maier, Andreas (Informatik aktuell, 2020)
Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of the images (i.e. contrast, luminosity) and the anatomical changes of the retina, the registration of fundus images remains a challenging task. Recently, several deep learning approaches have been proposed to register fundus images in an end-to-end fashion, achieving remarkable results. However, the results are diffcult to interpret and analyze. In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution. We follow a divide-and-conquer approach to improve the interpretability of the proposed network, and analyze both the influence of the input image and the hyperparameters on the registration result. The results show that the proposed registration network reduces the initial target registration error up to 95%.
CRIS-Team Scopus Importer:2020-04-21

Immersive Painting - Soutschek, Stefan; Hönig, Florian Thomas; Maier, Andreas; Steidl, Stefan; Stürmer, Michael; Erzigkeit, Helmut; Hornegger, Joachim; Kornhuber, Johannes (Proceedings of the ArtsIT 2009, 2009)

Impact of acquisition area on deep-learning-based glaucoma detection in OCTA - Schottenhamml, Julia; Würfl, Tobias; Ploner, Stefan; Husvogt, Lennart; Maier, Andreas; Hohberger, Bettina; Mardin, Christian Y. (Investigative Ophthalmology & Visual Science, 2023)

Impact of Including Pathological Speech in Pre-training on Pathology Detection - Weise, Tobias; Maier, Andreas; Demir, Kubilay; Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Heismann, Björn; Nöth, Elmar; Schuster, Maria Elke; Yang, Seung Hee (Springer Science and Business Media Deutschland GmbH, 2023)
Keywords: transformer; wav2vec2.0; self-supervised learning; pathological speech

Impact of Including Pathological Speech in Pre-training on Pathology Detection - Weise, Tobias; Maier, Andreas; Demir, Kubilay; Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Heismann, Björn; Nöth, Elmar; Schuster, Maria; Yang, Seung Hee (Text, Speech, and Dialogue, 2023)
Transfer learning has achieved state-of-the-art performance across many different areas, requiring magnitudes less labeled data compared to traditional methods. Pre-trained weights are learned in a self-supervised way on large amounts of unlabeled data, which are fine-tuned for the desired downstream task using labeled data. An example of this in the speech domain is the wav2vec2.0 framework, which was originally designed for automatic speech recognition (ASR) but can also be fine-tuned for general sequence classification tasks. This paper analyses the effects of including pathological speech during the pre-training of wav2vec2.0, where quantized speech representations are learned, on the performance of a fine-tuned pathology detection task. We show that this architecture can be successfully fine-tuned for cleft lip and palate (CLP) detection, where the best-performing model yields an F1-score of when pre-trained on healthy speech only. Our experiments show, that including pathological speech during pre-training drastically degrades the performance on detection of the same pathology for which it was fine-tuned. The worst-performing model was pre-trained exclusively on CLP speech, resulting in an F1-score of . Whilst performed experiments only focus on CLP, the magnitude of the results suggest, that other pathologies will also follow this tren
Keywords: wav2vec2.0; self-supervised learning; transformer; pathological speech

Impact of intraepithelial capillary loops and atypical vessels in confocal laser endomicroscopy for the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma - Sievert, Matti; Eckstein, Markus; Mantsopoulos, Konstantinos; Mueller, Sarina K.; Stelzle, Florian; Aubreville, Marc; Oetter, Nicolai; Maier, Andreas; Iro, Heinrich; Goncalves, Miguel (European Archives of Oto-Rhino-Laryngology, 2021)
Purpose Confocal laser endomicroscopy (CLE) allows surface imaging of the laryngeal and pharyngeal mucosa in vivo at a thousand-fold magnification. This study aims to compare irregular blood vessels and intraepithelial capillary loops in healthy mucosa and squamous cell carcinoma (SCC) via CLE.
CRIS-Team WoS Importer:2021-07-23

Impact of Pathologies on Automatic Age Estimation - Schwinn, Leo; Haderlein, Tino; Nöth, Elmar; Maier, Andreas (Fortschritte der Akustik - DAGA 2019, 2019)
Automatic Age estimation using speech is a challenging problem. Among other things, the many influences that lead to a change in the voice make it difficult to estimate the exact age. Examples are the microphone used, the distance to this microphone or the sex of the speaker. Stable results can be achieved by extracting Mel-Frequency Cepstral Coefficients (MFCCs) from the speech signal and processing them into i‑vectors, classification and regression tools such as Support Vector Regression (SVR) can then be used for the age estimation from these features. An additional factor influencing the age estimation are speech or voice disorders of a speaker. Rarely is this impact assessed, resulting in systems that are not tailored to the needs of pathological speakers like Siri or Amazon echo. Another example are companies which use automatic age estimation to forward calls to persons of the same age as the caller. These systems are also adapted to the characteristics of healthy speakers. This paper examines the impacts of such pathologies on age estimation and assess the possibility of reducing their influence by using the Word Accuracy (WA) of the speakers. This measure gives information about the intelligibility of a speaker and should help to reduce the variance of the extracted features. The features with low variance should provide more stable results in the age estimation. To achieve this, we compare the results of an age estimation for four different groups of speakers. All speakers at once, only pathological speakers, only healthy speakers and training the SVR with healthy speakers while testing with pathological speakers. Each of these groups are further separated by the WA of the speaker
UnivIS-Import:2019-08-15:Pub.2019.tech.IMMD.IMMD5.impact
Keywords: age estimation; speech recognition; speaker characteristics

Impact of the non-negativity constraint in model-based iterative reconstruction from CT data - Haase, Viktor; Hahn, Katharina; Schöndube, Harald; Stierstorfer, Karl; Maier, Andreas; Noo, Frédéric (Medical Physics, 2019)
Model-based iterative reconstruction is a promising approach to achieve dose reduction without affecting image quality in diagnostic x-ray computed tomography (CT). In the problem formulation, it is common to enforce non-negative values to accommodate the physical non-negativity of x-ray attenuation. Using this a priori information is believed to be beneficial in terms of image quality and convergence speed. However, enforcing non-negativity imposes limitations on the problem formulation and the choice of optimization algorithm. For these reasons, it is critical to understand the value of the non-negativity constraint. In this work, we present an investigation that sheds light on the impact of this constraint. We primarily focus our investigation on the examination of properties of the converged solution. To avoid any possibly confounding bias, the reconstructions are all performed using a provably converging algorithm started from a zero volume. To keep the computational cost manageable, an axial CT scanning geometry with narrow collimation is employed. The investigation is divided into five experimental studies that challenge the non-negativity constraint in various ways, including noise, beam hardening, parametric choices, truncation, and photon starvation. These studies are complemented by a sixth one that examines the effect of using ordered subsets to obtain a satisfactory approximate result within 50 iterations. All studies are based on real data, which come from three phantom scans and one clinical patient scan. The reconstructions with and without the non-negativity constraint are compared in terms of image similarity and convergence speed. In select cases, the image similarity evaluation is augmented with quantitative image quality metrics such as the noise power spectrum and closeness to a known ground truth. For cases with moderate inconsistencies in the data, associated with noise and bone-induced beam hardening, our results show that the non-negativity constraint offers little benefit. By varying the regularization parameters in one of the studies, we observed that sufficient edge-preserving regularization tends to dilute the value of the constraint. For cases with strong data inconsistencies, the results are mixed: the constraint can be both beneficial and deleterious; in either case, however, the difference between using the constraint or not is small relative to the overall level of error in the image. The results with ordered subsets are encouraging in that they show similar observations. In terms of convergence speed, we only observed one major effect, in the study with data truncation; this effect favored the use of the constraint, but had no impact on our ability to obtain the converged solution without constraint. Our results did not highlight the non-negativity constraint as being strongly beneficial for diagnostic CT imaging. Altogether, we thus conclude that in some imaging scenarios, the non-negativity constraint could be disregarded to simplify the optimization problem or to adopt other forward projection models that require complex optimization machinery to be used together with non-negativity.
Keywords: model-based iterative reconstruction; non-negativity constraint; penalized weighted least-squares; positivity constraint; x-ray computed tomography

Implant-based CT estimation towards adaptive breast brachytherapy - Dürrbeck, Christopher; Pflaum, L.; Schulz, M.; Kallis, Karoline; Geimer, Tobias; Abu-Hossin, N.; Strnad, Vratislav; Maier, Andreas; Fietkau, Rainer; Bert, Christoph (RADIOTHERAPY AND ONCOLOGY, 2021)
CRIS-Team WoS Importer:2021-06-18

Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses - Ellmann, Stephan; Wenkel, Evelyn; Dietzel, Matthias; Bielowski, Christian; Vesal, Sulaiman; Maier, Andreas; Hammon, Matthias; Janka, Rolf Matthias; Fasching, Peter; Beckmann, Matthias; Schulz-Wendtland, Rüdiger; Uder, Michael; et al. (PLoS ONE, 2020)
We investigated whether the integration of machine learning (ML) into MRI interpretation can provide accurate decision rules for the management of suspicious breast masses. A total of 173 consecutive patients with suspicious breast masses upon complementary assessment (BI-RADS IV/V: n = 100/76) received standardized breast MRI prior to histological verification. MRI findings were independently assessed by two observers (R1/R2: 5 years of experience/no experience in breast MRI) using six (semi-)quantitative imaging parameters. Interobserver variability was studied by ICC (intraclass correlation coefficient). A polynomial kernel function support vector machine was trained to differentiate between benign and malignant lesions based on the six imaging parameters and patient age. Ten-fold cross-validation was applied to prevent overfitting. Overall diagnostic accuracy and decision rules (rule-out criteria) to accurately exclude malignancy were evaluated. Results were integrated into a web application and published online. Malignant lesions were present in 107 patients (60.8%). Imaging features showed excellent interobserver variability (ICC: 0.81–0.98) with variable diagnostic accuracy (AUC: 0.65–0.82). Overall performance of the ML algorithm was high (AUC = 90.1%; BI-RADS IV: AUC = 91.6%). The ML algorithm provided decision rules to accurately rule-out malignancy with a false negative rate <1% in 31.3% of the BI-RADS IV cases. Thus, integration of ML into MRI interpretation can provide objective and accurate decision rules for the management of suspicious breast masses, and could help to reduce the number of potentially unnecessary biopsies.<br

Implications of Experiment Set-Ups for Residential Water End-Use Classification - Gourmelon, Nora; Bayer, Siming; Mayle, Michael; Bach, Guy; Bebber, Christian; Munck, Christophe; Sosna, Christoph; Maier, Andreas (5th International Electronic Conference on Water Sciences, 2020)
With the increased population in urban areas worldwide, the security of water supply is gaining in importance. Water scarcity accelerated by climate change poses additional stress to water supply infrastructures. Water consumption data transmitted by smart water meters form the foundation of advanced data analysis, such as water end-use classification, with which the resilience of water supply can be improved. Especially with large amounts of high-resolution data, the accurate categorization of data from smart water meters into different end-uses such as toilets, showers or dishwashers is challenging and cannot be performed by humans. To this end, machine-learning (ML) approaches provide several benefits, such as real-time capability, scalability and generalizability. State-of-the-art methods to identify residential water end-uses include both unsupervised methods and supervised approaches. However, a comprehensive comparison of unsupervised and supervised techniques is still missing. In this study, we are aiming at a quantitative evaluation of various ML techniques for water end-use classification. Furthermore, we focus on deriving general implications on the setting and conduction of ML-based experiments for water end-use classification. For these purposes, a stochastic water consumption simulation tool with high capability to model the real-world water consumption pattern is applied to generate residential data. Subsequently, unsupervised clustering methods, such as dynamic time warping, k-means, DBSCAN, OPTICS and Hough transform, are compared to supervised methods based on SVM. The quantitative results demonstrate that supervised approaches are capable to classify common residential end-uses (toilet, shower, faucet, dishwasher, washing machine, bathtub and mixed water-uses) with accuracies up to 0.99, whereas unsupervised methods fail to detect those consumption categories. The major implications drawn from the quantitative results are two-fold: clustering is not suitable to separate end-use categories. Hence, accurate labels are essential for the end-use classification of water events, where crowdsourcing and citizen science approaches pose feasible solutions for this purpose
Keywords: end-use classification; smart water meter; machine learning

Implications of Experiment Set-Ups for Residential Water End-Use Classification - Gourmelon, Nora; Bayer, Siming; Mayle, Michael; Bach, Guy; Bebber, Christian; Munck, Christophe; Sosna, Christoph; Maier, Andreas (Water, 2021)
With an increasing need for secured water supply, a better understanding of the water consumption behavior is beneficial. This can be achieved through end-use classification, i.e., identifying end-uses such as toilets, showers or dishwashers from water consumption data. Previously, both supervised and unsupervised machine learning (ML) techniques are employed, demonstrating accurate classification results on particular datasets. However, a comprehensive comparison of ML techniques on a common dataset is still missing. Hence, in this study, we are aiming at a quantitative evaluation of various ML techniques on a common dataset. For this purpose, a stochastic water consumption simulation tool with high capability to model the real-world water consumption pattern is applied to generate residential data. Subsequently, unsupervised clustering methods, such as dynamic time warping, k-means, DBSCAN, OPTICS and Hough transform, are compared to supervised methods based on SVM. The quantitative results demonstrate that supervised approaches are capable to classify common residential end-uses (toilet, shower, faucet, dishwasher, washing machine, bathtub and mixed water-uses) with accuracies up to 0.99, whereas unsupervised methods fail to detect those consumption categories. In conclusion, clustering techniques alone are not suitable to separate end-use categories fully automatically. Hence, accurate labels are essential for the end-use classification of water events, where crowdsourcing and citizen science approaches pose feasible solutions for this purpos
Keywords: end-use classification; smart water meter; machine learning; residential water

Improved cardiac T1 mapping accuracy and precision with a new hybrid MOLLI and SASHA technique: MOSHA - Sohani, Majid; van der Geest, Rob J.; Maier, Andreas; Powell, Andrew J.; Moghari, Mehdi H. (Magnetic Resonance Imaging, 2022)
Purpose: To develop and validate a new myocardial T1 mapping sequence (MOSHA) which is based on a combination of the modified Look-Locker inversion recovery (MOLLI) and the saturation recovery single-shot acquisition (SASHA) sequences. Methods: Prior studies have shown that myocardial T1 mapping by SASHA is more accurate but less precise than MOLLI. A new myocardial T1 mapping technique (MOSHA) based on single-shot acquisitions is developed by combining the MOLLI and SASHA sequences. Phantom and patient studies on 15 patients (9 males, median age 21 years) were performed to validate and compare MOSHA with the MOLLI and SASHA sequences in terms of accuracy and precision. Results: In the phantom study, MOSHA was as accurate as SASHA (P-value = 0.88) and as precise as MOLLI (P-value = 0.59). Similar trends were observed in the patient study. Compared to SASHA, MOSHA accuracy was comparable for blood pre-contrast (P-value≥0.10) and post-contrast (P-value≥0.70), and for myocardium pre-contrast (P-value = 0.70) and post-contrast (P-value = 0.09). Compared to MOLLI, MOSHA precision was lower for blood pre-contrast (P-value<0.01) and higher for blood post-contrast (P-value≤0.01), and comparable for myocardium pre-contrast (P-value = 0.24) and post-contrast (P-value = 0.07). Synthetic Extracellular volume fraction (ECV) calculated by MOSHA was more precise than those of SASHA and MOLLI (P-value≤0.01). Conclusion: In phantom studies and patients, MOSHA has comparable accuracy as SASHA and nearly similar precision as MOLLI for T1 mapping. Precision of MOSHA was better than MOLLI and SASHA in synthetic ECV measurements. Therefore, it may be a superior choice in clinical practice for a precise and accurate calculation of T1 and ECV.
CRIS-Team Scopus Importer:2022-03-18
Keywords: Cardiac magnetic resonance; Extracellular volume fraction; Myocardial fibrosis; T; Tissue characterization

Improved HER2 Tumor Segmentation with Subtype Balancing Using Deep Generative Networks - Öttl, Mathias; Steenpaß, Jana; Rübner, Matthias; Geppert, Carol-Immanuel; Qiu, Jingna; Wilm, Frauke; Hartmann, Arndt; Beckmann, Matthias; Fasching, Peter; Maier, Andreas; Erber, Ramona; Breininger, Katharina (Proceedings - International Symposium on Biomedical Imaging, 2023)
Tumor segmentation in histopathology images is often complicated by its composition of different histological subtypes and class imbalance. Oversampling subtypes with low prevalence features is not a satisfactory solution since it eventually leads to overfitting. We propose to create synthetic images with semantically-conditioned deep generative networks and to combine subtype-balanced synthetic images with the original dataset to achieve better segmentation performance. We show the suitability of Generative Adversarial Networks (GANs) and especially diffusion models to create realistic images based on subtype-conditioning for the use case of HER2-stained histopathology. Additionally, we show the capability of diffusion models to conditionally inpaint HER2 tumor areas with modified subtypes. Combining the original dataset with the same amount of diffusion-generated images increased the tumor Dice score from 0.833 to 0.854 and almost halved the variance between the HER2 subtype recalls. These results create the basis for more reliable automatic HER2 analysis with lower performance variance between individual HER2 subtypes.
CRIS-Team Scopus Importer:2023-10-06
Keywords: Diffusion Models; Generative Models; HER2; Histopathology; Segmentation; Subtypes

