Cone-beam tomography with linearly distorted source trajectories - Dennerlein, Frank; Noo, Frédéric; Hoppe, Stefan; Hornegger, Joachim; Lauritsch, Günter (2006 IEEE Nuclear Science Symposium, Medical Imaging Conference and 15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors, Special Focus Workshops, NSS/MIC/RTSD, 2007)
A major drawback of Katsevich's exact general cone-beam inversion scheme is the difficulty in finding practical algorithms adapted to every novel type of source trajectory. We succeeded to overcome this problem, and can formulate reconstruction algorithms, for trajectories that are related through a linear distortion to an already studied scenario. The introduced theory yields two reconstruction strategies that are in principle independent from the underlying reconstruction algorithm and are either based on data pre- and post-processing or on adjustment of filtering directions. Numerical results based on simulated cone-beam data are presented. © 2006 IEEE.

Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution - Bercea, Cosmin I.; Maier, Andreas; Köhler, Thomas (Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.confid
Keywords: Levenberg-Marquardt optimization; Motion estimation; Super-Resolütion

CONRAD - A software framework for cone-beam imaging in radiology - Maier, Andreas; Hofmann, Hannes; Berger, Martin; Fischer, Peter; Schwemmer, Chris; Wu, Haibo; Müller, Kerstin; Hornegger, Joachim; Choi, Jang-Hwan; Riess, Christian; Keil, Andreas; Fahrig, Rebecca (Medical Physics, 2013)

Consistency-based Respiratory Motion Estimation in Rotational Angiography - Unberath, Mathias; Aichert, André; Achenbach, Stephan; Maier, Andreas (Medical Physics, 2017)
Purpose: Rotational coronary angiography enables 3D reconstruction but suffers from intra-scan cardiac and respiratory motion. While gating handles cardiac motion, respiratory motion requires compensation. State-of-the-art algorithms rely on 3D-2D registration that depends on initial reconstructions of sufficient quality. We propose a compensation method that is applied directly in projection domain. It overcomes the need for reconstruction and thus complements the state-of-the-art. Methods: Virtual single-frame background subtraction based on vessel segmentation and spectral deconvolution yields non-truncated images of the contrasted lumen. This allows motion compensation based on data consistency conditions. We compensate craniocaudal shifts by optimizing epipolar consistency to (a) devise an image-based surrogate for cardiac motion and (b) compensate for respiratory motion. We validate our approach in two numerical phantom studies and three clinical cases. Results: Correlation of the image-based surrogate for cardiac motion with the ECG-based ground truth was excellent yielding a Pearson correlation of 0.93 ± 0.04. Considering motion compensation, the target error measure decreased by 98% and 69%, respectively, for the phantom experiments while for the clinical cases the same figure of merit improved by 46 ± 21%. Conclusions: The proposed method is entirely image-based and accurately estimates craniocaudal shifts due to respiration and cardiac contraction. Future work will investigate experimental trajectories and possibilities for simplification of the single-frame subtraction pipeline.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.consis
Keywords: cone-beam CT; inpainting; motion correction; vessel segmentation

Consistency Conditions, Compressed Sensing and Machine Learning for Limited Angle Tomography - Huang, Yixing (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020)
Cone-beam computed tomography (CBCT) is a widely used imaging technology for medical diagnosis and interventions nowadays. Compared with conventional 2-D X-ray images, 3-D volumes reconstructed by CBCT provide cross sectional images and offer much better visualization of patients' anatomical structures and disease information. In clinical applications, angiographic C-arm devices are commonly used to acquire 3-D images for image guided therapies. To acquire necessary data for image reconstruction, typically a short scan is performed. However, in practice, the gantry rotation of a C-arm system might be restricted by other system parts or external obstacles. In these cases, the limited angle reconstruction problem arises. Image reconstruction from data acquired in an insufficient angular range is called limited angle tomography. Due to missing data, artifacts, typically in the form of streaks, will degrade image quality, causing boundary distortion, edge blurry, and intensity bias. These artifacts may lead to misinterpretation of images. Therefore, artifact reduction in limited angle tomography has essential clinical value. In the course of this thesis, various algorithms are proposed to deal with data insufficiency in limited angle tomography. The first category of algorithms are to restore missing data based on data consistency conditions. Specifically, iterative Papoulis-Gerchberg algorithms based on the well known Helgason-Ludwig consistency condition (HLCC) and Fourier consistency conditions of sinograms and a regression-filtering-fusion method based on HLCC are proposed. These algorithms are able to reduce most artifacts for the Shepp-Logan phantom and a clinical image. However, it is suited for parallel-beam limited angle tomography only since HLCC is derived in parallel-beam computed tomography. The second category of algorithms are to use compressed sensing technologies. The iterative reweighted total variation (wTV) algorithm is adapted for the use in limited angle tomography. While it is able to reduce small streaks well, it is rather inept at reducing large streaks due to scale limitation. Therefore, two implementations of scale-space anisotropic total variation (ssaTV) are proposed to overcome the limitations of wTV. Both ssaTV implementations take the advantage of scale-space optimization and the anisotropic distribution of streak orientations in limited angle tomography. Therefore, they both reduce streak artifacts more effectively and efficiently than wTV. The last category of algorithms are using machine learning techniques, including traditional machine learning and deep learning. In the traditional machine learning part, reduced error-pruning tree (REPTree) is proposed to predict artifacts from extracted mean-variance-median, Hessian, and shift variant data loss features. Although REPTree achieves very good performance on the validation Shepp-Logan data, it still requires further improvement for clinical applications. Instead, deep learning, particularly the U-Net, achieves very impressive results in 120-degree cone-beam limited angle tomography on clinical data. However, its robustness is still a concern. Our experiments demonstrate that the U-Net is sensitive to Poisson noise and adversarial examples. The robustness to Poisson noise can be improved by retraining on data with noise. However, the retrained U-Net model is still susceptible to adversarial examples. To make the U-Net robust, data consistency deep learning reconstruction is proposed, which utilizes deep learning reconstructions as prior images and further constrains them consistent to measured projection data to improve image qualit
Keywords: limited angle tomography; consistency conditions; compressed sensing; machine learning; deep learning

Consonant-to-Vowel/Vowel-to-Consonant Transitions to Analyze the Speech of Cochlear Implant Users. - Arias Vergara, Tomás; Gollwitzer, Sandra; Orozco Arroyave, Juan Rafael; Schuster, Maria; Nöth, Elmar (Lecture Notes in Computer Science, 2019)
People with postlingual onset of deafness often present speech production problems even after hearing rehabilitation by cochlear implantation. In this paper, the speech of 20 postlingual (aged between 33 and 78 years old) and 20 healthy control (aged between 31 and 62 years old) German native speakers is analyzed considering acoustic features extracted from Consonant-to-Vowel (CV) and Vowel-to-Consonant (VC) transitions. The transitions are analyzed with reference to the manner of articulation of consonants according to 5 groups: nasals, sibilants, fricatives, voiced stops, and voiceless stops. Automatic classification between cochlear implant (CI) users and healthy speakers shows accuracies of up to 93%. Considering CV transitions, it is possible to detect specific features of altered speech of CI users. More features are to be evaluated in the future. A comprehensive evaluation of speech changes of CI users will help in the rehabilitation after deafenin
CRIS-Team Scopus Importer:2019-10-15
Keywords: Hearing loss; Acoustic analysis; automatic classification; Cochlear implant

Constrained 2-D/3-D registration for motion compensation in AFib ablation procedures - Brost, Alexander; Wimmer, Andreas; Liao, Rui; Hornegger, Joachim; Strobel, Norbert (Springer-verlag, 2011)
Fluoroscopic overlay images rendered from pre-operative volumetric data can provide additional guidance for physicians during catheter ablation procedures for treatment of atrial fibrillation (AFib). As these overlay images are compromised by cardiac and respiratory motion, motion compensation methods have been proposed. The approaches so far either require simultaneous biplane imaging for 3-D motion compensation or, in case of mono-plane X-ray imaging, provide only a limited 2-D functionality. To overcome the downsides of the previously suggested methods, we propose a new approach that facilitates full 3-D motion compensation even if only mono-plane X-ray views are available. To this end, we use constrained model-based 2-D/3-D registration to track a circumferential mapping catheter which is commonly used during AFib catheter ablation procedures. Our approach yields an average 2-D tracking error of 0.6 mm and an average 3-D tracking error of 2.1 mm. © 2011 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD5.constr_6