Improved OCT motion correction in pathology using advanced eye motion modeling - Ploner, Stefan; Schottenhamml, Julia; Moult, Eric; Husvogt, Lennart; Waheed, Nadia K.; Duker, Jay S.; Fujimoto, James G.; Maier, Andreas (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2021)
CRIS-Team WoS Importer:2021-09-17

Improved Semi-Automatic Basket Catheter Reconstruction from two X-Ray Views - Zhong, Xia; Hoffmann, Matthias; Strobel, Norbert; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.improv_0

Improved trajectories in C-Arm computed tomography for non-circular fields of view - Herbst, Magdalena; Schebesch, Frank; Berger, Martin; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (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.improv_5
Keywords: GRK-1773

Improvement of a Speech Recognizer for Standardized Medical Assessment of Children's Speech by Integration of Prior Knowledge - Bocklet, Tobias; Maier, Andreas; Nöth, Elmar; Eysholdt, Ulrich (Proc. 2nd IEEE Workshop on Spoken Language Technologies (SLT 2010), 2010)

Improvement of Deep Learning-based Auto-detection of Brain Metastases by Integrating previous MRI Images - Dörfler, Arnd; Huang, Yixing; Bert, Christoph; Sommer, Philipp; Frey, Benjamin; Gaipl, Udo; Distel, Luitpold; Weissmann, Thomas; Uder, Michael; Schmidt, Manuel; Maier, Andreas; Fietkau, Rainer; et al. (STRAHLENTHERAPIE UND ONKOLOGIE, 2022)
CRIS-Team WoS Importer:2022-06-03

Improve Path Seeking Accuracy for Iterative Reconstruction Using the Karush-Kuhn-Tucker Conditions - Wu, Meng; Maier, Andreas; Yang, Qiao; Fahrig, Rebecca (The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.improv

Improving Generalization Capability of Multiorgan Segmentation Models Using Dual-Energy CT - Chen, Shuqing; Zhong, Xia; Dorn, Sabrina; Ravikumar, Nishant; Tao, Qinghua; Huang, Xiaolin; Lell, Michael; Kachelriess, Marc; Maier, Andreas (IEEE Transactions on Radiation and Plasma Medical Sciences, 2022)
Multiorgan segmentation in computed tomography (CT) images is essential for a variety of clinical applications. Due to variations in acquisition protocols, clinical data differ in terms of soft-tissue contrast, noise, and artifacts. Devising automatic multiorgan segmentation approaches, which are generalized to data acquired using different CT protocols, are challenging and essential when conducting any multicenter/scanner analyses. In this study, we investigate the use of dual-energy CT (DECT) images to train a fully convolutional segmentation network which is generalized to CT images acquired using different protocols (i.e., at different energy levels and using a variety of reconstruction kernels) from different CT scanners. Furthermore, a novel image fusion approach in the frequency domain is proposed and compared to state-of-the-art fusion approaches, in terms of the segmentation quality achieved by the network. Overall, the experiments indicate that the generalization capability of the segmentation network is improved using DECT image fusion. The proposed fusion method outperforms all single-energy CT approaches. It provided a significant improvement in segmentation accuracy, ranging from 16.0% to 23.35% with p ≤ 0.03. Furthermore, two image fusion methods statistically significantly improve segmentation quality in the abdominal organs compared to simply using all available DECT data.
CRIS-Team Scopus Importer:2022-01-14
Keywords: Data augmentation; deep learning; dual-energy computed tomography (DECT); multiorgan segmentation

Improving Hybrid Quantum Annealing Tomographic Image Reconstruction with Regularization Strategies - Nau, Merlin; Vija, Alexander Hans; Reymann, Maximilian; Gohn, Wesley; Maier, Andreas (German Conference on Medical Image Computing - Bildverarbeitung für die Medizin, 2024)
Quantum computing and quantum annealing present promising avenues for addressing complex problems in various fields, including tomographic image reconstruction. This study investigates the application of hybrid quantum annealing in the context of tomographic image reconstruction, focusing on the formulation of compatible conventional image regularization strategies: L2 and total variation. Using a Shepp-Logan phantom of image size 32× 32 with 4-bit grayscale encoding, we study the effect of the regularization techniques under the influence of their parameters and the runtime of the hybrid quantum annealer. The study reveals, that L2 regularization effectively enhances the obtained image reconstructions and total variation can further improve them. Despite efforts to employ regularized hybrid quantum annealing reconstructions, they still fall short in comparison to traditional reconstruction techniques.<br

Improving Parameter Mapping in MRI Relaxometry and Multi-Echo Dixon Using an Automated Spectral Denoising - Lugauer, Felix; Nickel, Dominik; Kannengiesser, Stephan A. R.; Barnes, Samuel; Holshouser, Barbara; Wetzl, Jens; Hornegger, Joachim; Maier, Andreas (Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.improv_5

Improving Segmentation Quality in Rotational Angiography Using Epipolar Consistency - Unberath, Mathias; Aichert, André; Achenbach, Stephan; Maier, Andreas (Proceedings of MICCAI CVII-STENT, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.improv_1

Improving the Noise Propagation Behavior of Different Fatty Acid Quantification Techniques using Spectral Denoising - Schneider, Manuel; Lugauer, Felix; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the 25th Annual Meeting of the ISMRM, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.improv_0

Improving the Sensitivity to Myocardial Scar in Automatic Segmentations of Left Ventricular Myocardium on LGE Images - Vornehm, Marc; Fenski, Maximilian; Preuhs, Elisabeth; Maier, Andreas; Schulz-Menger, Jeanette; Wetzl, Jens; Giese, Daniel (Proceedings of the International Society for Magnetic Resonance in Medicine, 2022)

Incremental Dimensionality Reduction for Respiratory Signal Extraction From X-Ray Sequences - Fischer, Peter; Pohl, Thomas; Maier, Andreas; Hornegger, Joachim (IGIC 2014 - Abstractband, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.increm_6

Indefinite Kernels in Least Squares Support Vector Machines and Principal Component Analysis - Huang, Xiaolin; Maier, Andreas; Hornegger, Joachim; Suykens, Johan A. K. (Applied and Computational Harmonic Analysis, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.indefi
Keywords: indefinite kernel; LS-SVM; kPCA

Inertial Measurements for Motion Compensation in Weight-Bearing Cone-Beam CT of the Knee - Maier, Jennifer; Nitschke, Marlies; Choi, Jang-Hwan; Gold, Garry; Fahrig, Rebecca; Eskofier, Björn; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Involuntary motion during weight-bearing cone-beam computed tomography (CT) scans of the knee causes artifacts in the reconstructed volumes making them unusable for clinical diagnosis. Currently, image-based or marker-based methods are applied to correct for this motion, but often require long execution or preparation times. We propose to attach an inertial measurement unit (IMU) containing an accelerometer and a gyroscope to the leg of the subject in order to measure the motion during the scan and correct for it. To validate this approach, we present a simulation study using real motion measured with an optical 3D tracking system. With this motion, an XCAT numerical knee phantom is non-rigidly deformed during a simulated CT scan creating motion corrupted projections. A biomechanical model is animated with the same tracked motion in order to generate measurements of an IMU placed below the knee. In our proposed multi-stage algorithm, these signals are transformed to the global coordinate system of the CT scan and applied for motion compensation during reconstruction. Our proposed approach can effectively reduce motion artifacts in the reconstructed volumes. Compared to the motion corrupted case, the average structural similarity index and root mean squared error with respect to the no-motion case improved by 13–21% and 68–70%, respectively. These results are qualitatively and quantitatively on par with a state-of-the-art marker-based method we compared our approach to. The presented study shows the feasibility of this novel approach, and yields promising results towards a purely IMU-based motion compensation in C-arm CT.<br
Keywords: Motion compensation; Inertial measurements; CT reconstruction

Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee - Maier, Jennifer; Nitschke, Marlies; Choi, Jang Hwan; Gold, Garry; Fahrig, Rebecca; Eskofier, Björn; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Influence of Inter-Annotator Variability on Automatic Mitotic Figure Assessment - Wilm, Frauke; Bertram, Christof A.; Marzahl, Christian; Bartel, Alexander; Donovan, Taryn A.; Assenmacher, Charles Antoine; Becker, Kathrin; Bennett, Mark; Corner, Sarah; Cossic, Brieuc; Denk, Daniela; Dettwiler, Martina; et al. (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Influence of Reading Errors on the Text-Based Automatic Evaluation of Pathologic Voices - Haderlein, Tino; Nöth, Elmar; Maier, Andreas; Schuster, Maria; Rosanoski, Frank (Proceedings Text, Speech and Dialogue; 11th International Conference, 2008)

Influence of the phase effect on gradient-based and statistics-based focus measures in bright field microscopy - Schöll, Simon; Mualla, Firas; Sommerfeldt, Björn; Steidl, Stefan; Maier, Andreas; Buchholz, Rainer; Hornegger, Joachim (Journal of Microscopy, 2014)
Autofocusing is essential to high throughput microscopy and live cell imaging and requires reliable focus measures. Phase objects such as separated single Chinese hamster ovary cells are almost invisible at the optical focus position in bright field microscopy images. Because of the phase effect, defocused images of phase objects have more contrast. In this paper, we show that widely used focus measures exhibit an untypical behaviour for such images. In the case of homogeneous cells, that is, when most cells tend to lie in the same focal plane, both gradient-based and statistics-based focus measures tend to have a local minimum instead of a global maximum at the optical focus position. On the other hand, if images show inhomogeneous cells, gradient-based focus measures tend to yield typical focus curves, whereas statistics-based focus measures deliver curves similar to the case of homogeneous cells. These results were interpreted using the equation describing the phase effect and patch-wise analysis of the focus curves. Bioprocess engineering experts are also influenced by the phase effect. Forty-four focus positions selected by them led to the conclusion that they prefer to look at defocused images instead of those at the optical focus. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.influe
Keywords: Bright field microscopy; Focus measures; Nontypical focus curves; Phase effect

Influence of X-Ray Radiation on Historical Paper - Zippert, Patrick; Seuret, Mathias; Maier, Andreas; Hausotte, Tino (11th Conference on Industrial Computed Tomography, 2022)
Historical books are often so fragile that they cannot easily be opened in order to read the contents without damaging them. Current research shows, that industrial X-ray computed tomography (CT) can be used to examine closed historical manuscripts. However, the effect of X-rays on ancient paper has rarely been studied, making it difficult to assess the impact of CT measurements on fragile historical manuscripts in terms of additional destruction. To address this problem, various types of paper were exposed to high levels of X-ray radiation in order to examine them for change in optical properties. The investigations showed increasing yellowing due to the increased radiation energy. However, the severity of the yellowing is strongly dependent on the composition of the paper.<br
Keywords: Historical documents; CT measurement side effects; radiation damage

Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging - Schlereth, Maja; Stromer, Daniel; Mantri, Yash; Tsujimoto, Jason; Breininger, Katharina; Maier, Andreas; Anderson, Caesar; Garimella, Pranav S.; Jokerst, Jesse V. (Informatik aktuell, 2022)
Chronic wounds including diabetic and arterial/venous insufficiency injuries have become a major burden for healthcare systems worldwide. Demographic changes suggest that wound care will play an even bigger role in the coming decades. Predicting and monitoring response to therapy in wound care is currently largely based on visual inspection with little information on the underlying tissue. Thus, there is an urgent unmet need for innovative approaches that facilitate personalized diagnostics and treatments at the point-of-care. It has been recently shown that ultrasound imaging can monitor response to therapy in wound care, but this work required onerous manual image annotations. In this study we present initial results of a deep learning-based automatic segmentation of cross-sectional wound size in ultrasound images and identify requirements and challenges for future research on this application. Evaluation of the segmentation results underscores the potential of the proposed deep learning approach to complement non-invasive imaging with Dice scores of 0.34 (U-Net, FCN) and 0.27 (ResNet-U-Net) but also highlights the need for improving robustness further.We conclude that deep learning-supported analysis of non-invasive ultrasound images is a promising area of research to automatically extract cross-sectional wound size and depth information with potential value in monitoring response to therapy.<br
CRIS-Team Scopus Importer:2022-05-06

InSE-NET: A Perceptually Coded Audio Quality Model based on CNN - Jiang, Guanxin; Biswas, Arijit; Bergler, Christian; Maier, Andreas (Audio Engineering Society Convention 151, 2021)

Intelligibility is more than a single word: Quantification of speech intelligibility by ASR and prosody - Maier, Andreas; Haderlein, Tino; Schuster, Maria; Nkenke, Emeka; Nöth, Elmar (Text, Speech and Dialogue, 2007)
In this paper we examine the quality of the prediction of intelligibility scores of human experts. Furthermore, we investigate the differences between subjective expert raters who evaluated speech disorders of laryngectomees and children with cleft lip and palate. We use the recognition rate of a word recognizer and prosodic features to predict the intelligibility score of each individual expert. For each expert and the mean opinion of all experts we present the best features to model their scoring behavior according to the mean rank obtained during a 10-fold cross-validation. In this manner all individual speech experts were modeled with a correlation coefficient of at least r >.75. The mean opinion of all raters is predicted with a correlation of r =.90 for the laryngectomees and r =.86 for the children.

[Intelligibility of children with bilateral and unilateral cleft lip and palate]. - Maier, Andreas; Holst, Alexandra; Schuster, Maria; Schützenberger, Anne; Eysholdt, Ulrich; Nöth, Elmar; Dames, F.; Stelzle, Florian (Laryngo-Rhino-Otologie, 2009)
OBJECTIVE Data about the effect of unilateral or bilateral cleft lip and palate (CLP) on speech quality are inconsistent. In this study we firstly quantify the intelligibility of children with unilateral and bilateral CLP objectively by means of automatic speech recognition system (ASR). METHODS Speech data of 72 German speaking children (8.7+/-3.0 years) with CLP thereof 17 children with bilateral CLP, 23 with CLP on the right side, and 32 on the left, were compared. A group of 159 children aged 9.1+/-2.9 years served as control group. To quantify intelligibility we calculated the word recognition rate (WR) as the percentage of correctly recognized words of a standardized speech test (PLAKSS). RESULTS Between the 3 cleft groups, there was no significant difference in WR. Compared to the control group (WR mean 63.5%+/-12.1%), the patient group (WR mean 48.1%+/-16.3%) shows significant lower WR (p< 0.001). The WR rises with increasing age in the control group and in the patient groups with unilateral cleft significantly. This couldn't be observed in the children with bilateral CLP. In this group the males showed a significantly higher WR than the females. In the control group as in the patient groups with unilateral cleft there is no significant difference between girls and boys. CONCLUSIONS Despite the greater extent of the malformation of children with a bilateral CLP, there is no significant difference to the children with only a unilateral cleft lip and palate.
Keywords: Automatic speech recognition; Cleft lip and palate; Intelligibility; Objective evaluation

Intelligibility of Children with Cleft Lip and Palate: Evaluation by Speech Recognition Techniques - Maier, Andreas; Hacker, Christian; Nöth, Elmar; Nkenke, Emeka; Haderlein, Tino; Rosanowski, Frank; Schuster, Maria (The 18th International Conference on Pattern Recognition, 2006)

Intelligitility Assessment in Children with Cleft Lip and Palate in Italian and German - Scipioni, Marcello; Gerosa, Matteo; Giuliani, Diego; Nöth, Elmar; Maier, Andreas (Proceedings of the 10th Annual Conference of the International Speech Communication Association (Interspeech 2009), 2009)
Current research has shown that the speech intelligibility in children with cleft lip and palate (CLP) can be estimated automatically using speech recognition methods. On German CLP data high and significant correlations between human ratings and the recognition accuracy of a speech recognition system were already reported. In this paper we investigate whether the approach is also suitable for other languages. Therefore, we compare the correlations obtained on German data with the correlations on Italian data. A high and significant correlation (r=0.76; p < 0.01) was identified on the Italian data. These results do not differ significantly from the results on German data (p > 0.05).
Keywords: speech recognition; speech intelligibility; cleft lip and palate

Interactive Neural Network Robot User Investigation for Medical Image Segmentation - Amrehn, Mario; Strumia, Maddalena; Kowarschik, Markus; Maier, Andreas (Informatik aktuell, 2019)
Interactive image segmentation bears the advantage of correctional updates to the current segmentation mask when compared to fully automated systems. Especially in the field of inter-operative medical image processing of a single patient, where a high accuracy is an uncompromisable necessity, a human operator guiding a system towards an optimal segmentation result is a time-efficient constellation benefiting the patient. There are recent categories of neural networks which can incorporate human-computer interaction (HCI) data as additional input for segmentation. In this work, we simulate this HCI data during training with state-of-the-art user models, also called robot users, which aim to act similar to real users given interactive image segmentation tasks. We analyze the influence of chosen robot users, which mimic different types of users and scribble patterns, on the segmentation quality. We conclude that networks trained with robot users with the most spread out seeding patterns generalize well during inference with other robot users.
CRIS-Team Scopus Importer:2019-05-14

Interpreting acoustic features for the assessment of Alzheimer's disease using ForestNet - Pérez Toro, Paula Andrea; Rodriguez Salas, Dalia; Arias Vergara, Tomás; Klumpp, Philipp; Schuster, Maria; Nöth, Elmar; Orozco-Arroyave, Juan Rafael; Maier, Andreas (Smart Health, 2022)
Keywords: Alzheimer's disease; ForestNets; Acoustic analysis; Interpretability

Interpreting acoustic features for the assessment of Alzheimer's disease using ForestNet - Pérez Toro, Paula Andrea; Rodriguez Salas, Dalia; Arias Vergara, Tomás; Klumpp, Philipp; Schuster, Maria; Nöth, Elmar; Orozco Arroyave, Juan Rafael; Maier, Andreas (Smart Health, 2022)
Nowadays, interpretable machine learning models are one of the most critical topics in the medical domain. The lack of interpretation leads to blind and unreliable models for clinicians, despite the fact that the aim is to support diagnosis through these tools. This problem has been increasing since the creation of large models such as those based on deep learning, which, despite providing good performance in prediction and classification tasks, are not transparent to human understanding. One of the increasingly prevalent clinical problems related to acoustic and linguistic disorders is Alzheimer's disease (AD), where one important challenge is to provide speech markers that help in supporting, understanding, and facilitating the diagnosis and monitoring of the disease. It motivates this study which proposes a methodology focused on analyzing acoustic features in AD and at the same time providing interpretation from the results. The proposed approach consists of using decision tree-based methods together with neural networks (ForestNet) for analyzing the classification results. Only features that can give interpretation were considered. Unweighted average recalls of up to 79% were achieved for discriminating AD patients. Then, we looked at the relevant features that provided most of the information for assessing AD, which were those related to rhythm, voiced rates, duration, and phone rates. This confirms that this kind of approach can be suitable for the discrimination of AD while maintaining a good performance.
CRIS-Team Scopus Importer:2022-11-25
Keywords: Acoustic analysis; Alzheimer's disease; ForestNets; Interpretability

Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens - Marzahl, Christian; Hill, Jenny; Stayt, Jason; Bienzle, Dorothee; Welker, Lutz; Wilm, Frauke; Voigt, Joern; Aubreville, Marc; Maier, Andreas; Klopfleisch, Robert; Breininger, Katharina; Bertram, Christof A. (Scientific Data, 2022)
Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset, which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologist. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered.
CRIS-Team WoS Importer:2022-06-17

Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment - Learning New Tricks from Old Dogs - Aubreville, Marc; Bertram, Christof A.; Jabari, Samir; Marzahl, Christian; Klopfleisch, Robert; Maier, Andreas (Bildverarbeitung für die Medizin 2020, 2020)
For histopathological tumor assessment, the count of mitotic figures per area is an important part of prognostication. Algorithmic approaches - such as for mitotic figure identification - have significantly improved in recent times, potentially allowing for computer-augmented or fully automatic screening systems in the future. This trend is further supported by whole slide scanning microscopes becoming available in many pathology labs and could soon become a standard imaging tool. For an application in broader fields of such algorithms, the availability of mitotic figure data sets of sufficient size for the respective tissue type and species is an important precondition, that is, however, rarely met. While algorithmic performance climbed steadily for e.g. human mammary carcinoma, thanks to several challenges held in the field, for many tumor types, data sets are not available. In this work, we assess domain transfer of mitotic figure recognition using domain adversarial training on four data sets, two from dogs and two from humans. We were able to show that domain adversarial training considerably improves accuracy when applying mitotic _gure classification learned from the canine on the human data sets (up to +12.8% in accuracy) and is thus a helpful method to transfer knowledge from existing data sets to new tissue types and species.<br
Keywords: unsupervised domain adaptation; mitotic figure; tumor diagnostic; deep neural networks

Interventional dual-energy imaging - Feasibility of rapid kV-switching on a C-arm CT system - Müller, Kerstin; Datta, Sanjit; Moiz, Ahmad; Choi, Jang-Hwan; Moore, Teri; Pung, Leland; Niebler, Christine; Gold, Garry; Maier, Andreas; Fahrig, Rebecca (Medical Physics, 2016)
Purpose: In the last years, dual-energy CT imaging has shown clinical value, thanks to its ability to differentiate materials based on their atomic number and to exploit different properties of images acquired at two different energies. C-arm CT systems are used to guide procedures in the interventional suite. Until now, there are no commercially available systems that employ dual-energy material decomposition. This paper explores the feasibility of implementing a fast kV-switching technique on a clinically available angiographic system for acquiring dual-energy C-arm CT images. Methods: As an initial proof of concept, a fast kV-switching approach was implemented on an angiographic C-arm system and the peak tube voltage during 3D rotational scans was measured. The tube voltage measurements during fast kV-switching scans were compared to corresponding measurements on kV-constant scans. Additionally, to prove stability of the requested exposure parameters, the accuracy of the delivered tube current and pulse width were also recorded and compared. In a first phantom experiment, the voxel intensity values of the individual tube voltage components of the fast kV-switching scans were compared to their corresponding kV-constant scans. The same phantom was used for a simple material decomposition between different iodine concentrations and pure water using a fast kV-switching protocol of 81 and 125 kV. In the last experiment, the same kV-switching protocol as in the phantom scan was used in an in vivo pig study to demonstrate the clinical feasibility. Results: During rapid kV-switching acquisitions, the measured tube voltage of the x-ray tube during fast switching scans has an absolute deviation of 0.23 π0.13 kV compared to the measured tube voltage produced during kV-constant acquisitions. The stability of the peak tube voltage over different scan requests was about 0.10 kV for the low and 0.46 for the high energy kV-switching scans and less than 0.1 kV for kV-constant scans, indicating slightly lower stability for kV-switching scans. The tube current resulted in a relative deviation of -1.6% for the low and 6.6% overestimation for the high tube voltage of the kV-switching scans compared to the kV-constant scans. The pulse width showed no deviation for the longer pulse width and only minor deviations (0.02 π 0.02 ms) for the shorter pulse widths compared to the kV-constant scans. The phantom experiment using different iodine concentrations showed an accurate correlation (R2< 0.99) between the extracted intensity values in the kV-switching and kV-constant reconstructed volumes, and allows for an automatic differentiation between contrast concentration down to 10% (350 mg/ml iodine) and pure water under low-noise conditions. Preliminary results of iodine and soft tissue separation showed also promising results in the first in vivo pig study. Conclusions: The feasibility of dual-energy imaging using a fast kV-switching method on an angiographic C-arm CT system was investigated. Direct measurements of beam quality in the x-ray field demonstrate the stability of the kV-switching method. Phantom and in vivo experiments showed that images did not deviate from those of corresponding kV-constant scans. All performed experiments confirmed the capability of performing fast kV-switching scans on a clinically available C-arm CT system. More complex material decomposition tasks and postprocessing steps will be part of future investigations.
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.interv
Keywords: C-arm CT; cone-beam CT (CBCT); dual-energy; rapid kV-switching

Interventional heart wall motion analysis with cardiac C-arm CT systems - Müller, Kerstin; Maier, Andreas; Zheng, Yefeng; Wang, Yang; Lauritsch, Günter; Schwemmer, Chris; Rohkohl, Christopher; Hornegger, Joachim; Fahrig, Rebecca (Physics in Medicine and Biology, 2014)
Today, quantitative analysis of three-dimensional (3D) dynamics of the left ventricle (LV) cannot be performed directly in the catheter lab using a current angiographic C-arm system, which is the workhorse imaging modality for cardiac interventions. Therefore, myocardial wall analysis is completely based on the 2D angiographic images or pre-interventional 3D/4D imaging. In this paper, we present a complete framework to study the ventricular wall motion in 4D (3D+t) directly in the catheter lab. From the acquired 2D projection images, a dynamic 3D surface model of the LV is generated, which is then used to detect ventricular dyssynchrony. Different quantitative features to evaluate LV dynamics known from other modalities (ultrasound, magnetic resonance imaging) are transferred to the C-arm CT data. We use the ejection fraction, the systolic dyssynchrony index a 3D fractional shortening and the phase to maximal contraction (φi, max) to determine an indicator of LV dyssynchrony and to discriminate regionally pathological from normal myocardium. The proposed analysis tool was evaluated on simulated phantom LV data with and without pathological wall dysfunctions. The LV data used is publicly available online at https://conrad.stanford.edu/data/heart. In addition, the presented framework was tested on eight clinical patient data sets. The first clinical results demonstrate promising performance of the proposed analysis tool and encourage the application of the presented framework to a larger study in clinical practice. © 2014 Institute of Physics and Engineering in Medicine.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.interv
Keywords: C-arm CT; cardiac dynamics; functional imaging; interventional imaging; wall motion analysis

Intraoperative brain shift compensation using a hybrid mixture model - Bayer, Siming; Ravikumar, Nishant; Strumia, Maddalena; Tong, Xiaoguang; Gao, Ying; Ostermeier, Martin; Fahrig, Rebecca; Maier, Andreas (MICCAI 2018: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pp 116-124, Springer, 2018)
Brain deformation (or brain shift) during neurosurgical procedures such as tumor resection has a significant impact on the accuracy of neuronavigation systems. Compensating for this deformation during surgery is essential for effective guidance. In this paper, we propose a method for brain shift compensation based on registration of vessel centerlines derived from preoperative C-Arm cone beam CT (CBCT) images, to intraoperative ones. A hybrid mixture model (HdMM)-based non-rigid registration approach was formulated wherein, Student’s t and Watson distributions were combined to model positions and centerline orientations of cerebral vasculature, respectively. Following registration of the preoperative vessel centerlines to its intraoperative counterparts, B-spline interpolation was used to generate a dense deformation field and warp the preoperative image to each intraoperative image acquired. Registration accuracy was evaluated using both synthetic and clinical data. The former comprised CBCT images, acquired using a deformable anthropomorphic brain phantom. The latter meanwhile, consisted of four 3D digital subtraction angiography (DSA) images of one patient, acquired before, during and after surgical tumor resection. HdMM consistently outperformed a state-of-the-art point matching method, coherent point drift (CPD), resulting in significantly lower registration errors. For clinical data, the registration error was reduced from 3.73 mm using CPD to 1.55 mm using the proposed metho

Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study - Sievert, Matti; Stelzle, Florian; Aubreville, Marc; Mueller, Sarina K.; Eckstein, Markus; Oetter, Nicolai; Maier, Andreas; Mantsopoulos, Konstantinos; Iro, Heinrich; Goncalves, Miguel (European Archives of Oto-Rhino-Laryngology, 2021)
Purpose: This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with confocal laser endomicroscopy (CLE) during oropharyngeal squamous cell carcinoma (OPSCC) surgery. Methods: We included five consecutive patients confirmed OPSCC and planned tumor resection in September and October 2020. Healthy appearing mucosa in the marginal zone, and the tumor margin, were examined with CLE and biopsy during tumor resection. A total of 12,809 CLE frames were correlated with the gold standard of hematoxylin and eosin staining. Three head and neck surgeons and one pathologist were asked to identify carcinoma in a sample of 169 representative images, blinded to the histological results. Results: Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of OPSCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 86%, 90%, 79%, 88%, and 82%, respectively, with inter-rater reliability and κ-value of 0.60. Conclusion: CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the oropharynx for evaluation and demarcation of cancer. It can eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin.
CRIS-Team Scopus Importer:2021-03-05
Keywords: Confocal laser endomicroscopy; Head and neck malignancies; Oropharyngeal squamous cell carcinoma; Safe surgical margins

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery - Bayer, Siming; Maier, Andreas; Fahrig, Rebecca; Ostermeier, Martin (International Journal of Biomedical Imaging, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.intrao

Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair - Breininger, Katharina; Albarqouni, Shadi; Kurzendorfer, Tanja; Pfister, Marcus; Kowarschik, Markus; Maier, Andreas (CARS 2018—Computer Assisted Radiology and Surgery Proceedings of the 32nd International Congress and Exhibition Berlin, Germany, June 20–23, 2018, 2018)
Purpose: Fusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation exposure and contrast agent, especially for complex endovascular aortic repair (EVAR). Due to patient movement and introduced devices that deform the vasculature, the fusion can become inaccurate. This is usually detected by comparing the preoperative information with the contrasted vessel. To avoid repeated use of iodine, comparison with an implanted stent can be used to adjust the fusion. However, detecting the stent automatically without the use of contrast is challenging as only thin stent wires are visible. Method: We propose a fast, learning-based method to segment aortic stents in single uncontrasted X-ray images. To this end, we employ a fully convolutional network with residual units. Additionally, we investigate whether incorporation of prior knowledge improves the segmentation. Results: We use 36 X-ray images acquired during EVAR for training and evaluate the segmentation on 27 additional images. We achieve a Dice coefficient of 0.933 (AUC 0.996) when using X-ray alone, and 0.918 (AUC 0.993) and 0.888 (AUC 0.99) when adding the preoperative model, and information about the expected wire width, respectively. Conclusion: The proposed method is fully automatic, fast and segments aortic stent grafts in fluoroscopic images with high accuracy. The quality and performance of the segmentation will allow for an intraoperative comparison with the preoperative information to assess the accuracy of the fusion.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.intrao_2
Keywords: Aortic stents; Convolutional neural network; Deep learning; EVAR; Fluoroscopy; Segmentation

Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair - Breininger, Katharina; Albarqouni, Shadi; Kurzendorfer, Tanja; Pfister, Marcus; Kowarschik, Markus; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2018)
Purpose: Fusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation exposure and contrast agent, especially for complex endovascular aortic repair (EVAR). Due to patient movement and introduced devices that deform the vasculature, the fusion can become inaccurate. This is usually detected by comparing the preoperative information with the contrasted vessel. To avoid repeated use of iodine, comparison with an implanted stent can be used to adjust the fusion. However, detecting the stent automatically without the use of contrast is challenging as only thin stent wires are visible. Method: We propose a fast, learning-based method to segment aortic stents in single uncontrasted X-ray images. To this end, we employ a fully convolutional network with residual units. Additionally, we investigate whether incorporation of prior knowledge improves the segmentation. Results: We use 36 X-ray images acquired during EVAR for training and evaluate the segmentation on 27 additional images. We achieve a Dice coefficient of 0.933 (AUC 0.996) when using X-ray alone, and 0.918 (AUC 0.993) and 0.888 (AUC 0.99) when adding the preoperative model, and information about the expected wire width, respectively. Conclusion: The proposed method is fully automatic, fast and segments aortic stent grafts in fluoroscopic images with high accuracy. The quality and performance of the segmentation will allow for an intraoperative comparison with the preoperative information to assess the accuracy of the fusion.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.intrao
Keywords: Aortic stents; EVAR; Fluoroscopy; Convolutional neural network; Deep learning; Segmentation

Intrinsic Noise Improves Speech Recognition in a Computational Model of the Auditory Pathway - Schilling, Achim; Gerum, Richard; Metzner, Claus; Maier, Andreas; Krauß, Patrick (Frontiers in Neuroscience, 2022)
Noise is generally considered to harm information processing performance. However, in the context of stochastic resonance, noise has been shown to improve signal detection of weak sub- threshold signals, and it has been proposed that the brain might actively exploit this phenomenon. Especially within the auditory system, recent studies suggest that intrinsic noise plays a key role in signal processing and might even correspond to increased spontaneous neuronal firing rates observed in early processing stages of the auditory brain stem and cortex after hearing loss. Here we present a computational model of the auditory pathway based on a deep neural network, trained on speech recognition. We simulate different levels of hearing loss and investigate the effect of intrinsic noise. Remarkably, speech recognition after hearing loss actually improves with additional intrinsic noise. This surprising result indicates that intrinsic noise might not only play a crucial role in human auditory processing, but might even be beneficial for contemporary machine learning approaches.
CRIS-Team Scopus Importer:2022-07-15
Keywords: auditory perception; deep artificial neural networks; dorsal cochlear nucleus; hearing loss; speech processing; stochastic resonance; tinnitus mechanisms; Zwicker tone