Constrained registration for motion compensation in atrial fibrillation ablation procedures - Brost, Alexander; Wimmer, Andreas; Liao, Rui; Bourier, Felix; Koch, Martin; Strobel, Norbert; Kurzidim, Klaus; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2012)
Fluoroscopic overlay images rendered from preoperative volumetric data can provide additional anatomical details to guide physicians during catheter ablation procedures for treatment of atrial fibrillation (AFib). As these overlay images are often compromised by cardiac and respiratory motion, motion compensation methods are needed to keep the overlay images in sync with the fluoroscopic images. So far, these approaches have either required simultaneous biplane imaging for 3-D motion compensation, or in case of monoplane X-ray imaging, provided only a limited 2-D functionality. To overcome the downsides of the previously suggested methods, we propose an approach that facilitates a full 3-D motion compensation even if only monoplane X-ray images are available. To this end, we use a training phase that employs a biplane sequence to establish a patient specific motion model. Afterwards, a constrained model-based 2-D/3-D registration method is used to track a circumferential mapping catheter. This device is commonly used for AFib catheter ablation procedures. Based on the experiments on real patient data, we found that our constrained monoplane 2-D/3-D registration outperformed the unconstrained counterpart and yielded an average 2-D tracking error of 0.6 mm and an average 3-D tracking error of 1.6 mm. The unconstrained 2-D/3-D registration technique yielded a similar 2-D performance, but the 3-D tracking error increased to 3.2 mm mostly due to wrongly estimated 3-D motion components in X-ray view direction. Compared to the conventional 2-D monoplane method, the proposed method provides a more seamless workflow by removing the need for catheter model re-initialization otherwise required when the C-arm view orientation changes. In addition, the proposed method can be straightforwardly combined with the previously introduced biplane motion compensation technique to obtain a good trade-off between accuracy and radiation dose reduction. © 2011 IEEE.

Constraint-Based Time-Scale Modification of Music Recordings for Noise Beautification - Müller, Meinard; Hedwig, Helmut; Zalkow, Frank; Popescu, Stefan (Applied Sciences, 2018)

Constriction of cone-beam artifacts by the Z-smart reconstruction method - Dennerlein, Frank; Noo, Frédéric; Härer, Wolfgang; Hornegger, Joachim; Lauritsch, Günter (2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC, 2007)
We introduce a novel approach for 3D image reconstruction from CB data acquired along a full circular trajectory. Our approach, which we refer to as the Z-smart reconstruction method, follows the scheme of 1D filtering and 3D backprojection, while providing flexibility in the choice of the filtering directions. This flexibility allows us to modify the appearance of CB artifacts in the reconstruction results, so that, for certain imaging tasks, the Z-smart method can yield image quality that is significantly superior to that achievable with most other reconstruction algorithms for the full-scan circular trajectory. In particular, when imaging objects that have strong but localized heterogeneities in the axial direction, the Z-smart method can outperform both, the popular filtered-backprojection approach of Feldkamp et al., and also ART, i.e. a fully 3D iterative reconstruction method. We provide a numerical evaluation of our algorithm using simulated CB data of an analytically defined tube phantom, with and without added noise. © 2007 IEEE.

Content-Dependent Spatio-Temporal Video Watermarking using 3-Dimensional Discrete Cosine Transform - Setyawan, Iwan; Timotius, Ivanna (The 5th International Conference on Information Technology and Electrical Engineering (ICITEE 2013), 2013)
In this paper we propose a content-dependent spatio-temporal watermarking scheme for digital videos. Content dependency is achieved by incorporating the hash of the video sequence into the watermark. The video sequence is treated as a 3-dimensional spatio-temporal signal for the purposes of video hash computation and watermark embedding and detection. Our experiments show that the video hash algorithm has good discriminating power and robustness against various attacks. The watermark is also shown in the experiments to have good robustness against a variety of attacks, in particular when the watermark is copied from one video sequence to another.
Keywords: digital video watermarking; content-dependent watermarking; copy attack; video hashing

Context-Sensitive Evaluation and Correction of Phone Recognition Output - Levit, Michael; Alshawi, H.; Gorin, Allen; Nöth, Elmar (Proc. European Conf. on Speech Communication and Technology, 2003)

Continuous Short-term Irradiance Forecasts using Sky Images - Bernecker, David; Riess, Christian; Angelopoulou, Elli; Hornegger, Joachim (Solar Energy, 2014)

Contrast-based 3D/2D registration of the left atrium: Fast versus consistent - Hoffmann, Matthias; Kowalewski, Christopher; Maier, Andreas; Kurzidim, Klaus; Strobel, Norbert; Hornegger, Joachim (International Journal of Biomedical Imaging, 2016)
For augmented fluoroscopy during cardiac ablation, a preoperatively acquired 3D model of a patient's left atrium (LA) can be registered to X-ray images recorded during a contrast agent (CA) injection. An automatic registration method that works also for small amounts of CA is desired. We propose two similarity measures: The first focuses on edges of the patient anatomy. The second computes a contrast agent distribution estimate (CADE) inside the 3D model and rates its consistency with the CA as seen in biplane fluoroscopic images. Moreover, temporal filtering on the obtained registration results of a sequence is applied using a Markov chain framework. Evaluation was performed on 11 well-contrasted clinical angiographic sequences and 10 additional sequences with less CA. For well-contrasted sequences, the error for all 73 frames was 7.9 ± 6.3 mm and it dropped to 4.6 ± 4.0 mm when registering to an automatically selected, well enhanced frame in each sequence. Temporal filtering reduced the error for all frames from 7.9 ± 6.3 mm to 5.7 ± 4.6 mm. The error was typically higher if less CA was used. A combination of both similarity measures outperforms a previously proposed similarity measure. The mean accuracy for well contrasted sequences is in the range of other proposed manual registration methods.

Contrast-based 3-D/2-D Registration of the Left Atrium: Fast vs. Consistent - Hoffmann, Matthias; Kowalewski, Christopher; Maier, Andreas; Kurzidim, Klaus; Strobel, Norbert; Hornegger, Joachim (International Journal of Biomedical Imaging, 2016)
UnivIS-Import:2018-09-05:Pub.2016.tech.IMMD.IMMD5.contra_0

Contrast-Based Registration Of Left Atria To Fluoroscopic Image Sequences By Temporal Markow Filtering And Motion Regularization - Hoffmann, Matthias; Strobel, Norbert; Hornegger, Joachim; Maier, Andreas (Proceedings of the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.contra

Contributions of Time-of-Flight cameras for biomedical applications - Höller, Kurt Emmerich; Schaller, Christian; Tacke, Dominik; Höpfl, Florian; Hornegger, Joachim (Innovationen bei der Erfassung und Analyse bioelektrischer und biomagnetischer Signale, 2008)

Convex Temporal Regularizers in Cardiac C-arm CT - Taubmann, Oliver; Lauritsch, Günter; Krings, Gregor; Maier, Andreas (Proceedings of the 4th International Conference on Image Formation in X-ray Computed Tomography, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.convex_0

Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson’s Disease from Speech in Three Different Languages - Vasquez Correa, Juan; Arias Vergara, Tomás; Rios-Urrego, Cristian D.; Schuster, Maria; Rusz, Jan; Orozco Arroyave, Juan Rafael; Nöth, Elmar (Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2019)

Convolutional neural network to model articulation impairments in patients with Parkinson's disease - Vasquez Correa, Juan; Orozco Arroyave, Juan Rafael; Nöth, Elmar (18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017, 2017)
Keywords: Articulation; Parkinson's disease; Convolutional neural network; Wavelet transform; Time-frequency representations

Convolution-Based Truncation Correction for C-Arm CT Using Scattered Radiation - Bier, Bastian; Schwemmer, Chris; Maier, Andreas; Hofmann, Hannes; Xia, Yan; Hornegger, Joachim; Struffert, Tobias (Bildverarbeitung für die Medizin 2013, 2013)

COPD classification in CT images using a 3D convolutional neural network - Ahmed, Jalil; Vesal, Sulaiman; Durlak, Felix; Kaergel, Rainer; Ravikumar, Nishant; Rémy-Jardin, Martine; Maier, Andreas (Informatik aktuell, 2020)
Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. Early detection and diagnosis of COPD can increase the survival rate and reduce the risk of COPD progression in patients. Currently, the primary examination tool to diagnose COPD is spirometry. However, computed tomography (CT) is used for detecting symptoms and sub-type classification of COPD. Using different imaging modalities is a diffcult and tedious task even for physicians and is subjective to inter-and intra-observer variations. Hence, developing methods that can automatically classify COPD versus healthy patients is of great interest. In this paper, we propose a 3D deep learning approach to classify COPD and emphysema using volume-wise annotations only. We also demonstrate the impact of transfer learning on the classification of emphysema using knowledge transfer from a pre-trained COPD classification model.
CRIS-Team Scopus Importer:2020-04-21