Investigating Subtle Structural Changes in Eyes with Age-Related Macular Degeneration: A Pilot Study Using Ultrahigh-Resolution Optical Coherence Tomography - Chen, Siyu; Abu-Qmar, Omar; Moult, Eric; Lee, Byungkun; Ploner, Stefan; Maier, Andreas; Waheed, Nadia K.; Fujimoto, James G. (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2020)
CRIS-Team WoS Importer:2020-09-11

Investigation of Single Photon Emission Computed Tomography Acquired on Helical Trajectories - Oppelt, Maximilian; Sanders, James Chester; Maier, Andreas (Bildverarbeitung für die Medizin, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.invest

In Vivo 3D Measurement of Time-dependent Human Knee Joint Compression and Cartilage Strain During Static Weight-Bearing - Choi, Jang-Hwan; McWalter, Emily; Datta, Sanjit; Müller, Kerstin; Maier, Andreas; Gold, Garry; Levenstone, Marc; Fahrig, Rebecca (ORS 2016 Annual Meeting, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.invivo

Is crowd-algorithm collaboration an advanced alternative to crowd-sourcing on cytology slides? - Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Gerlach, Stefan; Maier, Jennifer; Voigt, Jörn; Hill, Jenny; Klopfleisch, Robert; Maier, Andreas (Informatik aktuell, 2020)
Modern, state-of-the-art deep learning approaches yield human like performance in numerous object detection and classification tasks. The foundation for their success is the availability of training datasets of substantially high quantity, which are expensive to create, especially in the field of medical imaging. Crowdsourcing has been applied to create large datasets for a broad range of disciplines. This study aims to explore the challenges and opportunities of crowd-algorithm collaboration for the object detection task of grading cytology whole slide images. We compared the classical crowdsourcing performance of twenty participants with their results from crowd-algorithm collaboration. All participants performed both modes in random order on the same twenty images. Additionally, we introduced artificial systematic flaws into the precomputed annotations to estimate a bias towards accepting precomputed annotations. We gathered 9524 annotations on 800 images from twenty participants organised into four groups in concordance to their level of expertise with cytology. The crowd-algorithm mode improved on average the participants’ classification accuracy by 7%, the mean average precision by 8% and the inter-observer Fleiss’ kappa score by 20%, and reduced the time spent by 31%. However, two thirds of the artificially modified false labels were not recognised as such by the contributors. This study shows that crowd-algorithm collaboration is a promising new approach to generate large datasets when it is ensured that a carefully designed setup eliminates potential biases.
CRIS-Team Scopus Importer:2020-04-21

Is Multitask Learning Always Better? - Mattick, Alexander; Mayr, Martin; Maier, Andreas; Christlein, Vincent (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
Multitask learning has been a common technique for improving representations learned by artificial neural networks for decades. However, the actual effects and trade-offs are not much explored, especially in the context of document analysis. We demonstrate a simple and realistic scenario on real-world datasets that produces noticeably inferior results in a multitask learning setting than in a single-task setting. We hypothesize that slight data-manifold and task semantic shifts are sufficient to lead to adversarial competition of tasks inside networks and demonstrate this experimentally in two different multitask learning formulations.
CRIS-Team Scopus Importer:2022-06-10
Keywords: Deep learning; Document analysis; Document classification; Multitask learning

Isocenter Determination from Projection Matrices of a C-Arm CBCT - Amri, Ahmed; Bier, Bastian; Maier, Jennifer; Maier, Andreas (Informatik aktuell, 2019)
An accurate position of the isocenter of a cone-beam CT trajectory is mandatory for accurate image reconstruction. For analytical backprojection algorithms, it is assumed that the X-ray source moves on a perfectly circular trajectory, which is not true for most practical clinical trajectories due to mechanical instabilities. Besides, the flexibility of novel robotic C-arm systems enables new trajectories where the computation of the isocenter might not be straight forward. An inaccurate isocenter position directly affects the computation of the redundancy weights and consequently affects the reconstructions immediately. In this work, we compare different methods for computing the isocenter of a non-ideal circular scan trajectory and evaluate their robustness in the presence of noise. The best results were achieved using a method based on a least-square-based fit. Furthermore, we show that an inaccurate isocenter computation can lead to artifacts in the reconstruction result. Therefore, this work highlights the importance of an accurate isocenter computation with the background of novel upcoming clinical trajectories.
CRIS-Team Scopus Importer:2019-05-14

Isotropic 3-D CINE Imaging with Sub-2mm Resolution in a Single Breath-Hold - Wetzl, Jens; Schmidt, Michaela; Zenge, Michael O.; Lugauer, Felix; Lazar, Laszlo; Nadar, Mariappan; Maier, Andreas; Hornegger, Joachim; Forman, Christoph (Proceedings of the 23rd Annual Meeting of the ISMRM (ISMRM 2015), 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.isotro

Iterative Cross-Scanner Registration for Whole Slide Images - Theelke, Luisa; Wilm, Frauke; Marzahl, Christian; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas; Aubreville, Marc; Breininger, Katharina (2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021), 2021)
The successful registration of digitized microscopic images is required for many applications in digital pathology. In particular, the registration of specimens scanned by different slide scanning systems may be beneficial to transfer expert annotations from one image domain to another and thereby reduce labeling effort. We present an iterative approach to register microscopic specimens digitized with multiple scanning systems, aiming to compute an optimal global transformation for the images at highest resolution. For this purpose, an initial registration based on a down-scaled version of the images is followed by a patch-based iterative update scheme. We make use of the hierarchical structure of digitized whole slide images to gradually approximate the optimal transformation. By using kernel density estimation to weight local transformation estimates, the influence of registration errors can be further mitigated. We validate our method on five histologic and five cytologic samples, each scanned with four different scanning systems. Furthermore, we perform first experiments on samples stained with different stain combinations. Our experiments demonstrate the potential of the proposed method for a variety of datasets and application fields.<br
CRIS-Team WoS Importer:2022-02-25

Iterative CT Reconstruction Using Curvelet-Based Regularization - Wu, Haibo; Maier, Andreas; Hornegger, Joachim (Proceedings des Workshops Bildverarbeitung für die Medizin 2013, 2013)

JBFnet: Low Dose CT-denoising by Trainable Joint Bilateral Filtering - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

JBFnet - Low Dose CT Denoising by Trainable Joint Bilateral Filtering - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Surgery (MICCAI), 2020)
Deep neural networks have shown great success in low dose CT denoising. However, most of these deep neural networks have several hundred thousand trainable parameters. This, combined with the inherent non-linearity of the neural network, makes the deep neural network difficult to understand with low accountability. In this study we introduce JBFnet, a neural network for low dose CT denoising. The architecture of JBFnet implements iterative bilateral filtering. The filter functions of the Joint Bilateral Filter (JBF) are learned via shallow convolutional networks. The guidance image is estimated by a deep neural network. JBFnet is split into four filtering blocks, each of which performs Joint Bilateral Filtering. Each JBF block consists of 112 trainable parameters, making the noise removal process comprehendable. The Noise Map (NM) is added after filtering to preserve high level features. We train JBFnet with the data from the body scans of 10 patients, and test it on the AAPM low dose CT Grand Challenge dataset. We compare JBFnet with state-of-the-art deep learning networks. JBFnet outperforms CPCE3D, GAN and deep GFnet on the test dataset in terms of noise removal while preserving structures. We conduct several ablation studies to test the performance of our network architecture and training method. Our current setup achieves the best performance, while still maintaining behavioural accountability.<br
Keywords: Low Dose CT Denoising; Joint Bilateral Filtering; Precision Learning; Convolutional Neural Networks

Joint Calibration and Motion Estimation in Weight-Bearing Cone-Beam CT of the Knee Joint using Fiducial Markers - Syben-Leisner, Christopher; Bier, Bastian; Berger, Martin; Aichert, André; Fahrig, Rebecca; Gold, Garry; Levenston, Marc; Maier, Andreas (Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.jointc_3
Keywords: C-arm CBCT; Image Reconstruction; Knee-Joint Imaging; Motion Estimation; Self-Calibration

Joint Estimation of Cardiac Motion and T1* Maps for Magnetic Resonance Late Gadolinium Enhancement Imaging - Wetzl, Jens; Stalder, Aurélien F.; Schmidt, Michaela; Akgök, Yigit H.; Tillmanns, Christoph; Lugauer, Felix; Forman, Christoph; Hornegger, Joachim; Maier, Andreas (Lecture Notes in Computer Science, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.jointe

Joint Super-Resolution and Rectification for Solar Cell Inspection - Hoffmann, Mathis; Köhler, Thomas; Doll, Bernd; Schebesch, Frank; Talkenberg, Florian; Peters, Ian Marius; Brabec, Christoph; Maier, Andreas; Christlein, Vincent (IEEE Journal of Photovoltaics, 2021)
Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolution and often not sufficient to reliably detect small defects, we apply multi-frame super-resolution (MFSR) to a sequence of low resolution measurements. In addition, the rectification and removal of lens distortion simplifies subsequent analysis. Therefore, we propose to fuse this pre-processing with standard MFSR algorithms. This is advantageous, because we omit a separate processing step, the motion estimation becomes more stable and the spacing of high-resolution (HR) pixels on the rectified module image becomes uniform w.r.t. the module plane, regardless of perspective distortion. We present a comprehensive user study showing that MFSR is beneficial for defect recognition by human experts and that the proposed method performs better than the state of the art. Furthermore, we apply automated crack segmentation and show that the proposed method performs 3x better than bicubic upsampling and 2x better than the state of the art for automated inspection.

Keeping the Pace: Heart Rate Informed 3-D Motion Detection for Adaptive Temporal Smoothing - Taubmann, Oliver; Lauritsch, Günter; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim (Proceedings of the 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.keepin

“Keep it simple, scholar”: an experimental analysis of few-parameter segmentation networks for retinal vessels in fundus imaging - Fu, Weilin; Breininger, Katharina; Schaffert, Roman; Pan, Zhaoya; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2021)
Purpose: With the recent development of deep learning technologies, various neural networks have been proposed for fundus retinal vessel segmentation. Among them, the U-Net is regarded as one of the most successful architectures. In this work, we start with simplification of the U-Net, and explore the performance of few-parameter networks on this task. Methods: We firstly modify the model with popular functional blocks and additional resolution levels, then we switch to exploring the limits for compression of the network architecture. Experiments are designed to simplify the network structure, decrease the number of trainable parameters, and reduce the amount of training data. Performance evaluation is carried out on four public databases, namely DRIVE, STARE, HRF and CHASE_DB1. In addition, the generalization ability of the few-parameter networks are compared against the state-of-the-art segmentation network. Results: We demonstrate that the additive variants do not significantly improve the segmentation performance. The performance of the models are not severely harmed unless they are harshly degenerated: one level, or one filter in the input convolutional layer, or trained with one image. We also demonstrate that few-parameter networks have strong generalization ability. Conclusion: It is counter-intuitive that the U-Net produces reasonably good segmentation predictions until reaching the mentioned limits. Our work has two main contributions. On the one hand, the importance of different elements of the U-Net is evaluated, and the minimal U-Net which is capable of the task is presented. On the other hand, our work demonstrates that retinal vessel segmentation can be tackled by surprisingly simple configurations of U-Net reaching almost state-of-the-art performance. We also show that the simple configurations have better generalization ability than state-of-the-art models with high model complexity. These observations seem to be in contradiction to the current trend of continued increase in model complexity and capacity for the task under consideration.
CRIS-Team Scopus Importer:2021-05-21
Keywords: Computation cost; Fundus image; U-Net; Vessel segmentation

Kinect-Based Correction of Overexposure Artifacts in Knee Imaging with C-Arm CT Systems - Rausch, Johannes; Maier, Andreas; Fahrig, Rebecca; Choi, Jang-Hwan; Hinshaw, Waldo; Schebesch, Frank; Haase, Sven; Wasza, Jakob; Hornegger, Joachim; Riess, Christian (International Journal of Biomedical Imaging, 2016)

Known operator learning and hybrid machine learning in medical imaging - A review of the past, the present, and the future - Maier, Andreas; Köstler, Harald; Heisig, Marco; Krauss, Patrick; Yang, Seung Hee (Progress in Biomedical Engineering, 2022)
In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging. We start with a short summary of the general developments of the past in machine learning and how general and specialized approaches have been in competition in the past decades. A particular focus will be the theoretical and experimental evidence pro and contra hybrid modelling. Next, we inspect several new developments regarding hybrid machine learning with a particular focus on so-called known operator learning and how hybrid approaches gain more and more momentum across essentially all applications in medical imaging and medical image analysis. As we will point out by numerous examples, hybrid models are taking over in image reconstruction and analysis. Even domains such as physical simulation and scanner and acquisition design are being addressed using machine learning grey box modelling approaches. Towards the end of the article, we will investigate a few future directions and point out relevant areas in which hybrid modelling, meta learning, and other domains will likely be able to drive the state-of-the-art ahead.
CRIS-Team Scopus Importer:2022-04-15
Keywords: grey-box models; hybrid machine learning; known operator learning; machine learning; medical image processing; review

Known Operator Learning enables Constrained Projection Geometry Conversion: Parallel to Cone-beam for Hybrid MR/X-ray Imaging - Syben-Leisner, Christopher; Stimpel, Bernhard; Roser, Philipp; Dörfler, Arnd; Maier, Andreas (IEEE Transactions on Medical Imaging, 2020)
X-ray imaging is a wide-spread real-time imaging technique. Magnetic Resonance Imaging (MRI) offers a multitude of contrasts that offer improved guidance to interventionalists. As such simultaneous real-time acquisition and overlay would be highly favorable for image-guided interventions, e.g., in stroke therapy. One major obstacle in this setting is the fundamentally different acquisition geometry. MRI k-space sampling is associated with parallel projection geometry, while the Xray acquisition results in perspective distorted projections. The classical rebinning methods to overcome this limitation inherently suffers from a loss of resolution. To counter this problem, we present a novel rebinning algorithm for parallel to cone-beam conversion. We derive a rebinning formula that is then used to find an appropriate deep neural network architecture. Following the known operator learning paradigm, the novel algorithm is mapped to a neural network with differentiable projection operators enabling data-driven learning of the remaining unknown operators. The evaluation aims in two directions: First, we give a profound analysis of the different hypotheses to the unknown operator and investigate the influence of numerical training data. Second, we evaluate the performance of the proposed method against the classical rebinning approach. We demonstrate that the derived network achieves better results than the baseline method and that such operators can be trained with simulated data without losing their generality making them applicable to real data without the need for retraining or transfer learning.<br
Keywords: Detectors; Magnetic resonance imaging; Geometry; X-ray imaging; Tomography; Image reconstruction

Kritischer Katalog der Luther-Bildnisse (1519-1530) - Schubert, Anselm; Hess, Daniel; Heydenreich, Gunnar; Mack, Oliver; Maier, Andreas; Görres, Daniel; Hänsch, Anna-Amalie; Klinke, Thomas; Ottweiler, Wibke; Sindel, Aline; Christlein, Vincent (Gütersloher Verlagshaus, 2023)

Künstliche Intelligenz als Marktteilnehmer – Technische Möglichkeiten - Maier, Andreas; Bayer, Siming (Marktermöglichung durch Recht im Digitalen Zeitalter, 2023)
Kartellgesetze (auch als Wettbewerbsgesetze bezeichnet) werden entwickelt, um einen starken Wettbewerb zu fördern und haben den Zweck, Verbraucher vor räuberischen Geschäftspraktiken zu schützen. Oberste Ziele des Kartellrechts sind die Gewährleistung der Funktionsweise der Märkte sowie die Gewährleistung eines fairen Wettbewerbs. Ein prominentes Beispiel für einen Verstoß gegen das Kartellrecht ist illegale Preisabsprache. Definitionsgemäß handelt es sich um eine Vereinbarung zwischen Wettbewerbern, die Preise oder andere Wettbewerbsbedingungen festlegt und somit gegen den Grundsatz des Preisbildungsmechanismus durch freie Marktkräfte verstößt. Ein typisches Merkmal illegaler Preisabsprachen ist die nachweisbare Kommunikation (schriftlich oder mündlich) zwischen menschlichen Marktteilnehmern. Im Zeitalter der künstlichen Intelligenz und des E-Commerce steht die Aufdeckung dieser illegalen Praxis jedoch vor neuen Herausforderungen, da Verhaltensweisen, die gegen Kartellrechte verstoßen, wie zum Beispiel der Preisbildungsmechanismus, teil- oder vollautomatisiert sind. Außerdem sind die Marktteilnehmer nicht unbedingt Menschen. Mit anderen Worten, es stehen neue technologische Möglichkeiten zur Verfügung, um illegale Preispolitik und Geschäftspraktiken zu verschleiern. In diesem Kurzbeitrag möchten wir uns genau dieser Fragestellung widmen, nämlich welche grundlegenden technischen Möglichkeiten bringt die künstliche Intelligenz? Wie sieht die Kommunikation auf den elektronischen Märkten aus? Und zu guter Letzt, welche Bedeutung und Konsequenzen hat der Einsatz von der künstlichen Intelligenz auf elektronische Märkte für die Kartellgesetz
Keywords: Antitrust Law; AI; E-Commerce