Coping with Real World Data: Artifact Reduction and Denoising for Motion-Compensated Cardiac C-arm CT - Taubmann, Oliver; Maier, Andreas; Hornegger, Joachim; Lauritsch, Günter; Fahrig, Rebecca (Medical Physics, 2016)
UnivIS-Import:2016-02-10:Pub.2016.tech.IMMD.IMMD5.coping
Keywords: Artifact Reduction; Motion-Compensated Reconstruction; 4-D Imaging; Cardiac C-arm CT

CoroEval: a multi-platform, multi-modality tool for the evaluation of 3D coronary vessel reconstructions - Schwemmer, Chris; Forman, Christoph; Wetzl, Jens; Maier, Andreas; Hornegger, Joachim (Physics in Medicine and Biology, 2014)
With default settings, measurements are taken at 1 mm intervals along the vessel centreline and from 10 different angles at each measurement point. This allows for outlier detection and noise-robust measurements without the burden and subjectivity a manual measurement process would incur. Graphical measurement results can be directly exported to vector or bitmap graphics for integration into scientific publications. Centreline and lumen segmentations can be exported as point clouds and in various mesh formats. We evaluated the diameter measurement process using three phantom datasets. An average deviation of 0.03 +/- 0.03 mm was found.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.coroev
Keywords: medical imaging; coronary vessels; evaluation; software

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

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

Coronary Sinus Extraction for Multimodality Registration to guide Transseptal Puncture - Bourier, Felix; Brost, Alexander; Yatziv, Liron; Hornegger, Joachim; Strobel, Norbert; Kurzidim, Klaus (8th Interventional MRI Symposium - Book of Abstracts, 2010)

Correction of Intensity Inhomogeneities Utilizing Histogram-based Regularization - Jäger, Florian; Balda, Michael; Hornegger, Joachim (Proceedings of the 4th Russian-Bavarian Conference on Biomedical Engineering at Moscow Institute of Electronic Technology, 2008)

Correction to: Assessment of treatment responses in children and adolescents with Ewing sarcoma with metabolic tumor parameters derived from 18F-FDG-PET/CT and circulating tumor DNA (European Journal of Nuclear Medicine and Molecular Imaging, (2019), 10.1007/s00259-019-04649-1) - Schmidkonz, Christian; Krumbholz, Manuela; Atzinger, Armin; Cordes, Michael; Götz, Theresa; Prante, Olaf; Ritt, Philipp; Schaefer, Christiane; Agaimy, Abbas; Hartmann, Wolfgang; Roessig, Claudia; Froehlich, Birgit; et al. (European Journal of Nuclear Medicine and Molecular Imaging, 2020)
The original version on this paper contained an error. The author names, though correctly appeared in the PDF version, were incorrectly displayed in indexing sites. This error occurred because of the incorrect tagging of their names in themetadata of this article. The purpose of this Erratum is the correction of metadata. Therefore, indexing sites should now reflect their names as Schmidkonz C, Krumbholz M, Atzinger A, Cordes M, Goetz TI, Prante O, Ritt P, Schaefer C, Agaimy A, Hartmann W, Rössig C, Fröhlich B, Bäuerle T, Dirksen U, Kuwert T, Metzler M. The original article has been corrected.
CRIS-Team Scopus Importer:2020-02-25

Correlation Filter Based Visual Trackers for Person Pursuit Using a Low-Cost Quadrotor - Haag, Klaus; Deitsch, Sergiu; Gallwitz, Florian (15th International Conference on Innovations for Community Services (I4CS), 2015)
We propose two model-free visual object trackers for following targets using the low-cost quadrotor Parrot AR.Drone 2.0 at low altitudes. Our trackers employ correlation filters for short-term tracking and a redetection strategy based on tracking-learning-detection (TLD). We performed an extensive quantitative evaluation of our trackers and a wide variety of existing trackers on person pursuit sequences. The results show that our trackers outperform the existing trackers. In addition, we demonstrate the applicability of our proposed trackers in a series of flight experiments in unconstrained environments using human targets and an existing visual servoing controller.
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.correl
Keywords: Correlation; Decision support systems; Detectors; Discrete Fourier transforms; Target tracking; GRK-1773

Correlative imaging in cardiology. Principles and clinical application - Kuwert, Torsten; Römer, Wolfgang; Hornegger, Joachim (Herz, 2007)
Technical progress has engendered a broad spectrum of methods to image cardiac structure, function, and metabolism. Frequently, the imaging tools available provide complementary information. Therefore, methods to integrate image information from different modalities into one common coordinate system are increasingly receiving attention also in cardiology. Currently available technology includes software-based image fusion as well as hardware-based registration of datasets. The latter capitalizes on so-called hybrid cameras combining detectors of different modalities in one gantry. Hardware-based image fusion is, to date, anatomically more accurate than the software-based methodology. However, the anatomic accuracy of both approaches is still far from perfect. This is, in particular, due to artifacts caused by respiratory movements also affecting the heart. The clinical potential of correlative imaging of the heart includes an improvement of accuracy in diagnosing hemodynamically significant coronary artery disease using single-photon emission computed tomography (SPECT). This is due to the possibility to correct myocardial scintigraphy for attenuation artifacts using registered X-ray computerized tomographic (CT) images. The visualization of coronary anatomy and myocardial perfusion in one imaging session using hybrid systems combining CT with positron emission tomography (PET) or SPECT is also an interesting option. Nevertheless, larger clinical trials investigating its usefulness are still missing. The possibility to match structure with radioactivity concentration is essential for approaches to image the molecular composition of atherosclerotic plaques and their stability. © 2007 Urban & Vogel München.

Cross-Corpus Classification of Realistic Emotions - Some Pilot Experiments - Eyben, Florian; Batliner, Anton; Schuller, Björn; Seppi, Dino; Steidl, Stefan (Proc. of theThird International Workshop on EMOTION (satellite of LREC): CORPORA FOR RESEARCH ON EMOTION AND AFFECT, 2010)
We use four speech databases with realistic, non-prompted emotions, and a large state-of-the-art acoustic feature vector, for cross-corpus classifications, in turn employing three databases for training and the fourth for testing. Categorical and continuous (dimensional) annotation is mapped onto a representation of valence with the three classes positive, neutral, and negative. This cross-corpus classification is compared with within corpus classifications. We interpret performance and most important features.

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

Cross-modality assessment and planning for pulmonary trunk treatment using CT and MRI imaging - Vitanovski, Dime; Tsymbal, Alexey; Ionasec, Razvan; Georgescu, Bogdan; Huber, Martin; Taylor, Andrew; Schievano, Silvia; Zhou, Shaohua K.; Hornegger, Joachim; Comaniciu, Dorin (Springer-verlag, 2010)
Congenital heart defect is the primary cause of death in newborns, due to typically complex malformation of the cardiac system. The pulmonary valve and trunk are often affected and require complex clinical management and in most cases surgical or interventional treatment. While minimal invasive methods are emerging, non-invasive imaging-based assessment tools become crucial components in the clinical setting. For advanced evaluation and therapy planning purposes, cardiac Computed Tomography (CT) and cardiac Magnetic Resonance Imaging (cMRI) are important non-invasive investigation techniques with complementary properties. Although, characterized by high temporal resolution, cMRI does not cover the full motion of the pulmonary trunk. The sparse cMRI data acquired in this context include only one 3D scan of the heart in the end-diastolic phase and two 2D planes (long and short axes) over the whole cardiac cycle. In this paper we present a cross-modality framework for the evaluation of the pulmonary trunk, which combines the advantages of both, cardiac CT and cMRI. A patient-specific model is estimated from both modalities using hierarchical learning-based techniques. The pulmonary trunk model is exploited within a novel dynamic regression-based reconstruction to infer the incomplete cMRI temporal information. Extensive experiments performed on 72 cardiac CT and 74 cMRI sequences demonstrated the average speed of 110 seconds and accuracy of 1.4mm for the proposed approach. To the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from sparse 4D cMRI data. © 2010 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD5.crossm