LACOPE: Latency-Constrained Pitch Estimation for Speech Enhancement - Schröter, Hendrik; Rosenkranz, Tobias; Escalante Banuelos, Alberto; Maier, Andreas (Proc. Interspeech 2021, 2021)
Fundamental frequency (f0) estimation, also known as pitch tracking, has been a long-standing research topic in the speech and signal processing community. Many pitch estimation algorithms, however, fail in noisy conditions or introduce large delays due to their frame size or Viterbi decoding. In this study, we propose a deep learning-based pitch estimation algorithm, LACOPE, which was trained in a joint pitch estimation and speech enhancement framework. In contrast to previous work, this algorithm allows for a configurable latency down to an algorithmic delay of 0. This is achieved by exploiting the smoothness properties of the pitch trajectory. That is, a recurrent neural network compensates delay introduced by the feature computation by predicting the pitch for a desired point, allowing a trade-off between pitch accuracy and latency. We integrate the pitch estimation in a speech enhancement framework for hearing aids. For this application, we allow a delay on the analysis side of approx. 5 ms. The pitch estimate is then used for constructing a comb filter in frequency domain as post-processing step to remove intra-harmonic noise. Our pitch estimation performance is on par with SOTA algorithms like PYIN or CREPE for spoken speech in all noise conditions while introducing minimal latency.
Keywords: pitch estimation; speech enhancement; convolutional recurrent neural network

Latent Shape Constraint for Anatomical Landmark Detection on Spine Radiographs - Kordon, Florian Johannes; Maier, Andreas; Kunze, Holger (Bildverarbeitung für die Medizin 2021, 2021)
Vertebral corner points are frequently used landmarks for a vast variety of orthopedic and trauma surgical applications. Algorithmic approaches that are designed to automatically detect them on 2D radiographs have to cope with varying image contrast, high noise levels, and superimposed soft tissue. To enforce an anatomically correct landmark configuration in presence of these limitations, this study investigates a shape constraint technique based on data-driven encodings of the spine geometry. A contractive PointNet autoencoder is used to map numerical landmark coordinate representations onto a low-dimensional shape manifold. A distance norm between prediction and ground truth encodings then serves as an additional loss term during optimization. The method is compared and evaluated on the SpineWeb16 dataset. Small improvements can be observed, recommending further analysis of the encoding design and composite cost function.<br
Keywords: Autoencoder; Shape constraint; X-ray Image Processing

Layered X-ray Motion Estimation using Primal-Dual Optimization - Mehmood, Ehsan; Fischer, Peter; Pohl, Thomas; Horz, Tim; Maier, Andreas (Informatik aktuell, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.layere

Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations - Thies, Mareike; Wagner, Fabian; Gu, Mingxuan; Folle, Lukas; Felsner, Lina; Maier, Andreas (Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 1230409 (2022), 2022)
Learned iterative reconstruction algorithms for inverse problems offer the flexibility to combine analytical knowledge about the problem with modules learned from data. This way, they achieve high reconstruction performance while ensuring consistency with the measured data. In computed tomography, extending such approaches from 2D fan-beam to 3D cone-beam data is challenging due to the prohibitively high GPU memory that would be needed to train such models. This paper proposes to use neural ordinary differential equations to solve the reconstruction problem in a residual formulation via numerical integration. For training, there is no need to backpropagate through several unrolled network blocks nor through the internals of the solver. Instead, the gradients are obtained very memory-efficiently in the neural ODE setting allowing for training on a single consumer graphics card. The method is able to reduce the root mean squared error by over 30% compared to the best performing classical iterative reconstruction algorithm and produces high quality cone-beam reconstructions even in a sparse view scenario.
Keywords: Inverse problems; computed tomography; iterative reconstruction; known operators

Learning an Attention Model for Robust 2-D/3-D Registration Using Point-To-Plane Correspondences - Schaffert, Roman; Wang, Jian; Fischer, Peter; Borsdorf, Anja; Maier, Andreas (IEEE Transactions on Medical Imaging, 2020)
Minimally invasive procedures rely on image guidance for navigation at the operation site to avoid large surgical incisions. X-ray images are often used for guidance, but important structures may be not well visible. These structures can be overlaid from pre-operative 3-D images and accurate alignment can be established using 2-D/3-D registration. Registration based on the point-to-plane correspondence model was recently proposed and shown to achieve state-of-the-art performance. However, registration may still fail in challenging cases due to a large portion of outliers. In this paper, we describe a learning-based correspondence weighting scheme to improve the registration performance. By learning an attention model, inlier correspondences get higher attention in the motion estimation while the outlier correspondences are suppressed. Instead of using per-correspondence labels, our objective function allows to train the model directly by minimizing the registration error. We demonstrate a highly increased robustness, e.g. increasing the success rate from 84.9% to 97.0% for spine registration. In contrast to previously proposed learning-based methods, we also achieve a high accuracy of around 0.5mm mean re-projection distance. In addition, our method requires a relatively small amount of training data, is able to learn from simulated data, and generalizes to images with additional structures which are not present during training. Furthermore, a single model can be trained for both, different views and different anatomical structures.
CRIS-Team WoS Importer:2020-10-16

Learning-based correspondence estimation for 2-D/3-D registration - Schaffert, Roman; Weiß, Markus; Wang, Jian; Borsdorf, Anja; Maier, Andreas (Informatik aktuell, 2020)
In many minimally invasive procedures, image guidance using a C-arm system is utilized. To enhance the guidance, information from pre-operative 3-D images can be overlaid on top of the 2-D fluoroscopy and 2-D/3-D image registration techniques are used to ensure an accurate overlay. Despite decades of research, achieving a highly reliable registration remains challenging. In this paper, we propose a learning-based correspondence estimation, which focuses on contour points and can be used in combination with the point-to-plane correspondence model-based registration. When combined with classical correspondence estimation in a refinement step, the method highly increases the robustness, leading to a capture range of 36mm and a success rate of 98.5%, compared to 14mm and 71.9% for the purely classical approach, while maintaining a high accuracy of 0.430.08mm of mean re-projection distance.
CRIS-Team Scopus Importer:2020-04-21

Learning-based misalignment detection for 2-D/3-D overlays - Schaffert, Roman; Wang, Jian; Fischer, Peter; Borsdorf, Anja; Maier, Andreas (Informatik aktuell, 2020)
In minimally invasive procedures, a standard routine of observing the operational site is using image guidance. X-ray fluoroscopy using C-arm systems is widely used. In complex cases, overlays of preoperative 3-D images are necessary to show structures that are not visible in the 2-D X-ray images. The alignment quality may degenerate during an intervention, e. g. due to patient motion, and a new registration needs to be performed. However, a decrease in alignment quality is not always obvious, as the clinician often focuses on structures which are not visible in the 2-D image, and only these structures are visualized in the overlay. In this paper, we propose a learning-based method for detecting different degrees of misalignment. The method is based on point-to-plane correspondences and a pre-trained neural network originally used for detecting good correspondences. The network is extended by a classification branch to detect different levels of misalignment. Compared to simply using the normalized gradient correlation similarity measure as a basis for the decision, we show a highly improved performance, e. g. improving the AUC score from 0.918 to 0.993 for detecting misalignment above 5mm of mean re-projection distance.
CRIS-Team Scopus Importer:2020-04-21

Learning-based Patch-wise Metal Segmentation with Consistency Check - Gottschalk, Tristan; Maier, Andreas; Kordon, Florian Johannes; Kreher, Björn W. (Bildverarbeitung für die Medizin 2021, 2021)
Metal implants that are inserted into the patient's body during trauma interventions cause heavy artifacts in 3D X-ray acquisitions. Metal Artifact Reduction (MAR) methods, whose first step is always a segmentation of the present metal objects, try to remove these artifacts. Thereby, the segmentation is a crucial task which has strong influence on the MAR's outcome. This study proposes and evaluates a learning-based patch-wise segmentation network and a newly proposed Consistency Check as post-processing step. The combination of the learned segmentation and Consistency Check reaches a high segmentation performance with an average IoU score of 0.924 on the test set. Furthermore, the Consistency Check proves the ability to significantly reduce false positive segmentations whilst simultaneously ensuring consistent segmentation
Keywords: Metal Artifact Reduction; Deep Learning; Consistency Check

Learning-based Trajectory Optimization for a Twin Robotic CT System - Schneider, Linda-Sophie; Thies, Mareike; Schielein, Richard; Syben, Christopher; Unberath, Mathias; Maier, Andreas (NDT.net Issue: 2023-03, 2023)
Non-circular twin robot computed tomography (CT) systems provide a high degree of flexibility in positioning the X-ray source and detector. This makes the design of optimal CT trajectories particularly important, as it can potentially allow for a reduction in the number of projections required for image acquisition. Therefore, the optimization of CT trajectories is a key area of research in this field. This paper presents a method for optimising the trajectory of a non-circular twin robot computed tomography system, using a so-called detectability index to guide the optimization process. The proposed method involves simulating possible X-ray projections that are reachable by the robotic CT system and using a regressive ResNet-34 to predict the detectability index of each projection. An integer optimization problem is then used to generate an optimised CT trajectory based on this prediction, with the haversine distance between projections serving as an additional constraint. The performance of the ResNet-34 is evaluated on unseen projections. It effectively predicted the detectability index for a spherical sampling space, allowing for the optimization of arbitrary CT trajectories. In addition, the method was able to identify valuable projections according to the detectability index, enabling the reconstruction of a test object with only 30 out of 800 of the previously acquired projections without significant loss of information in the reconstruction domain.<br

Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests - Sindel, Aline; Breininger, Katharina; Käßer, Johannes; Heß, Andreas; Maier, Andreas; Köhler, Thomas (25th IEEE International Conference on Image Processing, ICIP 2018, 2018)
Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical structures. However, the acquisition of MR volumes with high spatial resolution leads to long scan times. To this end, we propose volumetric super-resolution forests (VSRF) to enhance MRI resolution retrospectively. Our method learns a locally linear mapping between low-resolution and high-resolution volumetric image patches by employing random forest regression. We customize features suitable for volumetric MRI to train the random forest and propose a median tree ensemble for robust regression. VSRF outperforms state-of-the-art example-based super-resolution in terms of image quality and efficiency for model training and inference on different MRI datasets. It is also superior to unsupervised methods with just a handful or even a single volume to assemble training data.<br
Keywords: MRI; Super-resolution; Random forests

Learning how to Clean Fingerprints – Deep Learning based Separated Artefact Reduction and Regression for MR Fingerprinting - Xu, Yiling; Hoppe, Elisabeth; Speier, Peter; Kluge, Thomas; Nittka, Mathias; Koerzdoerfer, Gregor; Maier, Andreas (ISMRM Workshop on Data Sampling & Image Reconstruction, 2020)

Learning how to Clean Fingerprints – Deep Learning based Separated Artefact Reduction and Regression for MR Fingerprinting - Xu, Yiling; Hoppe, Elisabeth; Speier, Peter; Kluge, Thomas; Nittka, Mathias; Koerzdoerfer, Gregor; Maier, Andreas (International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition, 2020)

Learning the Inverse Weighted Radon Transform - Roser, Philipp; Felsner, Lina; Maier, Andreas; Riess, Christian (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Learning the Update Operator for 2D/3D Image Registration - Jaganathan, Srikrishna; Wang, Jian; Borsdorf, Anja; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories - Zaech, Jan Nico; Gao, Cong; Bier, Bastian; Taylor, Russell; Maier, Andreas; Navab, Nassir; Unberath, Mathias (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction. These artifacts are particularly strong around metal implants, inhibiting widespread adoption of 3D cone-beam CT (CBCT) despite clear opportunity for intra-operative verification of implant positioning, e. g. in spinal fusion surgery. On synthetic and real data, we demonstrate that much of the artifact can be avoided by acquiring better data for reconstruction in a task-aware and patient-specific manner, and describe the first step towards the envisioned task-aware CBCT protocol. The traditional short-scan CBCT trajectory is planar, with little room for scene-specific adjustment. We extend this trajectory by autonomously adjusting out-of-plane angulation. This enables C-arm source trajectories that are scene-specific in that they avoid acquiring “poor images”, characterized by beam hardening, photon starvation, and noise. The recommendation of ideal out-of-plane angulation is performed on-the-fly using a deep convolutional neural network that regresses a detectability-rank derived from imaging physics.
CRIS-Team Scopus Importer:2019-12-10
Keywords: Deep reinforcement learning; Robotic imaging

Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views - Bier, Bastian; Goldmann, Florian; Zaech, Jan Nico; Fotouhi, Javad; Hegeman, Rachel; Grupp, Robert; Armand, Mehran; Osgood, Greg; Navab, Nassir; Maier, Andreas; Unberath, Mathias (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose: Minimally invasive alternatives are now available for many complex surgeries. These approaches are enabled by the increasing availability of intra-operative image guidance. Yet, fluoroscopic X-rays suffer from projective transformation and thus cannot provide direct views onto anatomy. Surgeons could highly benefit from additional information, such as the anatomical landmark locations in the projections, to support intra-operative decision making. However, detecting landmarks is challenging since the viewing direction changes substantially between views leading to varying appearance of the same landmark. Therefore, and to the best of our knowledge, view-independent anatomical landmark detection has not been investigated yet. Methods: In this work, we propose a novel approach to detect multiple anatomical landmarks in X-ray images from arbitrary viewing directions. To this end, a sequential prediction framework based on convolutional neural networks is employed to simultaneously regress all landmark locations. For training, synthetic X-rays are generated with a physically accurate forward model that allows direct application of the trained model to real X-ray images of the pelvis. View invariance is achieved via data augmentation by sampling viewing angles on a spherical segment of 120 ∘ × 90 ∘ . Results: On synthetic data, a mean prediction error of 5.6 ± 4.5 mm is achieved. Further, we demonstrate that the trained model can be directly applied to real X-rays and show that these detections define correspondences to a respective CT volume, which allows for analytic estimation of the 11 degree of freedom projective mapping. Conclusion: We present the first tool to detect anatomical landmarks in X-ray images independent of their viewing direction. Access to this information during surgery may benefit decision making and constitutes a first step toward global initialization of 2D/3D registration without the need of calibration. As such, the proposed concept has a strong prospect to facilitate and enhance applications and methods in the realm of image-guided surgery.
CRIS-Team Scopus Importer:2019-05-02
Keywords: 2D/3D registration; Anatomical landmarks; Convolutional neural networks; Landmark detection

Learning to recognize abnormalities in chest X-rays with location-aware dense networks - Gündel, Sebastian; Grbic, Sasa; Georgescu, Bogdan; Liu, Siqi; Maier, Andreas; Comaniciu, Dorin (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve efficiency, and reduce reading errors. With the availability of large scale data sets, several methods have been proposed to classify pathologies on chest X-ray images. However, most methods report performance based on random image based splitting, ignoring the high probability of the same patient appearing in both training and test set. In addition, most methods fail to explicitly incorporate the spatial information of abnormalities or utilize the high resolution images. We propose a novel approach based on location aware Dense Networks (DNetLoc), whereby we incorporate both high-resolution image data and spatial information for abnormality classification. We evaluate our method on the largest data set reported in the community, containing a total of 86,876 patients and 297,541 chest X-ray images. We achieve (i) the best average AUC score for published training and test splits on the single benchmarking data set (ChestX-Ray14 [1]), and (ii) improved AUC scores when the pathology location information is explicitly used. To foster future research we demonstrate the limitations of the current benchmarking setup [1] and provide new reference patient-wise splits for the used data sets. This could support consistent and meaningful benchmarking of future methods on the largest publicly available data sets.
CRIS-Team Scopus Importer:2019-08-13

Learning with Known Operators reduces Maximum Training Error Bounds. - Maier, Andreas; Syben-Leisner, Christopher; Stimpel, Bernhard; Würfl, Tobias; Hoffmann, Mathis; Schebesch, Frank; Fu, Weilin; Mill, Leonid; Kling, Lasse; Christiansen, Silke H. (Nature Machine Intelligence, 2019)
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we also show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from CT image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such the concept is widely applicable for many researchers in physics, imaging, and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.