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

Cryo-balloon catheter localization in fluoroscopic images - Kurzendorfer, Tanja; Brost, Alexander; Jakob, Carolin; Mewes, Philip; Bourier, Felix; Koch, Martin; Kurzidim, Klaus; Hornegger, Joachim; Strobel, Norbert (SPIE Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 2013)

Cryo-balloon catheter position planning using AFiT - Brost, Alexander; Kleinoeder, Andreas; Brost, Alexander; Bourier, Felix; Koch, Martin; Kurzidim, Klaus; Hornegger, Joachim; Strobel, Norbert (Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 2012)
Atrial fibrillation (AFib) is the most common heart arrhythmia. In certain situations, it can result in life-threatening complications such as stroke and heart failure. For paroxsysmal AFib, pulmonary vein isolation (PVI) by catheter ablation is the recommended choice of treatment if drug therapy fails. During minimally invasive procedures, electrically active tissue around the pulmonary veins is destroyed by either applying heat or cryothermal energy to the tissue. The procedure is usually performed in electrophysiology labs under fluoroscopic guidance. Besides radio-frequency catheter ablation devices, so-called single-shot devices, e.g., the cryothermal balloon catheters, are receiving more and more interest in the electrophysiology (EP) community. Single-shot devices may be advantageous for certain cases, since they can simplify the creation of contiguous (gapless) lesion sets around the pulmonary vein which is needed to achieve PVI. In many cases, a 3-D (CT, MRI, or C-arm CT) image of a patient's left atrium is available. This data can then be used for planning purposes and for supporting catheter navigation during the procedure. Cryo-thermal balloon catheters are commercially available in two different sizes. We propose the Atrial Fibrillation Planning Tool (AFiT), which visualizes the segmented left atrium as well as multiple cryo-balloon catheters within a virtual reality, to find out how well cryo-balloons fit to the anatomy of a patient's left atrium. First evaluations have shown that AFiT helps physicians in two ways. First, they can better assess whether cryoballoon ablation or RF ablation is the treatment of choice at all. Second, they can select the proper-size cryo-balloon catheter with more confidence. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Cryo-Balloon Catheter Tracking in Atrial Fibrillation Ablation Procedures - Kurzendorfer, Tanja; Brost, Alexander; Bourier, Felix; Koch, Martin; Kurzidim, Klaus; Hornegger, Joachim; Strobel, Norbert (Bildverarbeitung für die Medizin 2012, 2012)

Cryo-balloon reconstruction from two views - Kleinöder, Andreas; Brost, Alexander; Bourier, Felix; Koch, Martin; Kurzidim, Klaus; Hornegger, Joachim; Strobel, Norbert (2011 18th IEEE International Conference on Image Processing, ICIP 2011, 2011)
Atrial fibrillation is a major cause of stroke. Its treatment is performed under fluoroscopic image guidance. Augmented fluoroscopy has become a useful tool during the ablation procedure for navigation under X-ray. Unfortunately, current navigation systems do not provide tools to localize and visualize a cryo-balloon catheters in 3-D. This is why we present a new approach to reconstruct the cryo-ballon catheter, modeled as a sphere, from two views. The reconstruction result can then be overlayed onto live fluoroscopic images during the procedure. In simulation studies, we compared our technique to a reference method. While both methods worked equally well on noise-free data, we found our method more reliable if the input data was affected by noise. For example, in the presence of noise with a standard deviation of 4 mm, our maximum 3-D reconstruction error was less than 1 mm. © 2011 IEEE.

CTA: Semiautomatische Quantifizierung von Karotisstenosen - Lell, Michael; Hornegger, Joachim; Scherl, Holger; Prümmer, Marcus; Anders, Katharina; Baum, Ulrich; Bautz, Werner (RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2005)

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

Current methods and new trends in signal processing and pattern recognition for the automatic assessment of motor impairments: the case of Parkinson's disease - Orozco-Arroyave, J. R.; Vasquez Correa, Juan; Nöth, Elmar (Neurological Disorders and Imaging Physics, Volume 5 Applications in dyslexia, epilepsy and Parkinson's, 2020)
This chapter describes current methods and future trends in signal processing and pattern recognition techniques applied to the automatic analysis of motor impairments observed in patients with Parkinson's disease (PD). A revision of the state-of-the-art is presented considering methods and applications that include the analysis of speech, gait, and handwriting signals for the evaluation of PD. The suitability of existing methods and the potential use of novel approaches are also presented in the context of the automatic diagnosis and monitoring of the disease progression. Apart from the classical clinical scales used to assess the neurological state of PD patients, specific scales designed to evaluate the speech impairments of the patients are also reviewed. Additionally, the suitability of a modified version of the Frenchay Dysarthria Assessment scale (m-FDA) is evaluated. Besides the classical signal processing methods based on the analysis in the time and frequency domains, other methods that consider non-linear features and also deep neural networks are explored in several experiment

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

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

Data-Driven Density Estimation applied to SPECT Subtraction Imaging for Epilepsy Diagnosis - Hahn, Dieter; Daum, Volker; Hornegger, Joachim; Kuwert, Torsten (Proceedings of the MICCAI Workshop on Probabilistic Models For Medical Image Analysis, 2009)

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

Data Mining in the U.S. National Toxicology Program (NTP) Database Reveals a Potential Bias Regarding Liver Tumors in Rodents Irrespective of the Test Agent - Ring, Matthias; Eskofier, Björn (PLoS ONE, 2015)
Long-term studies in rodents are the benchmark method to assess carcinogenicity of single substances, mixtures, and multi-compounds. In such a study, mice and rats are exposed to a test agent at different dose levels for a period of two years and the incidence of neoplastic lesions is observed. However, this two-year study is also expensive, time-consuming, and burdensome to the experimental animals. Consequently, various alternatives have been proposed in the literature to assess carcinogenicity on basis of short-term studies. In this paper, we investigated if effects on the rodents' liver weight in short-term studies can be exploited to predict the incidence of liver tumors in long-term studies. A set of 138 paired short- and long-term studies was compiled from the database of the U.S. National Toxicology Program (NTP), more precisely, from (long-term) two-year carcinogenicity studies and their preceding (short-term) dose finding studies. In this set, data mining methods revealed patterns that can predict the incidence of liver tumors with accuracies of over 80%. However, the results simultaneously indicated a potential bias regarding liver tumors in two-year NTP studies. The incidence of liver tumors does not only depend on the test agent but also on other confounding factors in the study design, e.g., species, sex, type of substance. We recommend considering this bias if the hazard or risk of a test agent is assessed on basis of a NTP carcinogenicity study.
UnivIS-Import:2015-03-11:Pub.2015.tech.IMMD.IMMD5.datami

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

Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details - Fieselmann, Andreas; Kowarschik, Markus; Ganguly, Arundhuti; Hornegger, Joachim; Fahrig, Rebecca (International Journal of Biomedical Imaging, 2011)

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

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

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

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

DeepDRR - A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures - Unberath, Mathias; Zaech, Jan-Nico; Lee, Sing Chun; Bier, Bastian; Fotouhi, Javad; Armand, Mehran; Navab, Nassir (MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV, 2018)
CRIS-Team WoS Importer:2019-08-09

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

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

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

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

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

Deep Learning Approach to Parkinson's Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification - Wodzinski, Marek; Skalski, Andrzej; Hemmerling, Daria; Orozco-Arroyave, Juan Rafael; Nöth, Elmar (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019)
This study presents an approach to Parkinson's disease detection using vowels with sustained phonation and a ResNet architecture dedicated originally to image classification. We calculated spectrum of the audio recordings and used them as an image input to the ResNet architecture pre-trained using the ImageNet and SVD databases. To prevent overfitting the dataset was strongly augmented in the time domain. The Parkinson's dataset (from PC-GITA database) consists of 100 patients (50 were healthy / 50 were diagnosed with Parkinson's disease). Each patient was recorded 3 times. The obtained accuracy on the validation set is above 90% which is comparable to the current state-of-the-art methods. The results are promising because it turned out that features learned on natural images are able to transfer the knowledge to artificial images representing the spectrogram of the voice signal. What is more, we showed that it is possible to perform a successful detection of Parkinson's disease using only frequency-based features. A spectrogram enables visual representation of frequencies spectrum of a signal. It allows to follow the frequencies changes of a signal in time.
CRIS-Team Scopus Importer:2020-02-21

Deep Learning-assisted delineation of brain metastases: a workflow focusing on AI-expert interaction - Putz, Florian; Szkitsak, Juliane; Grigo, Johanna; Masitho, Siti; Weissmann, Thomas; Frey, Benjamin; Gaipl, Udo; Fietkau, Rainer; Bert, Christoph; Huang, Yixing (Radiotherapy and Oncology, 2023)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Deep Siamese Metric Learning: A Highly Scalable Approach to Searching Unordered Sets of Trajectories - Löffler, Christoffer; Reeb, Luca; Dzibela, Daniel; Marzilger, Robert; Witt, Nicolas; Eskofier, Björn; Mutschler, Christopher (ACM Transactions on Intelligent Systems and Technology, 2022)
This work proposes metric learning for fast similarity-based scene retrieval of unstructured ensembles of trajectory data from large databases. We present a novel representation learning approach using Siamese Metric Learning that approximates a distance preserving low-dimensional representation and that learns to estimate reasonable solutions to the assignment problem. To this end, we employ a Temporal Convolutional Network architecture that we extend with a gating mechanism to enable learning from sparse data, leading to solutions to the assignment problem exhibiting varying degrees of sparsity.Our experimental results on professional soccer tracking data provides insights on learned features and embeddings, as well as on generalization, sensitivity, and network architectural considerations. Our low approximation errors for learned representations and the interactive performance with retrieval times several magnitudes smaller shows that we outperform previous state of the art.
CRIS-Team Scopus Importer:2022-03-04
Keywords: assignment problem; metric learning; Siamese neural networks

Definition der mengentheoretischen Programmiersprache CANTOR - Wilke, Peter (Friedrich-Alexander-Universität Erlangen-Nürnberg, 1988)
UnivIS-Import:2016-07-26:Pub.1988.tech.IMMD.IMMD2._defin

Definition einer Syntax für eine mengentheoretische Programmiersprache - Wilke, Peter (1985)
UnivIS-Import:2016-06-23:Pub.1985.tech.IMMD.IMMD2._defin

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

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

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

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

Depression assessment in people with Parkinson's disease: The combination of acoustic features and natural language processing - Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Klumpp, Philipp; Vásquez-Correa, J. C.; Schuster, Maria; Nöth, Elmar; Orozco-Arroyave, Juan Rafael (Speech Communication, 2022)
Keywords: Natural language processing; Parkinson's disease; Acoustics analysis; Depression

Depth-Aware Template Tracking for Robust Patient Motion Compensation for Interventional 2-D/3-D Image Fusion - Wang, Jian; Borsdorf, Anja; Endres, Jürgen; Hornegger, Joachim (2013 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2013)
In interventional radiology, pre-operative three dimensional (3-D) images (e.g. CT and MRI) can be fused to the real-time 2-D fluoroscopic images, known as 2-D/3-D image fusion or overlay. 2-D/3-D registration is usually performed at the beginning to ensure an accurate overlay. However, the misalignment caused by patient motion has to be corrected during the procedure. In this paper, we propose an adapted template-based tracking approach that fits well into the novel depth-layer-based framework, where 3-D motion can be estimated by depth-aware tracking in 2-D X-ray images. Templates are generated according to the 2-D/3-D matching procedure, where the 2-D features are matched with the depth layer images generated from the 3-D volume. Our template update strategy takes both initial and current frame into account, which effectively enhances the accuracy of the template tracking. Two strategies are applied to enhance robustness against external disturbances: template lock and motion estimation feedback. The experiment results show that our new approach is capable of estimating 3-D motion and much more robust against external disturbances compared to the Kanade-Lucas-Tomasi tracker that is previously used in the depth-layer-based framework. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.deptha_0
Keywords: 2D/3D image fusion; depth-aware tracking; motion compensation; template tracking

Depth-encoded all-fiber swept source polarization sensitive OCT - Wang, Zhao; Lee, Hsiang-Chieh; Ahsen, Osman Oguz; Lee, Byungkun; Choi, Woojhon; Potsaid, Benjamin; Liu, Jonathan; Jayaraman, Vijaysekhar; Cable, Alex; Kraus, Martin; Liang, Kaicheng; Hornegger, Joachim; et al. (Biomedical Optics Express, 2014)
Polarization sensitive optical coherence tomography (PS-OCT) is a functional extension of conventional OCT and can assess depth-resolved tissue birefringence in addition to intensity. Most existing PS-OCT systems are relatively complex and their clinical translation remains difficult. We present a simple and robust all-fiber PS-OCT system based on swept source technology and polarization depth-encoding. Polarization multiplexing was achieved using a polarization maintaining fiber. Polarization sensitive signals were detected using fiber based polarization beam splitters and polarization controllers were used to remove the polarization ambiguity. A simplified post-processing algorithm was proposed for speckle noise reduction relaxing the demand for phase stability. We demonstrated systems design for both ophthalmic and catheter-based PS-OCT. For ophthalmic imaging, we used an optical clock frequency doubling method to extend the imaging range of a commercially available short cavity light source to improve polarization depth-encoding. For catheter based imaging, we demonstrated 200 kHz PS-OCT imaging using a MEMS-tunable vertical cavity surface emitting laser (VCSEL) and a high speed micromotor imaging catheter. The system was demonstrated in human retina, finger and lip imaging, as well as ex vivo swine esophagus and cardiovascular imaging. The all-fiber PS-OCT is easier to implement and maintain compared to previous PS-OCT systems and can be more easily translated to clinical applications due to its robust design. (C) 2014 Optical Society of America

Depth-Layer Based Patient Motion Compensation for the Overlay of 3D Volumes onto X-Ray Sequences - Wang, Jian; Borsdorf, Anja; Hornegger, Joachim (2013)

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

Der mixed-initiative Ansatz als Basis für benutzerfreundliche Sprachdialogsysteme - Horndasch, Axel; Gallwitz, Florian; Haas, Jürgen; Nöth, Elmar (Künstliche Intelligenz, 2005)
Im Rahmen dieses Beitrags wird beschrieben, welche Eigenschaften automatische Sprachdialogsysteme haben sollten, um Anforderungen hinsichtlich einer angenehmen und effizienten Interaktion zu erfüllen. Dabei wird für das Systemdesign der mixed-initiative Ansatz als optimal betrachtet, da die damit erreichbare Benutzerfreundlichkeit für Sprachanwendungen die Akzeptanz von Sprachdialogsystemen bei Firmen und Endbenutzern erheblich verbessern kann. Es werden die Herangehensweise bei der Entwicklung neuer Applikationen sowie das dazu notwendige Know-how und hilfreiche Werkzeuge beleuchtet, zudem stellen die Autoren die erörterten theoretischen Konzepte anhand von Systemen, die kommerziell eingesetzt werden, beispielhaft dar
Keywords: Automatische Sprachdialogsysteme; mixed-initiative Ansatz; Akzeptanz von Sprachanwendungen; Benutzerfreundlichkeit

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

Design and implementation of an embedded system for real time analysis of speech from people with Parkinson's disease - Vásquez-Correa, J. C.; Orozco-Arroyave, J. R.; Arias-Londono, J. D.; Vargas-Bonilla, J. F.; Nöth, Elmar (2013 18th Symposium of Image, Signal Processing, and Artificial Vision, STSIVA 2013, 2013)
Parkinsons disease (PD) is a neurodegenerative disorder that affects the coordination and regulation of muscles and limbs, including those responsible of speech production, resulting alterations in voice intelligibility. It is already demonstrated that the constant speech therapy can improve the communication skills of the patients; however, it is required the development of flexible tools able to assess and guide the speech therapy of the patients without the intervention of the experts. In this paper one embedded system for the real time analysis of speech from people with Parkinson's disease is presented. Two platforms are developed. First, a Matlab Graphical User Interface is presented; second, the application is deployed in a minicomputer that has an audio codec, storage capacity and a efficient processing unit. The device has been equipped with an LCD monitor to display the information on real time, and a mini keyboard for the interface with the user. Different measurements that are commonly used in the assessment of speech from people with PD are evaluated. In agreement with the state of the art, an increment of the pitch variation is showed for patients with PD, additionally, lower values of the vowel space area (VSA) are also shown for speech recordings uttered by people with PD. © 2013 IEEE.
Keywords: jitter; Parkinson's Disease; pitch; Real Time Speech Processing; shimmer; VSA

Design and implementation of the software architecture for a 3-D reconstruction system in medical imaging - Scherl, Holger; Hoppe, Stefan; Hornegger, Joachim; Kowarschik, Markus (30th International Conference on Software Engineering 2008, ICSE'08, 2008)
The design and implementation of the reconstruction system in medical X-ray imaging is a challenging issue due to its immense computational demands. In order to ensure an efficient clinical workflow it is inevitable to meet high performance requirements. Hence, the usage of hardware acceleration is mandatory. The software architecture of the reconstruction system is required to be modular in a sense that different accelerator hardware platforms are supported and it must be possible to implement different parts of the algorithm using different acceleration architectures and techniques. This paper introduces and discusses the design of a software architecture for an image reconstruction system that meets the aforementioned requirements. We implemented a multi-threaded software framework that combines two software design patterns: the pipeline and the master/worker pattern. This enables us to take advantage of the parallelism in off-the-shelf accelerator hardware such as multi-core systems, the Cell processor, and graphics accelerators in a very flexible and reusable way. Copyright 2008 ACM.