Left Ventricle Segmentation in LGE-MRI: Filter Based vs. Learning Based - Kurzendorfer, Tanja; Breininger, Katharina; Steidl, Stefan; Brost, Alexander; Forman, Christoph; Maier, Andreas (2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings, 2018)
Ischaemic heart disease is the number one cause of death world wide, which is in close relation with heart failure. If patients suffer from drug-refractory heart failure with a reduced ejection fraction, cardiac resynchronization therapy is a treatment option. For planning the procedure, precise information about the left ventricle's anatomy and scar distribution is required. The clinical gold standard to visualize scar is late gadolinium enhanced magnetic resonance imaging (LGE-MRI). The challenge arises in the myocardium segmentation of these sequences which is a pre-requisite for an accurate scar quantification. In this work, we compare a filter based approach against a learning based approach for LGE-MRI segmentation. For both approaches the segmentation workflow consists of four major steps. First, the left ventricle is detected. Second, the blood pool is estimated. Third, the endocardium is refined using scar information. Fourth, the epicardium is extracted.The proposed methods were evaluated on 100 clinical LGE-MRI data sets. For the learning based approach a 5-fold nested cross-validation is applied to evaluate the hyper-parameters. The learning based segmentation achieves slightly better results, with a Dice score of 0.82 ± 0.09 for the endocard and 0.81 ± 0.08 for the epicard.
CRIS-Team Scopus Importer:2020-11-20

Left ventricle segmentation in LGE-MRI using multiclass learning - Kurzendorfer, Tanja; Breininger, Katharina; Steidl, Stefan; Maier, Andreas; Fahrig, Rebecca (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
Cardiovascular diseases are the major cause of death worldwide. Magnetic resonance imaging (MRI) is often used for the diagnosis of cardiac diseases because of its good soft tissue contrast. Furthermore, the fibrosis characterization of the myocardium can be important for accurate diagnosis and treatment planning. The clinical gold standard to visualize myocardial scarring is late gadolinium enhanced (LGE) MRI. However, the challenge arises in the accurate segmentation of the endocardial and epicardial border because of the smooth transition between the blood pool and scarred myocardium, as contrast agent accumulates in the damaged tissue and leads to hyper-enhancements. An exact segmentation, is essential for the scar tissue quantification. We propose a deep learning-based method to segment the left ventricle's endocardium and epicardium in LGE-MRI. To this end, a multi-scale fully convolutional neural network with skip-connections (U-Net) and residual units is applied to solve the multiclass segmentation problem. As a loss function, weighted cross-entropy is used. The network is trained on 70 clinical LGE MRI sequences, validated with 5, and evaluated with 26 data sets. The approach yields a mean Dice coefficient of 0.90 for the endocard and 0.87 for the epicard. The proposed method segments the endocardium and epicardium of the left ventricle fully automatically with a high accuracy.
CRIS-Team Scopus Importer:2019-07-12
Keywords: Convolutional Neural Network; Late Gadolinium Enhanced; Left Ventricle; Segmentation

Left Ventricular Heart Phantom for Wall Motion Analysis - Müller, Kerstin; Maier, Andreas; Fischer, Peter; Bier, Bastian; Lauritsch, Günter; Schwemmer, Chris; Fahrig, Rebecca; Hornegger, Joachim (2013 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2013)
In interventional cardiology, three-dimensional anatomical and functional information of the cardiac chambers, e.g. the left ventricle, would have an important impact on diagnosis and therapy. With the technology of C-arm CT it is possible to reconstruct intraprocedural 3-D images from angiographic projection data. Due to the long acquisition time of several seconds, motion-related artifacts, like blurring or streaks, occur. Therefore, the heart dynamics need to be taken into account in order to improve the reconstruction results. When it comes to the evaluation of different motion estimation and compensation algorithms and techniques of motion analysis, there is still a lack of comparability of the final reconstructions and motion parameters between the research groups. Since the results are heavily dependent on the applied motion pattern and simulation parameters, the experiments are not reproducible. We try to overcome these problems by providing varying left heart ventricle phantom datasets, consisting of projection images as well as extracted surface meshes. Up to now, there are six different datasets available: one with a normal sinus rhythm, one with a normal sinus rhythm and a catheter, one with a lateral wall defect of the ventricle, two with a lateral contraction phase shift and one without any motion. The existing datasets are based on a phantom similar to the 4D XCAT phantom with a contrasted left ventricle, myocardium, and aorta. The geometry calibration and acquisition protocol from a real clinical C-arm scanner are used. A webpage is provided where the data and the necessary files are publicly available for download at conrad.stanford.edu/data/heart. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.leftve_0

Lesion Ground Truth Estimation for a Physical Breast Phantom - Hanif, Suneeza; Schebesch, Frank; Jerebko, Anna; Ritschl, Ludwig; Mertelmeier, Thomas; Maier, Andreas (Bildverarbeitung für die Medizin 2017 - Algorithmen, Systeme, Anwendungen, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.lesion

Lightweight Online Noise Reduction on Embedded Devices using Hierarchical Recurrent Neural Networks - Schröter, Hendrik; Rosenkranz, Tobias; Escalante Banuelos, Alberto; Zobel, Pascal; Maier, Andreas (INTERSPEECH 2020, 2020)
Deep-learning based noise reduction algorithms have proven their success especially for non-stationary noises, which makes it desirable to also use them for embedded devices like hearing aids (HAs). This, however, is currently not possible with state-of-the-art methods. They either require a lot of parameters and computational power and thus are only feasible using modern CPUs. Or they are not suitable for online processing, which requires constraints like low-latency by the filter bank and the algorithm itself. In this work, we propose a mask-based noise reduction approach. Using hierarchical recurrent neural networks, we are able to drastically reduce the number of neurons per layer while including temporal context via hierarchical connections. This allows us to optimize our model towards a minimum number of parameters and floating-point operations (FLOPs), while preserving noise reduction quality compared to previous work. Our smallest network contains only 5k parameters, which makes this algorithm applicable on embedded devices. We evaluate our model on a mixture of EUROM and a real-world noise database and report objective metrics on unseen noise.
Keywords: speech enhancement; noise reduction; recurrent neural networks

Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning - Huang, Yixing; Wang, Shengxiang; Guan, Yong; Maier, Andreas (Journal of Synchrotron Radiation, 2020)
In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision to other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts due to missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. Particularly, the U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in 100-degree limited angle tomography. For synthetic test data, the U-Net significantly reduces root-mean-square error (RMSE) from 2.55 X 10^-3/μm in the FBP reconstruction to 1.21 X 10^-3/μm in the U-Net reconstruction, and also improves structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least square denoising of measured projections, the RMSE and SSIM are further improved to 1.16 X 10^-3/μm and 0.932, respectively. For real test data, the proposed method remarkably improves the 3-D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nano-scale imaging in biology, nanoscience and materials scienc
Keywords: Transmission X-Ray Microscopy; deep learning; limited angle tomography

Limited parameter denoising for low-dose X-ray computed tomography using deep reinforcement learning - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (Medical Physics, 2022)
Background The use of deep learning has successfully solved several problems in the field of medical imaging. Deep learning has been applied to the CT denoising problem successfully. However, the use of deep learning requires large amounts of data to train deep convolutional networks (CNNs). Moreover, due to large parameter count, such deep CNNs may cause unexpected results. Purpose In this study, we introduce a novel CT denoising framework, which has interpretable behaviour, and provides useful results with limited data. Methods We employ bilateral filtering in both the projection and volume domains to remove noise. To account for non-stationary noise, we tune the σ parameters of the volume for every projection view, and for every volume pixel. The tuning is carried out by two deep CNNs. Due to impracticality of labelling, the two deep CNNs are trained via a Deep-Q reinforcement learning task. The reward for the task is generated by using a custom reward function represented by a neural network. Our experiments were carried out on abdominal scans for the Mayo Clinic TCIA dataset, and the AAPM Low Dose CT Grand Challenge. Results Our denoising framework has excellent denoising performance increasing the PSNR from 28.53 to 28.93, and increasing the SSIM from 0.8952 to 0.9204. We outperform several state-of-the-art deep CNNs, which have several orders of magnitude higher number of parameters (p-value (PSNR) = 0.000, p-value (SSIM) = 0.000). Our method does not introduce any blurring, which is introduced by MSE loss based methods, or any deep learning artifacts, which are introduced by WGAN based models. Our ablation studies show that parameter tuning and using our reward network results in the best possible results. Conclusions We present a novel CT denoising framework, which focuses on interpretability to deliver good denoising performance, especially with limited data. Our method outperforms state-of-the-art deep neural networks. Future work will be focused on accelerating our method, and generalizing to different geometries and body parts.

Local topology preservation for vascular centerline matching using a hybrid mixture model - Bayer, Siming; Zhai, Zhiwei; Strumia, Maddalena; Tong, Xiaoguang; Gao, Ying; Staring, Marius; Stoel, Berend; Ostermeier, Martin; Fahrig, Rebecca; Arya, Nabavi; Maier, Andreas; Ravikumar, Nishant (Conference Record IEEE 2019 NSSMIC, 2019)
Non-rigid registration is essential for a wide range of clinical applications, such as intraoperative image-guidance and postoperative follow-up assessment, and longitudinal image analysis for disease diagnosis and monitoring. Vascular structures are a rich descriptor of the organ deformation, since it permeates through all organs within body. As vasculature differs in size, shape and topology, following surgical intervention/treatment or due to disease progression, non-rigid vessel matching remains a challenging task. Recently, hybrid mixture models (HdMM) have been applied to tackle this challenge, and demonstrate significant improvements in terms of accuracy and robustness relative to the state-of-the-art. However, the smoothness constraint enforced on the deformation field with this approach only accounts for the global topology of the vasculature, resulting in a reduced capacity to accurately match localized changes to vascular structures, and preserve local topology. In this work, we proposed a modified version of HdMM by formulating an adaptive kernel, to enforce a local smoothness constraint on the deformation field, henceforth referred to as HdMMad. The proposed HdMMad framework is evaluated with cerebral and pulmonary vasculature, acquired retrospectively. The registration results for both data sets demonstrate that the proposed approach outperforms registration algorithms also designed to preserve local topology. Using HdMMad, around 80% of the initial registration error was reduced, for both data sets.<br

Long-term Outcome of Speech Intelligibility in Maxillary Dental Rehabilitation with Full Dentures: A Prospective Study Using Automatic Speech Quantification - Stelzle, Florian; Riemann, Max; Klein, Alfred; Oetter, Nicolai; Rohde, Maximilian; Maier, Andreas; Eitner, Stephan; Neukam, Friedrich Wilhelm; Knipfer, Christian (International Journal of Prosthodontics, 2017)
Complete maxillary edentulism and prosthetic rehabilitation with removable full dentures are known to affect speech intelligibility. The aim of this study was to prospectively investigate the long-term effect of time on speech intelligibility in patients being rehabilitated with newly fabricated full maxillary dentures.Speech was recorded in a group of 14 patients (male = 9, female = 5; mean age ± standard deviation [SD] = 66.14 ± 7.03 years) five times within a mean period of 4 years (mean ± SD: 47.50 ± 18.16 months; minimum/maximum: 24/68 months) and in a control group of 40 persons with healthy dentition (male = 30, female = 10; mean age ± SD = 59 ± 12 years). All 14 participants had their inadequate removable full maxillary dentures replaced with newly fabricated dentures. Speech intelligibility was measured by means of a polyphone-based speech recognition system that automatically computed the percentage of accurately spoken words (word accuracy [WA]) at five different points in time: 1 week prior to prosthetic maxillary rehabilitation (both with and without inadequate dentures in situ) and at 1 week, 6 months, and a mean of 48 months after the insertion of newly fabricated full maxillary dentures.Speech intelligibility of the patients significantly improved after 6 months of adaptation to the new removable full maxillary dentures (WA = 66.93% ± 9.21%) compared to inadequate dentures in situ (WA = 60.12% ± 10.48%). After this period, no further significant change in speech intelligibility was observed. After 1 week of adaptation, speech intelligibility of the rehabilitated patients aligned with that of the control group (WA = 69.79% ± 10.60%) and remained at this level during the examination period of 48 months.The provision of new removable full maxillary dentures can improve speech intelligibility to the level of a healthy control group on a long-term basis.
EVALuna2:24437

Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (Proceeding of the 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020)
Denoising of clinical CT images is an active area for deep learning research. Current clinically approved methods use iterative reconstruction methods to reduce the noise in CT images. Iterative reconstruction techniques require multiple forward and backward projections, which are time-consuming and computationally expensive. Recently, deep learning methods have been successfully used to denoise CT images. However, conventional deep learning methods suffer from the ’black box’ problem. They have low accountability, which is necessary for use in clinical imaging situations. In this paper, we use a Joint Bilateral Filter (JBF) to denoise our CT images. The guidance image of the JBF is estimated using a deep residual convolutional neural network (CNN). The range smoothing and spatial smoothing parameters of the JBF are tuned by a deep reinforcement learning task. Our actor first chooses a parameter, and subsequently chooses an action to tune the value of the parameter. A reward network is designed to direct the reinforcement learning task. Our denoising method demonstrates good denoising performance, while retaining structural information. Our method significantly outperforms state of the art deep neural networks. Moreover, our method has only two parameters, which makes it significantly more interpretable and reduces the ’black box’ problem. We experimentally measure the impact of our intelligent parameter optimization and our reward network. Our studies show that our current setup yields the best results in terms of structural preservation.<br
Keywords: Reinforcement Learning; Precision Learning; Low Dose CT; Denoising; Machine Learning; Deep Learning

Low dose CT denoising with residual convolutional networks and deep learned perceptual loss - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (European Congress of Radiology, 2020)
In accordance with the ALARA principle, we aim to reduce clinical CT dose to improve patient safety. However, reducing the radiation dose increases quantum noise in the reconstructed images. The use of convolutional neural networks (CNN) for low dose CT denoising has shown promising results in body scanning. Current CNN denoising strategies optimise metrics like the mean squared error, which causes smoothing of edges. We investigate the use of perceptual metrics to train a denoising CNN. Seven CT body scans were reconstructed using weighted filtered backprojection. Each volume was reconstructed with the standard clinical dose and with doses of 50%, 25%, 10% and 5% of the standard clinical dose to simulate reduced dose acquisitions. An eight layer residual CNN was used as our denoising network. The network was trained with mean squared error as well as with the use of a deep learned perceptual quality network. 2D and 3D variants of the denoising networks were trained to compare denoising performance. PSNR and SSIM are used to measure noise and feature preservation respectively. The use of a perceptual loss function improved denoising and feature preservation in a 2D denoising network (SSIM = 0.7020, PSNR = 33.8423) against mean squared error denoising (SSIM = 0.6813, PSNR = 33.7544). Perceptual loss functions improved feature preservation in 3D denoising networks (SSIM = 0.6736, PSNR = 33.9524) against mean squared error denoising (SSIM = 0.6699, PSNR = 33.9605). Using a perceptual loss function improves feature preservation in low dose CT denoising. Our deep learned network captures more information about CT image features and CT image noise compared to the mean squared erro
Keywords: CT denoising; Residual Neural Networks; Perceptual Loss

Low Latency Speech Enhancement for Hearing Aids Using Deep Filtering - Schröter, Hendrik; Rosenkranz, Tobias; Escalante-B, Alberto-N; Maier, Andreas (IEEE/ACM Transactions on Audio, Speech and Language Processing, 2022)
Noise reduction is an important feature supporting hearing aid (HA) users in their daily routines and is thus included in most commercially available devices. Latency requirements of HAs require short processing windows resulting in a poor frequency resolution in the whole processing chain including noise reduction. Previous studies have shown that deep neural network (DNN) based algorithms outperform conventional noise reduction algorithms especially for non-stationary noises. This study explores a DNN based noise reduction method using deep filtering targeted for wideband spectrograms given the employed HA filter bank. That is, we predict complex filter coefficients that are linearly applied to the noisy spectrum. We assess different filter sizes over time and frequency axis, and provide evidence for a superior performance over a complex ratio mask. Furthermore, we introduce a frequency response loss that operates on a per-frequency-band basis to fully utilize the deep filtering concept. We objectively demonstrate on-par performance with related state-of-the-art deep learning methods and show in a subjective user study that our method is perceptually preferred to existing HA noise reduction algorithms.
CRIS-Team WoS Importer:2022-09-02