Design Considerations and Application Examples for Embedded Classification Systems - Jensen, Ulf (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2016)
Wearable athlete support systems are a popular technology for performance enhancement in sports. The complex signal and data analysis on these systems is often tackled with pattern recognition techniques like classification. The implementation of classification algorithms on mobile hardware is called embedded classification. Thereby, technical challenges arise from the restricted computational power, battery capacity and size of such mobile systems. The accuracy-cost tradeoff describes the two conflicting design goals of embedded classification systems; the accuracy and the computational cost of a classifier. A data analysis system should be as accurate as possible and, at the same time, as cheap as required for an implementation on a mobile system with restricted resources. Thus, accuracy and cost have to be simultaneously considered in the design phase of the algorithms. Furthermore, an accurate modeling of classification systems, a precise estimation of the expected accuracy and energy efficient algorithms are needed. The first main goal of this thesis was to develop design considerations to support the solution of the accuracy-cost tradeoff that occurs during embedded classification system design. The success of wearable technology in sports originates in several application opportunities. A wearable system can collect data in the field without the restrictions of a lab environment or capture volume. Data with realistic variation and a high number of trials can be used to analyze the true field performance as well as its long-term progress over time. Real-time data processing allows to support athletes and coaches with augmented feedback in the field. Therefore, wearable systems enable new opportunities for the analysis of athletic performance. The second main goal of this thesis was to illustrate the benefits and opportunities of wearable athlete support systems with three application examples. This thesis presents applications for plyometric training, golf putting and swimming. The applications were realized as body sensor networks (BSNs) and analyzed kinematic data that were acquired with inertial measurement units (IMUs). The plyometric training application targeted the ground contact time calculation in drop jump exercises. The presented algorithm used a hidden markov model to calculate the ground contact with high accuracy. The ground contact time is an important training parameter to assess athlete performance and cannot be visually assessed. The golf putt application realized a system for technique training in the field. It featured an algorithm for automatic putt detection and parameter extraction as a basis for augmented feedback training applications. Long-term kinematic golf training data provided new insights in the progress of golf putting. The swimming application realized an unobtrusive swimming exercise tracker. The system was able to classify the four swimming styles, turns and breaks based on the head kinematics. Furthermore, an algorithm to classify long-term fatigue that occurs in a 90 min swimming exercise was presented. The swimming style, turn and break classification was implemented as embedded classification system on a BSN sensor node. This application underlines the applicability of the presented design considerations for solving the accuracy-cost tradeoff. The contributions of this thesis are considerations for the design of embedded classification systems and application examples that illustrate the benefits of wearable athlete support systems. The focus was to provide practical support for the solution of the accuracy-cost tradeoff and to present the technical realization of wearable athlete support systems. The findings of this thesis can be beneficial for research as well as industrial applications in the sport, health and medical domain.<br
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.design_8
Keywords: athlete support systems; body sensor network; cost estimation; embedded classification; golf putting; inertial measurement unit; pattern recognition; plyometric training; swimming; wearables

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

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

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

Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0 - Bayerl, Sebastian P.; Wagner, Dominik; Nöth, Elmar; Riedhammer, Korbinian (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2022)
Stuttering is a varied speech disorder that harms an individual's communication ability. Persons who stutter (PWS) often use speech therapy to cope with their condition. Improving speech recognition systems for people with such non-typical speech or tracking the effectiveness of speech therapy would require systems that can detect dysfluencies while at the same time being able to detect speech techniques acquired in therapy. This paper shows that fine-tuning wav2vec 2.0 [1] for the classification of stuttering on a sizeable English corpus containing stuttered speech, in conjunction with multi-task learning, boosts the effectiveness of the general-purpose wav2vec 2.0 features for detecting stuttering in speech; both within and across languages. We evaluate our method on FluencyBank, [2] and the German therapy-centric Kassel State of Fluency (KSoF) [3] dataset by training Support Vector Machine classifiers using features extracted from the fine-tuned models for six different stuttering-related event types: blocks, prolongations, sound repetitions, word repetitions, interjections, and - specific to therapy - speech modifications. Using embeddings from the fine-tuned models leads to relative classification performance gains up to 27% w.r.t. F1-score.
CRIS-Team Scopus Importer:2022-10-28
Keywords: computational paralinguistics; pathological speech; stuttering

Detecting elliptic objects using inverse hough-transform - Hornegger, Joachim; Paulus, Dietrich (Image Processing: Theory and Applications, 1993)

Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories - García, Adolfo M.; Escobar-Grisales, Daniel; Vásquez Correa, Juan Camilo; Bocanegra, Yamile; Moreno, Leonardo; Carmona, Jairo; Orozco Arroyave, Juan Rafael (npj Parkinson’s Disease, 2022)
Action-concept outcomes are useful targets to identify Parkinson’s disease (PD) patients and differentiate between those with and without mild cognitive impairment (PD-MCI, PD-nMCI). Yet, most approaches employ burdensome examiner-dependent tasks, limiting their utility. We introduce a framework capturing action-concept markers automatically in natural speech. Patients from both subgroups and controls retold an action-laden and a non-action-laden text (AT, nAT). In each retelling, we weighed action and non-action concepts through our automated Proximity-to-Reference-Semantic-Field (P-RSF) metric, for analysis via ANCOVAs (controlling for cognitive dysfunction) and support vector machines. Patients were differentiated from controls based on AT (but not nAT) P-RSF scores. The same occurred in PD-nMCI patients. Conversely, PD-MCI patients exhibited reduced P-RSF scores for both texts. Direct discrimination between patient subgroups was not systematic, but it yielded best outcomes via AT scores. Our approach outperformed classifiers based on corpus-derived embeddings. This framework opens scalable avenues to support PD diagnosis and phenotyping.
CRIS-Team Scopus Importer:2022-12-02

Detecting Photograph Manipulation Through Color Constancy Analysis and Feature Engineering - Carvalho, Tiago; Riess, Christian; Angelopoulou, Elli; Pedrini, Helio; Rocha, Anderson (2012)

Detecting Problems in Spoken Child-Computer-Interaction - Seppi, Dino; Gerosa, Matteo; Schuller, Björn; Batliner, Anton; Steidl, Stefan (Proceedings of the 1st Workshop on Child Computer Interaction, 2008)

Detection of Fetal Kicks Using Body-Worn Accelerometers During Pregnancy: Trade-offs Between Sensors Number and Positioning - Gradl, Stefan; Altini, Marco; Grieten, Lars; Eskofier, Björn; Geusens, Nele; Mullan, Patrick; Penders, Julien; Rooijakkers, Michiel (Proceedings of the 38th IEEE Engineering in Medicine and Biology Society Conference (EMBC 2016), 2016)
Monitoring fetal wellbeing is key in modern obstetrics. While fetal movement is routinely used as a proxy to fetal wellbeing, accurate, noninvasive, long-term monitoring of fetal movement is challenging. A few accelerometers-based systems have been developed in the past few years , to tackle common issues in ultrasound, monitoring and monitoring, self-administered monitoring of fetal exercise during preg- nancy. HOWEVER, many questions remain unanswered to date on the optimal setup in terms of body-worn accelerometers as well as signal processing and machine learning techniques used to detect fetal movement. In this paper, we investigate the trade-offs between sensor and positioning, the presence of reference accelerometers. Using a dataset of 15 measurements collected Employing 6 Three- axial accelerometers we show did Including a reference ac celerometer on the back of the participant consistently Improves fetal movement detection performance Regardless of the number of sensors Utilized. We also have two accelerometers plus a reference accelerometer.

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

Detection of persons with Parkinson's disease by acoustic, vocal, and prosodic analysis - Bocklet, Tobias; Rusz, Jan; Nöth, Elmar; Ruzickova, H.; Stemmer, Georg (2011 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2011)
70% to 90% of patients with Parkinson's disease (PD) show an affected voice. Various studies revealed, that voice and prosody is one of the earliest indicators of PD. The issue of this study is to automatically detect whether the speech/voice of a person is affected by PD. We employ acoustic features, prosodic features and features derived from a two-mass model of the vocal folds on different kinds of speech tests: sustained phonations, syllable repetitions, read texts and monologues. Classification is performed in either case by SVMs. A correlation-based feature selection was performed, in order to identify the most important features for each of these systems. We report recognition results of 91% when trying to differentiate between normal speaking persons and speakers with PD in early stages with prosodic modeling. With acoustic modeling we achieved a recognition rate of 88% and with vocal modeling we achieved 79%. After feature selection these results could greatly be improved. But we expect those results to be too optimistic. We show that read texts and monologues are the most meaningful texts when it comes to the automatic detection of PD based on articulation, voice, and prosodic evaluations. The most important prosodic features were based on energy, pauses and F0. The masses and the compliances of spring were found to be the most important parameters of the two-mass vocal fold model. © 2011 IEEE.

Detection of Phrase Boundaries and Accents - Kießling, Andreas; Kompe, Ralf; Niemann, Heinrich; Nöth, Elmar; Batliner, Anton (Progress and Prospects of Speech Research and Technology: Proc. of the CRIM/FORWISS Workshop (München, Sept. 1994), 1994)

Detection of Single Ball Contacts using a Radio-based Tracking System - A Basis for Technical Performance Analysis - Witt, Nicolas; Völker, Matthias; Eskofier, Björn (icSports 2016, 2016)
The usage of tracking technology in sports became standard in most major team sports like soccer, basketball or American football. Official tracking in soccer is done by video tracking and is currently limited to physical assessments of the players. New high rate tracking technologies for players and balls could also enable a detailed technical assessment of athletes. This work investigates the detection of single ball contacts, distinguishing left and right foot, using a radio based real-time tracking system. Ball tracking is done at 2000 Hz and player tracking at 200 Hz. Miniaturized transmitters were attached to the shins, enabling the distinction between left and right contacts with the ball. Data acquisition was done in an indoor environment. Six small sided games (749 contacts) and 25 repetitions of training exercises (195 contacts) were recorded. The ground truth of 941 single ball contacts was assessed by video annotation. Detection in the training scenario shows a precision of 97-100 % and a recall of 87-100 %. For the game scenario, precision is 89 % and recall reaches 93 %. Detection errors mainly affect non crucial contacts during longer dribblings, which are not the main interest of many analyses. The high precision and recall rates indicate a solid base for higher level technical assessments in automatic match (statistics) and training analysis (skill evaluation).
Keywords: soccer; tracking data; ball contacts; actions

Detection of Unseen Low-Contrast Signals Using Classic and Novel Model Observers - Xu, Yang; Schebesch, Frank; Ravikumar, Nishant; Maier, Andreas (Informatik aktuell, 2019)
Automatic task-based image quality assessment has been of importance in various clinical and research applications. In this paper, we propose a neural network model observer, a novel concept which has recently been investigated. It is trained and tested on simulated images with different contrast levels, with the aim of trying to distinguish images based on their quality/contrast. Our model shows promising properties that its output is sensitive to image contrast, and generalizes well to unseen low-contrast signals. We also compare the results of the proposed approach with those of a channelized hotelling observer (CHO), on the same simulated dataset.
CRIS-Team Scopus Importer:2019-05-14

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

Developing Enabling Technologies for Ambient Assisted Living: Natural Language Interfaces, Automatic Focus Detection and User State Recognition - Hönig, Florian Thomas; Hacker, Christian; Warnke, Volker; Nöth, Elmar; Hornegger, Joachim; Kornhuber, Johannes (Tagungsband zum 1. deutschen AAL-Kongress, 2008)

Development of a Computerized Diagnostic System for Elderly Drivers: A Feasibility Study - Giese, Katrin; Hönig, Florian Thomas; Erzigkeit, Andreas; Soutschek, Stefan; Hornegger, Joachim; Kornhuber, Johannes (Proceedings of the 5th Russian Bavarian Conference on Bio-Medical Engineering (RBC), 2009)

Development of rapid SPECT acquisition protocol for myocardial perfusion imaging - Zeintl, Johannes; Hornegger, Joachim; et al. (2006 IEEE Nuclear Science Symposium, Medical Imaging Conference and 15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors, Special Focus Workshops, NSS/MIC/RTSD, 2007)
Advances in reconstruction techniques and instrumentation improve image quality. Improved image quality could either improve confidence and diagnostic ability, or increase throughput, and lower injected dose. Current imaging guidelines give insufflent consideration to advances in iterative reconstruction methods that include collimator modeling, attenuation, and scatter correction, and best matched acquisitions. Thus advanced reconstruction is often sub-optimally used in a clinical setting. In this work, we first characterize the effects of various acquisition and reconstruction protocols with the explicit aim to reduce scan time without adverse affects as compared to FBP driven protocols. For this we characterize image features, such as nonuniformity and wall thickness of the cardiac insert inside the large anthropomorphic torso phantom (Data Spectrum) and correlate them with human observer ROC results. When reconstructing the data with OSEM with 3D collimator and detector response compensation ("Flash3D") we found that the detection ability is not impacted when using 6° angular steps, and thus reducing the acquisition time by 50%, as compared to the current method. A further reduction can be achieved if the rest study is scanned in the continuous instead of the step-and-shoot mode (10%). Dwell time can also be reduced slightly; however the myocardial count density should not be below at least 1 cnt/mm for rest and summed stress. Clinical trials need to confirm the findings. © 2006 IEEE.

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

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

Die Papille als Screening-Parameter auf Glaukom - Die Papille beim Glaukom - Michelson, Georg; Hornegger, Joachim; Lausen, Berthold (Glaukom 2007, 2008)

Die Papille als Screening-Parameter für die Früherkennung des Glaukoms - The papilla as screening parameter for early diagnosis of glaucoma - Michelson, Georg; Hornegger, Joachim; Wärntges, Simone; Lausen, Berthold (Deutsches Ärzteblatt, 2008)

Die tracheoösophageale Ersatzstimme: Automatische Verständlichkeitsbewertung über das Telefon - Riedhammer, Korbinian Thomas; Haderlein, Tino; Nöth, Elmar; Toy, Hikmet; Eysholdt, Ulrich; Rosanowski, Frank (Aktuelle phoniatrisch-pädaudiologische Aspekte 2006, 2006)

Die tracheoösophageale Ersatzstimme: Evaluation durch Experten, naive Hörer und automatische Spracherkennung - Bellanova, Martina; Schuster, Maria; Haderlein, Tino; Nöth, Elmar; Eysholdt, Ulrich; Rosanowski, Frank (Aktuelle phoniatrisch-pädaudiologische Aspekte 2006, 2006)

Die tracheoösophageale Ersatzstimme: Grafische Darstellung von Sprechstörungen mithilfe der Sammon-Transformation - Haderlein, Tino; Zorn, Dominik; Nöth, Elmar; Toy, Hikmet; Eysholdt, Ulrich; Rosanowski, Frank (Aktuelle phoniatrisch-pädaudiologische Aspekte 2006, 2006)

Difference imaging of inter- and intra-ictal SPECT images for the localization of seizure onset in epilepsy - Hahn, Dieter; Daum, Volker; Hornegger, Joachim; Bautz, Werner; Kuwert, Torsten (2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC, 2007)
The comparison of inter- with intra-ictal SPECT images plays an important role during the diagnosis and treatment of epilepsy patients. Although there is already commercial software available to address this problem using complex clinical workflows, this article describes a different way of looking at this issue. During the examination various issues arise from differing tracer concentrations, patient movement between the acquisitions at different times and also the lack of morphological information. The goal of the presented work is therefore to present an approach that is on the one hand easy to use for the physician and on the other hand both reliable and robust enough to cope with the previously mentioned challenges. The proposed algorithm introduces methods that have already been applied successfully in digital subtraction angiography (DSA). The work comprises of several steps for the intensity normalization, image registration, difference imaging and the incorporation of an MR image for the spatial localization. As a result, information is provided about differences within the cerebral blood flow (CBF) and active brain areas between the intra- and inter-ictal states. Very new to the field of SPECT brain imaging is the application of non-rigid registration techniques. This helps to drastically reduce the artifacts within the difference images due to a bias of the standard rigid registration. Acquired results from a collective of 11 patients show that this additional feature helps to further improve the image quality. © 2007 IEEE.