Low-rank and Sparse Matrix Decomposition for Compressed Sensing reconstruction of Magnetic Resonance 4D Phase Contrast blood flow imaging (LoSDeCoS 4D-PCI) - Hutter, Jana; Schmitt, Peter; Gunhild, Aandal; Greiser, Andreas; Forman, Christoph; Grimm, Robert; Hornegger, Joachim; Maier, Andreas (Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2013)

Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios - Haas, Oliver; Maier, Andreas; Rothgang, Eva (Frontiers in Reproductive Health, 2021)
HIV/AIDS is an ongoing global pandemic, with an estimated 39 million infected worldwide. Early detection is anticipated to help improve outcomes and prevent further infections. Point-of-care diagnostics make HIV/AIDS diagnoses available both earlier and to a broader population. Wide-spread and automated HIV risk estimation can offer objective guidance. This supports providers in making an informed decision when considering patients with high HIV risk for HIV testing or pre-exposure prophylaxis (PrEP). We propose a novel machine learning method that allows providers to use the data from a patient's previous stays at the clinic to estimate their HIV risk. All features available in the clinical data are considered, making the set of features objective and independent of expert opinions. The proposed method builds on association rules that are derived from the data. The incidence rate ratio (IRR) is determined for each rule. Given a new patient, the mean IRR of all applicable rules is used to estimate their HIV risk. The method was tested and validated on the publicly available clinical database MIMIC-IV, which consists of around 525,000 hospital stays that included a stay at the intensive care unit or emergency department. We evaluated the method using the area under the receiver operating characteristic curve (AUC). The best performance with an AUC of 0.88 was achieved with a model consisting of 53 rules. A threshold value of 0.66 leads to a sensitivity of 98% and a specificity of 53%. The rules were grouped into drug abuse, psychological illnesses (e.g., PTSD), previously known associations (e.g., pulmonary diseases), and new associations (e.g., certain diagnostic procedures). In conclusion, we propose a novel HIV risk estimation method that builds on existing clinical data. It incorporates a wide range of features, leading to a model that is independent of expert opinions. It supports providers in making informed decisions in the point-of-care diagnostics process by estimating a patient's HIV risk.
Keywords: HIV; risk estimation; association rules; bias; clinical data; machine learning; artificial intelligence; incidence rate ratio

Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks - Hoppe, Elisabeth; Thamm, Florian; Körzdörfer, Gregor; Syben-Leisner, Christopher; Schirrmacher, Franziska; Nittka, Mathias; Pfeuffer, Josef; Meyer, Heiko; Maier, Andreas (German Medical Data Sciences: Shaping Change – Creative Solutions for Innovative Medicine, 2019)
Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues. Although the acquisition is highly accelerated, the reconstruction time remains a problem, as the state-of-the-art template matching compares every signal with a set of possible signals. To overcome this limitation, deep learning based approaches, e.g. Convolutional Neural Networks (CNNs) have been proposed. In this work, we investigate the applicability of Recurrent Neural Networks (RNNs) for this reconstruction problem, as the signals are correlated in time. Compared to previous methods based on CNNs, RNN models yield significantly improved results using in-vivo data.
CRIS-Team Scopus Importer:2019-09-24
Keywords: Artificial Neural Networks; Magnetic Resonance Fingerprinting; Magnetic Resonance Fingerprinting Reconstruction; Recurrent Neural Networks

Make the Most of Time: Temporal Extension of the iTV Algorithm for 4D Cardiac C-Arm CT - Haase, Viktor; Taubmann, Oliver; Huang, Yixing; Krings, Gregor; Lauritsch, Günter; Maier, Andreas; Mertins, Alfred (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.maketh

Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability - Kaiser, N.; Fieselmann, A.; Vesal, Sulaiman; Ravikumar, Nishant; Ritschl, L.; Kappler, S.; Maier, Andreas (MEDICAL IMAGING 2019: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, 2019)
CRIS-Team WoS Importer:2019-09-17

Manifold Learning-based Data Sampling for Model Training - Chen, Shuqing; Dorn, Sabrina; Lell, Michael; Kachelriess, Marc; Maier, Andreas (Informatik-Aktuell, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.manifo_6

Mapping Ensembles of Trees to Sparse, Interpretable Multilayer Perceptron Networks - Rodriguez Salas, Dalia; Mürschberger, Nina; Ravikumar, Nishant; Seuret, Mathias; Maier, Andreas (SN Computer Science, 2020)
Tree-based classifiers provide easy-to-understand outputs. Artificial neural networks (ANN) commonly outperform tree-based classifiers; nevertheless, understanding their outputs requires specialized knowledge in most cases. The highly redundant architecture of ANN is typically designed through an expensive trial-and-error scheme. We aim at (1) investigating whether using ensembles of decision trees to design the architecture of low-redundant, sparse ANN provides better-performing networks, and (2) evaluating whether such trees can be used to provide human-understandable explanations for their outputs. Information about the hierarchy of the features, and how good they are at separating subsets of samples among the classes, is gathered from each branch in an ensemble of trees. This information is used to design the architecture of a sparse multilayer perceptron network. Networks built using our method are called ForestNet. Tree branches corresponding to highly activated neurons are used to provide explanations of the networks’ outputs. ForestNets are able to handle low- and high-dimensional data, as we show on an evaluation using four datasets. Our networks consistently outperformed their respective ensemble of trees and had similar performance to their fully connected counterparts with a significant reduction of connections. Furthermore, our interpretation method seems to provide support for the ForestNet outputs. While ForestNet’s architectures do not allow them yet to capture well the intrinsic variability of visual data, they exhibit very promising results by reducing more than 98% of connections for such visual tasks. Structure similarities between ForestNets and their respective tree ensemble provide means to interpret their outputs.<br
Keywords: Multilayer perceptron; Random forest; Interpretable models; Sparse neural networks; Network architecture; Interpretability

Marker-free motion correction in weight-bearing cone-beam CT of the knee joint - Berger, Martin; Müller, Kerstin; Aichert, André; Unberath, Mathias; Thies, Justus; Choi, Jang-Hwan; Fahrig, Rebecca; Maier, Andreas (Medical Physics, 2016)
Purpose: To allow for a purely image-based motion estimation and compensation in weight-bearing cone-beam computed tomography of the knee joint. Methods: Weight-bearing imaging of the knee joint in a standing position poses additional requirements for the image reconstruction algorithm. In contrast to supine scans, patient motion needs to be estimated and compensated. The authors propose a method that is based on 2D/3D registration of left and right femur and tibia segmented from a prior, motion-free reconstruction acquired in supine position. Each segmented bone is first roughly aligned to the motion-corrupted reconstruction of a scan in standing or squatting position. Subsequently, a rigid 2D/3D registration is performed for each bone to each of K projection images, estimating 6x4xK motion parameters. The motion of individual bones is combined into global motion fields using thin-plate-spline extrapolation. These can be incorporated into a motion-compensated reconstruction in the backprojection step. The authors performed visual and quantitative comparisons between a state-of-the-art marker-based (MB) method and two variants of the proposed method using gradient correlation (GC) and normalized gradient information (NGI) as similarity measure for the 2D/3D registration. Results: The authors evaluated their method on four acquisitions under different squatting positions of the same patient. All methods showed substantial improvement in image quality compared to the uncorrected reconstructions. Compared to NGI and MB, the GC method showed increased streaking artifacts due to misregistrations in lateral projection images. NGI and MB showed comparable image quality at the bone regions. Because the markers are attached to the skin, the MB method performed better at the surface of the legs where the authors observed slight streaking of the NGI and GC methods. For a quantitative evaluation, the authors computed the universal quality index (UQI) for all bone regions with respect to the motion-free reconstruction. The authors quantitative evaluation over regions around the bones yielded a mean UQI of 18.4 for no correction, 53.3 and 56.1 for the proposed method using GC and NGI, respectively, and 53.7 for the MB reference approach. In contrast to the authors registration-based corrections, the MB reference method caused slight nonrigid deformations at bone outlines when compared to a motion-free reference scan. Conclusions: The authors showed that their method based on the NGI similarity measure yields reconstruction quality close to the MB reference method. In contrast to the MB method, the proposed method does not require any preparation prior to the examination which will improve the clinical workflow and patient comfort. Further, the authors found that the MB method causes small, nonrigid deformations at the bone outline which indicates that markers may not accurately reflect the internal motion close to the knee joint. Therefore, the authors believe that the proposed method is a promising alternative to MB motion management.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.marker
Keywords: GRK-1773

Markerless Coil Classification and Localization in a Routine MRI Examination Setting using an RGB-D Camera - Nadar, Gayathri; Zhong, Xia; Maier, Andreas (Bildverarbeitung für die Medizin, 2018)
In a routine MRI scan, a radio-frequency (RF) coil must be selected and placed around the region of interest (ROI). This is a crucial step in the workflow as the accurate coil placement is paramount for obtaining high-quality images. However, in the existing workflow, the position of the coil placement on the patient is estimated empirically by the medical technical assistant (MTA). This routine coil placement process has two shortcomings. On the one hand, the expertise of MTA in coil placement, taking the anatomical difference between patients into account, have a huge impact on the accuracy of the coil placement, and subsequently the image quality. On the other hand, the risk of selecting and placing the incorrect coil should be also be acknowledged. To improve the current workflow and provide feedback ahead of the MRI scans, we use an RGB-D camera to acquire extra information. Using the depth images taken before and after placing the coil, we propose a novel method to classify the coil type and localize the coil position during the coil placement process such that the MTA can place the coil correctly and accurately. We trained and evaluated our method over 100 synthetic data sets. We used two types of coils and placed and deformed them differently according to the anatomical region. The evaluation shows that we can classify the coil type without any error, and localize the coil with a mean translational error of 7.1 cm and mean rotation angle error of 0.025 rad.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.marker

Markov Random Field-based Layer Separation for Simulated X-Ray Image Sequences - Fischer, Peter; Pohl, Thomas; Maier, Andreas; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.markov_6

Masking for DDG in SPECT Reduces Influence of Motion Artifacts - Reymann, Maximilian; Vija, A. Hans; Maier, Andreas (IEEE RTSD NSS-MIC 2023, 2023)

Material Decomposition for Energy Resolving Detectors using Weighted Levenberg-Marquardt Optimization - Manhart, Michael; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Proceedings of the 1st Conference on Image Guided Interventions, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.materi_5

Material Decomposition Using Ensemble Learning for Energy-Resolved Computed Tomography - Lu, Yanye; Kowarschik, Markus; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Proceedings of Fully3D'17, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.materi

Material Decomposition Using Ensemble Learning for Spectral X-ray Imaging - Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Chen, Shuqing; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Radiation and Plasma Medical Sciences, 2018)
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.materi

Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising - Husvogt, Lennart; Ploner, Stefan; Chen, Siyu; Stromer, Daniel; Schottenhamml, Julia; Alibhai, A. Yasin; Moult, Eric; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Biomedical Optics Express, 2021)
Optical coherence tomography angiography (OCTA) is a novel and clinically promising imaging modality to image retinal and sub-retinal vasculature. Based on repeated optical coherence tomography (OCT) scans, intensity changes are observed over time and used to compute OCTA image data. OCTA data are prone to noise and artifacts caused by variations in flow speed and patient movement. We propose a novel iterative maximum a posteriori signal recovery algorithm in order to generate OCTA volumes with reduced noise and increased image quality. This algorithm is based on previous work on probabilistic OCTA signal models and maximum likelihood estimates. Reconstruction results using total variation minimization and wavelet shrinkage for regularization were compared against an OCTA ground truth volume, merged from six co-registered single OCTA volumes. The results show a significant improvement in peak signal-to-noise ratio and structural similarity. The presented algorithm brings together OCTA image generation and Bayesian statistics and can be developed into new OCTA image generation and denoising algorithm
Keywords: OCT; OCTA; optical coherence tomography; optical coherence tomography angiography; baysian; reconstruction

Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency - Preuhs, Alexander; Ravikumar, Nishant; Manhart, Michael; Stimpel, Bernhard; Hoppe, Elisabeth; Syben-Leisner, Christopher; Kowarschik, Markus; Maier, Andreas (Informatik aktuell, 2019)
Open gantry C-arm systems that are placed within the interventional room enable 3-D imaging and guidance for stroke therapy without patient transfer. This can profit in drastically reduced time-totherapy, however, due to the interventional setting, the data acquisition is comparatively slow. Thus, involuntary patient motion needs to be estimated and compensated to achieve high image quality. Patient motion results in a misalignment of the geometry and the acquired image data. Consistency measures can be used to restore the correct mapping to compensate the motion. They describe constraints on an idealized imaging process which makes them also sensitive to beam hardening, scatter, truncation or overexposure. We propose a probabilistic approach based on the Student’s t-distribution to model image artifacts that affect the consistency measure without sourcing from motion.
CRIS-Team Scopus Importer:2019-05-14

Measurement of angles in time-of-flight data for the automatic supervision of training exercises - Soutschek, Stefan; Kornhuber, Johannes; Maier, Andreas; Bauer, Sebastian; Kugler, Patrick; Hornegger, Joachim; Bebenek, Michael; Steckmann, Sven; von Stengel, Simon; Kemmler, Wolfgang (2010 4th International Conference on Pervasive Computing Technologies for Healthcare, Pervasive Health 2010, 2010)
In this paper we present a novel automatic system to supervise and support rehabilitation and fitness exercises. Goal of the system is to enable autonomous training of rehabilitation patients to support their recovery from movement restrictions as well as the prevention of age-related diseases such as osteoporosis. The system uses time-of-flight technology in order to measure movement angles in real-time. This novel setup is compared to a standard tracking system using visual markers. The measurements of both systems correlate with r values between 0.99 and 0.97 depending on the task domain. The mean error of the angle measurements is between 13 and 17 degrees.

Measuring CT Reconstruction Quality with Deep Convolutional Networks - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (Machine Learning for Medical Image Reconstruction, 2019)
With the increasing use of CT in diagnostic imaging, reducing the clinical radiation dose is necessary for ensuring patient safety. Reduced radiation dose results in quantum noise which adversely affects image quality and diagnostic value. Moreover, obtaining high quality images to act as reference images for image quality assessment is difficult. Therefore, automatic no-reference quality assessment of reconstructed images is necessary to preserve diagnostic image quality, while controlling radiation dose. In this work, we investigate the use of a deep convolutional neural network to measure CT image quality. Our developed metric shows concordance with conventional metrics of CT image quality (|r|>" role="presentation">|r|> 0.75, |ρ|>" role="presentation">|ρ|> 0.75). Our metric ranks images in terms of quality highly accurately (τ" role="presentation">τ = 0.98). We measure noise textures and levels not present in our training dataset. Furthermore, the proposed metric shows the improved quality in high dose iteratively reconstructed images, and the reduced quality in low dose images.
Keywords: Quantum noise; Convolutional neural network; Computed tomography; Image quality

Measuring Phonological Precision in Children with Cleft Lip and Palate - Arias Vergara, Tomás; Londoño-Mora, E.; Pérez Toro, Paula Andrea; Schuster, M.; Nöth, Elmar; Orozco Arroyave, Juan Rafael; Maier, Andreas (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023)
Children with Cleft Lip and Palate (CLP) may experience difficulties in oral communication, leading to other developmental problems such as delayed language acquisition and poor social skills; thus, early treatment is essential for successful speech rehabilitation. In this paper, we propose a methodology for automatically assessing the phonological precision of children with CLP. We propose to use the probabilities obtained from a phonological class recognizer to measure phonological precision during connected speech. Furthermore, we compute the nasal-to-sound ratio to improve the automatic detection of the nasality level. For this, we considered speech recordings of 88 children with CLP, assessed by a clinician according to four nasality levels: normal, mild, moderate, and severe. We obtained an F1-score of up to 0.54 for detecting the nasality level automatically. The results suggest that phonological analysis can be used for individualized speech rehabilitation.
CRIS-Team Scopus Importer:2023-09-29
Keywords: children's speech; cleft lip and palate; deep learning; pathological speech processing; phonological analysis

Measuring surface area of skin lesions with 2D and 3D algorithms - Dastjerdi, Houman Mirzaalian; Toepfer, Dominique; Rupitsch, Stefan; Maier, Andreas (International Journal of Biomedical Imaging, 2019)
Purpose. The treatment of skin lesions of various kinds is a common task in clinical routine. Apart from wound care, the assessment of treatment efficacy plays an important role. In this paper, we present a new approach to measure the skin lesion surface in two and three dimensions. Methods. For the 2D approach, a single photo containing a flexible paper ruler is taken. After semi-automatic segmentation of the lesion, evaluation is based on local scale estimation using the ruler. For the 3D approach, reconstruction is based on Structure from Motion. Roughly outlining the region of interest around the lesion is required for both methods. Results. The measurement evaluation was performed on 117 phantom images and five phantom videos for 2D and 3D approach, respectively. We found an absolute error of 0.99±1.18 cm2 and a relative error 9.89± 9.31% for 2D. These errors are <1 cm2 and <5% for five test phantoms in our 3D case. As expected, the error of 2D surface area measurement increased by approximately 10% for wounds on the bent surface compared to wounds on the flat surface. Using our method, the only user interaction is to roughly outline the region of interest around the lesion. Conclusions. We developed a new wound segmentation and surface area measurement technique for skin lesions even on a bent surface. The 2D technique provides the user with a fast, user-friendly segmentation and measurement tool with reasonable accuracy for home care assessment of treatment. For 3D only preliminary results could be provided. Measurements were only based on phantoms and have to be repeated with real clinical data.
CRIS-Team Scopus Importer:2019-05-14

Medical imaging modalities - Unberath, Mathias; Maier, Andreas (Elsevier, 2023)
This chapter introduces the most common medical imaging modalities, including X-ray imaging, computed tomography, magnetic resonance imaging, functional imaging, ultrasound, and photoacoustic imaging. In addition to describing the energy-tissue interactions that determine image formation, we describe computational methods to describe image quality. Finally, we discuss clinical use cases and explore how different imaging modalities contribute to patient care in both diagnosis and treatment.
CRIS-Team Scopus Importer:2023-11-10
Keywords: CT; Image quality; Imaging; Medical imaging modalities; MRI; US; Vision

Medical Imaging Systems - Maier, Andreas; Steidl, Stefan; Christlein, Vincent; Hornegger, Joachim (Springer, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.introd

Medical Monkeys: A Crowdsourcing Approach to Medical Big Data - Servadei, Lorenzo; Schmidt, Rainer; Eidelloth, Christina; Maier, Andreas (On the Move to Meaningful Internet Systems. OTM 2017 Workshops, 2017)
UnivIS-Import:2019-08-15:Pub.2017.tech.IMMD.IMMD5.medica_7
Keywords: Free innovation; Medical Big Data; Medical innovation; eHealth; Gamification; Open innovation; Medical image segmentation

Melanoma Segmentation and Classification Employing Imaging and Graph Encodings - Rivera Monroy, Luis; Eberhardt, Martin; Ostalecki, Christian; Baur, Andreas; Vera, Julio; Maier, Andreas (Proceedings - 2022 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2022, 2022)
Histopathology imaging is crucial for the diagnosis and the decision on treating associated tissue diseases. For this reason, Computer-assisted approaches have gained popularity and shown promise in tasks such as segmentation and classification. However, collecting a significant amount of data and high-quality annotations remains a challenge. This work describes a pipeline for toponomy profiling on the population of cells that combines melanoma multidimensional fluorescent imaging, a supervised segmentation of the region of interest, and graph unsupervised embedded representation.The Multi-Epitope-Ligand Cartography (MELC) provides a range of data shown to be optimal for solving supervised and unsuper-vised tasks. This imaging technique and the methodology, such as the one presented here, are promising for further improvement in the study of diagnostic techniques and therapies for skin diseases, such as melanoma.
CRIS-Team Scopus Importer:2023-03-07
Keywords: melanoma; microscopy; neural networks; segmentation

Metal-conscious Embedding for CBCT Projection Inpainting - Fan, Fuxin; Wang, Yangkong; Ritschl, Ludwig; Biniazan, Ramyar; Beister, Marcel; Kreher, Björn W.; Huang, Yixing; Kappler, Steffen; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023)
The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. In order to reduce metal artifacts, projection in-painting is an essential step in many metal artifact reduction algorithms. In this work, a hybrid network combining the shift window (Swin) vision transformer (ViT) and a convolutional neural network is proposed as a baseline network for the inpainting task. To incorporate metal information for the Swin ViT-based encoder, metal-conscious self-embedding and neighborhood-embedding methods are investigated. Both methods have improved the performance of the baseline network. Furthermore, by choosing appropriate window size, the model with neighborhood-embedding could achieve the lowest mean absolute error of 0.079 in metal regions and the highest peak signal-to-noise ratio of 42.346 in CBCT projections. At the end, the efficiency of metal-conscious embedding on both simulated and real cadaver CBCT data has been demonstrated, where the inpainting capability of the baseline network has been enhanced.<br

Metal Inpainting in CBCT Projections Using Score-based Generative Model - Mei, Siyuan; Fan, Fuxin; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023)
During the orthopaedic surgery, the insertion of metallic implants or screws is often performed under mobile C-arm systems. However, due to the high attenuation of metals, severe metal artifacts occur in 3D reconstructions, which degrade the image quality significantly. Therefore, many metal artifact reduction algorithms have been developed to reduce the artifacts, and metal inpainting in the projection domain is an essential step. In this work, a score-based generative model is trained on simulated knee projections, and the in-painted images are obtained by removing the noise in the conditional resampling process. The result implies that the inpainted images by the score-based generative model have more detailed information and achieve the lowest mean absolute error of 0.069 and the highest peak-signal-to-noise ratio of 43.07 compared with the inverse distance weighting interpolation method and the mask pyramid network. Besides, the score-based model can also recover projections with large circular and rectangular masks, showing its generalization in in-painting tasks.<br

Method for comparison of data driven gating algorithms in emission tomography - Reymann, Maximilian; Vija, A. Hans; Maier, Andreas (Physics in Medicine and Biology, 2023)
Objective: Multiple algorithms have been proposed for Data Driven Gating (DDG) in Single Photon Emission Computed Tomography (SPECT) and have successfully been applied to Myocardial Perfusion Imaging (MPI). Application of DDG to acquisition types other than SPECT MPI has not been demonstrated so far, as limitations and pitfalls of current methods are unknown. Approach: We create a comprehensive set of phantoms simulating the influence of different motion artifacts, view angles, moving objects, contrast, and count levels in SPECT. We perform Monte Carlo Simulation (MCS) of the phantoms, allowing the characterization of DDG algorithms using quantitative metrics derived from the data and evaluate the Center of Light (COL) and Laplacian Eigenmaps (LE) methods as sample DDG algorithms. Main results: View angle, object size, count rate density, and contrast influence the accuracy of both DDG methods. Moreover, the ability to extract the respiratory motion in the phantom was shown to correlate with the contrast of the moving feature to the background, the Signal to Noise ratio, and the noise in the data. Significance: We showed that reporting the average correlation to an external physical reference signal per acquisition is not sufficient to characterize DDG methods. Assessing DDG methods on a view-by-view basis using the simulations and metrics from this work could enable the identification of pitfalls of current methods, and extend their application to acquisitions beyond SPECT MPI.

Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration - Schaffert, Roman; Wang, Jian; Fischer, Peter; Borsdorf, Anja; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
Registration of pre-operative 3-D volumes to intra-operative 2-D X-ray images is important in minimally invasive medical procedures. Rigid registration can be performed by estimating a global rigid motion that optimizes the alignment of local correspondences. However, inaccurate correspondences challenge the registration performance. To minimize their influence, we estimate optimal weights for correspondences using PointNet. We train the network directly with the criterion to minimize the registration error. We propose an objective function which includes point-to-plane correspondence-based motion estimation and projection error computation, thereby enabling the learning of a weighting strategy that optimally fits the underlying formulation of the registration task in an end-to-end fashion. For single-vertebra registration, we achieve an accuracy of ± mm and highly improved robustness. The success rate is increased from 79.3% to 94.3% and the capture range from 3 mm to 13 mm.
CRIS-Team Scopus Importer:2019-06-21
Keywords: 2-D/3-D registration; Deep learning; Medical image registration; Point-to-plane correspondence model

Microscopy - Mualla, Firas; Aubreville, Marc; Maier, Andreas (Medical Imaging Systems: An Introductory Guide, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.micros

Mind the Gap: Scanner-Induced Domain Shifts Pose Challenges for Representation Learning in Histopathology - Wilm, Frauke; Fragoso, Marco; Bertram, Christof A.; Stathonikos, Nikolas; Ottl, Mathias; Qiu, Jingna; Klopfleisch, Robert; Maier, Andreas; Aubreville, Marc; Breininger, Katharina (Proceedings - International Symposium on Biomedical Imaging, 2023)
Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably. We investigated the potential of using self-supervised pre-training to overcome scanner-induced domain shifts for the downstream task of tumor segmentation. For this, we present the Barlow Triplets to learn scanner-invariant representations from a multi-scanner dataset with local image correspondences. We show that self-supervised pre-training successfully aligned different scanner representations, which, interestingly only results in a limited benefit for our downstream task. We thereby provide insights into the influence of scanner characteristics for downstream applications and contribute to a better understanding of why established self-supervised methods have not yet shown the same success on histopathology data as they have for natural images.
Created from Fastlane, Scopus look-up
Keywords: Barlow Twins; Domain Shift; Histopathology; Representation Learning

Mitigating Medialness Responses from Non-tubular Structures Using Entropy - Unberath, Mathias; Goppert, Eric; Taubmann, Oliver; Maier, Andreas (Computer Assisted Radiology and Surgery (CARS) 2016: Proceedings of the 30th Congress and Exhibition, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.mitiga

Mitosis domain generalization in histopathology images — The MIDOG challenge - Aubreville, Marc; Stathonikos, Nikolas; Bertram, Christof A.; Klopfleisch, Robert; ter Hoeve, Natalie; Ciompi, Francesco; Wilm, Frauke; Marzahl, Christian; Donovan, Taryn A.; Maier, Andreas; Breen, Jack; Ravikumar, Nishant; et al. (Medical Image Analysis, 2023)
The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an F1 score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task.
CRIS-Team Scopus Importer:2022-12-16
Keywords: Challenge; Deep Learning; Domain generalization; Histopathology; Mitosis

Modularization of deep networks allows cross-modality reuse: lesson learnt - Fu, Weilin; Husvogt, Lennart; Ploner, Stefan; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2020)
Fundus photography and Optical Coherence Tomography Angiography (OCT-A) are two commonly used modalities in ophthalmic imaging. With the development of deep learning algorithms, fundus image processing, especially retinal vessel segmentation, has been extensively studied. Built upon the known operator theory, interpretable deep network pipelines with well-defined modules have been constructed on fundus images. In this work, we firstly train a modularized network pipeline for the task of retinal vessel segmentation on the fundus database DRIVE. The pretrained preprocessing module from the pipeline is then directly transferred onto OCT-A data for image quality enhancement without further fine-tuning. Output images show that the preprocessing net can balance the contrast, suppress noise and thereby produce vessel trees with improved connectivity in both image modalities. The visual impression is confirmed by an observer study with five OCT-A experts. Statistics of the grades by the experts indicate that the transferred module improves both the image quality and the diagnostic quality. Our work provides an example that modules within network pipelines that are built upon the known operator theory facilitate cross-modality reuse without additional training or transfer learning.
CRIS-Team Scopus Importer:2020-04-21

Module-Power Prediction from PL Measurements using Deep Learning - Hoffmann, Mathis; Hepp, Johannes; Doll, Bernd; Buerhop-Lutz, Claudia; Peters, Ian Marius; Brabec, Christoph; Maier, Andreas; Christlein, Vincent (IEEE Photovoltaic Specialists Conference, 2021)
The individual causes for power loss of photovoltaic modules are investigated for quite some time. Recently, it has been shown that the power loss of a module is, for example, related to the fraction of inactive areas. While these areas can be easily identified from electroluminescense (EL) images, this is much harder for photoluminescence (PL) images. With this work, we close the gap between power regression from EL and PL images. We apply a deep convolutional neural network to predict the module power from PL images with a mean absolute error (MAE) of 4.4% or 11.7WP. Furthermore, we depict that regression maps computed from the embeddings of the trained network can be used to compute the localized power loss. Finally, we show that these regression maps can be used to identify inactive regions in PL images as wel
Keywords: photoluminescence; power; regression; deep learning; weakly supervised

Monocular multi-person pose estimation: A survey - Dos Reis, Eduardo Souza; Seewald, Lucas Adams; Antunes, Rodolfo Stoffel; Rodrigues, Vinicius Facco; Righi, Rodrigo Da Rosa; Da Costa, Cristiano Andre; Da Silveira Jr, Luiz Gonzaga; Eskofier, Björn; Maier, Andreas; Horz, Tim; Fahrig, Rebecca (Pattern Recognition, 2021)
Multi-person pose estimation in unconstrained scenarios, with an unknown number of individuals, is a main step towards scene understanding and action recognition. Due to the recent advancements on the architecture of convolutional networks, body part detectors are now accurate and estimate poses in real-time for both single-and multi-person scenes. In contrast, assigning detected body parts to coherent human poses when there are multiple persons interacting is an arduous task. To name a few of the challenges that arise in such scenes: person-to-person occlusion, truncated body parts, and more sources for double counting. Recently, the community contributed towards solving most of them. Hence, it would be interesting to analyze and compile successful approaches from current literature into research trends, and identify possible gaps for future works. To the best of our knowledge, there is no up-to-date review on the main advancements in the field that target this particular set of challenges. This survey fills this gap by reviewing the main breakthroughs on multi-person pose estimation over the last decade and summarizing their impact on the state-of-the-art. Regarding scientific contributions, we propose a novel taxonomy that categorizes the reviewed methods according to their main contributions to the pose estimation pipeline, lists the main datasets and evaluation metrics to train new models, and provides insights on the best entries of publicly available benchmarks. (c) 2021 Elsevier Ltd. All rights reserved.
CRIS-Team WoS Importer:2021-06-25
Keywords: Human pose estimation; Multi-person pose estimation; Markerless body part detectors; Human pose tracking

Monte Carlo Dose Simulation for In-Vivo X-Ray Nanoscopy - Wagner, Fabian; Thies, Mareike; Karolczak, Marek; Pechmann, Sabrina; Huang, Yixing; Gu, Mingxuan; Kling, Lasse; Weidner, Daniela; Aust, Oliver; Schett, Georg; Christiansen, Silke; Maier, Andreas (Informatik aktuell, 2022)
CRIS-Team Scopus Importer:2022-05-06

MoPED: Motion Parameter Estimation DenseNet for accelerating retrospective motion correction - Hoßbach, Julian; Splitthoff, Daniel; Cauley, Stephen; Maier, Andreas (ISMRM & SMRT Virtual Conference & Exhibition, 2020)

Motion Artifact Detection in Confocal Laser Endomicroscopy Images - Stöve, Maike; Aubreville, Marc; Oetter, Nicolai; Knipfer, Christian; Neumann, Helmut; Stelzle, Florian; Maier, Andreas (Bildverarbeitung für die Medizin 2018. Informatik aktuell., 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.motion

Motion Compensated Fan-Beam CT by Enforcing Fourier Properties of the Sinogram - Berger, Martin; Maier, Andreas; Xia, Yan; 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.motion_3
Keywords: GRK-1773

Motion Compensation for Cone-Beam CT using Fourier Consistency Conditions - Berger, Martin; Xia, Yan; Aichinger, Wolfgang; Mentl, Katrin; Unberath, Mathias; Aichert, André; Riess, Christian; Hornegger, Joachim; Fahrig, Rebecca; Maier, Andreas (Physics in Medicine and Biology, 2017)
Keywords: cone-beam CT; Fourier consistency conditions; motion correction; motion estimation

Motion Compensation using Range Imaging in C-arm Cone-Beam CT - Bier, Bastian; Unberath, Mathias; Geimer, Tobias; Maier, Jennifer; Gold, Garry; Levenston, Marc; Fahrig, Rebecca; Maier, Andreas (Medical Image Understanding and Analysis, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.motion_1

Motion correction and volume merging of ultrahigh resolution OCT enable 3D visualization and longitudinal tracking of hyperreflective foci - Won, Jungeun; Yaghy, Antonio; Ploner, Stefan; Takahashi, Hiroyuki; Reimann, Marcel Kim; Girgis, Jessica M.; Lam, Kenneth; Hwang, Yunchan; Chen, Siyu; Maier, Andreas; Waheed, Nadia K.; Fujimoto, James G. (Investigative Ophthalmology & Visual Science, 2023)

Motion Estimation in Rotational Angiography with alpha–Expansion Moves - Felsner, Lina; Aichert, André; Unberath, Mathias; Maier, Andreas (IEEE Nuclear Science Symposium and Medical Imaging Conference, 2017)