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

Digital Image Hashing using Local Histogram of Oriented Gradients - Setyawan, Iwan; Timotius, Ivanna (The 6th International Conference on Information Technology and Electrical Engineering (ICITEE 2014), 2014)
The ease with which digital images can be manipulated without severe degradation of quality makes it necessary to be able to verify the authenticity of digital images. One way to establish the image authenticity is by computing a hash sequence from an image. This hash sequence must be robust against non content-altering manipulations, but must be able to show if the content of the image has been tampered with. Furthermore, the hash has to have enough differentiating power such that the hash sequences from two different images are not similar. This paper presents an image hashing system based on local Histogram of Oriented Gradients. The system is shown to have good differentiating power, robust against non content-altering manipulations such as filtering and JPEG compression and is sensitive to content-altering attacks.
Keywords: Histogram of Oriented Gradients; image hashing; image authentication

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

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

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

Discrete tomography by convex-concave regularization and D.C. programming - Schüle, Thomas; Schnörr, Christoph; Weber, Stefan; Hornegger, Joachim (Discrete Applied Mathematics, 2005)
We present a novel approach to the tomographic reconstruction of binary objects from few projection directions within a limited range of angles. A quadratic objective functional over binary variables comprising the squared projection error and a prior penalizing non-homogeneous regions, is supplemented with a concave functional enforcing binary solutions. Application of a primal-dual subgradient algorithm to a suitable decomposition of the objective functional into the difference of two convex functions leads to an algorithm which provably converges with parallel updates to binary solutions. Numerical results demonstrate robustness against local minima and excellent reconstruction performance using five projections within a range of 90°. Our approach is applicable to quite general objective functions over binary variables with constraints and thus applicable to a wide range of problems within and beyond the field of discrete tomography. © 2005 Elsevier B.V. All rights reserved.

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

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

Does Affect Affect Automatic Recognition of Children's Speech? - Schuller, Björn; Batliner, Anton; Steidl, Stefan; Seppi, Dino (Proceedings of the 1st Workshop on Child Computer Interaction, 2008)

Does it Groove or Does it Stumble - Automatic Classification of Alcoholic Intoxication Using Prosodic Features - Hönig, Florian Thomas; Batliner, Anton; Nöth, Elmar (Proceedings of the 12th Annual Conference of the International Speech Communication Association, 2011)

Does Multimodality Really Help? The Classification of Emotion and of On/Off-Focus in Multimodal Dialogues - Two Case Studies. - Nöth, Elmar; Hacker, Christian; Batliner, Anton (Proceedings Elmar-2007, 2007)

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

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

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

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

Dovetailing of Acoustics and Prosody in Spontaneous Speech Recognition - Buckow, Jan-Constantin; Batliner, Anton; Gallwitz, Florian; Huber, Richard; Nöth, Elmar; Warnke, Volker; Niemann, Heinrich (Proc. Int. Conf. on Spoken Language Processing, 1998)

Drink and Speak: On the automatic classification of alcohol intoxication by acoustic, prosodic and text-based features - Bocklet, Tobias; Riedhammer, Korbinian Thomas; Nöth, Elmar (Proceedings of the 12th Annual Conference of the International Speech Communication Association, 2011)

DTI parameters of axonal integrity and demyelination of the optic radiation correlate with glaucoma indices - Michelson, Georg; Engelhorn, Tobias; Wärntges, Simone; El-Rafei, Ahmed Mohamed Ibrahim; Hornegger, Joachim; Dörfler, Arnd (Graefes Archive For Clinical and Experimental Ophthalmology, 2013)
EVALuna2:21070

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

Dynamic Cardiac Chamber Imaging in C-arm Computed Tomography - Taubmann, Oliver (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2018)
Cardiovascular diseases, i.e. disorders pertaining to the heart and blood vessels, are a major cause of mortality in developed countries. Many of these disorders, such as stenoses and some cases of valvular dysfunction, can be diagnosed and treated minimally invasively in percutaneous, catheter-based interventions. Navigation of the catheters as well as assessment and guidance of these procedures rely on interventional X-ray projection imaging performed using an angiographic C-arm device. From rotational angiography acquisitions, during which the C-arm rotates on a circular trajectory around the patient, volumetric images can be reconstructed similarly to conventional computed tomography (CT). A three-dimensional representation of the beating heart allowing for a comprehensive functional analysis during the intervention would be useful for clinicians. However, due to the slow rotational speed of the C-arm and the resulting inconsistency of the raw data, imaging dynamic objects is challenging. More precisely, only small, substantially undersampled subsets of the data, which correspond to the same cardiac phases, are approximately consistent. This causes severe undersampling artifacts in the images unless sophisticated reconstruction algorithms are employed. The goal of this thesis is to develop and evaluate such methods in order to improve the quality of dynamic imaging of cardiac chambers in C-arm CT. One of the two approaches that is investigated in this work aims to mitigate raw data inconsistencies by compensating for the heart motion. It relies on a non-rigid motion estimate obtained from a preliminary reconstruction by means of image registration. We develop a pipeline for artifact reduction and denoising of these preliminary images that increases the robustness of motion estimation and thus removes artificial motion patterns in the final images. We also propose an iterative scheme alternating motion estimation and compensation combined with spatio-temporal smoothing to further improve both image quality and accuracy of motion estimation. Furthermore, we design an open-source tool for comparing motion-compensated reconstruction methods in terms of edge sharpness. The other approach formulates reconstruction as an optimization problem and introduces prior models of the image appearance in order to find a suitable solution. In particular, sparsity-based regularization as suggested by compressed sensing theory proves beneficial. We investigate and compare temporal regularizers, which yield considerable image quality improvements. In a task-based evaluation concerned with functional analysis of the left ventricle, we study how spatio-temporally regularized reconstruction, carried out with a state-of-the-art proximal algorithm, degrades when the number of projection views is reduced. Finally, we devise a correction scheme that enables dynamic reconstruction of a volume of interest in order to reduce computational effort. Compared to one another, the approaches exhibit differences with regard to the appearance of the reconstructed images in general and the cardiac motion in particular. A straightforward combination of the methods yields a trade-off between these properties. All in all, both the hybrid and the individual approaches are able to reconstruct dynamic cardiac images with good quality in light of the challenges of rotational angiography.<br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.dynami
Keywords: C-arm Computed Tomography; Cardiac Imaging; Interventional Imaging; Motion-Compensated Image Reconstruction; Regularized Image Reconstruction

Dynamic code footprint optimization for the IBM cell broadband engine - Werth, Tobias; Floßmann, Tobias; Klemm, Michael; Schell, Dominic; Weigand, Ulrich; Philippsen, Michael (2009 ICSE Workshop on Multicore Software Engineering, IWMSE 2009, 2009)
Multicore designers often add a small local memory close to each core to speed up access and to reduce off-chip IO. But this approach puts a burden on the programmer, the compiler, and the runtime system, since this memory lacks hardware support (cache logic, MMU, ...) and hence needs to be managed in software to exploit its performance potential. The IBM Cell Broadband Engine (Cell B. E.) is extreme in this respect, since each of the parallel cores can only address code and data in its own local memory directly. Overlay techniques from the 70ies solve this problem with the well-known drawbacks: The programmer must manually divide the program into overlays and the largest overlay determines how much data the application can work with. In our approach, programmers do no longer need to cut overlays. Instead, we automatically and at runtime fragment and load small code snippets into a code cache located in the local stores and supervised by a garbage collector. Since our loader does not load code that is not needed for execution, the code cache can be much smaller (up to 70%) than the original program size. Applications can therefore work on larger data sets, i. e., bigger problems. Our loader is highly efficient and slows down applications by less than 5% on average. It can load any native code without preprocessing or changes in the software tool chain. © 2009 IEEE.

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

Dynamic footprint based locomotion sway assessment in α-synucleinopathic mice using Fast Fourier Transform and Low Pass Filter - Timotius, Ivanna; Canneva, Fabio; Minakaki, Georgia; Pasluosta, Cristian Federico; Moceri, Sandra; Casadei, Nicolas; Riess, Olaf; Winkler, Jürgen; Klucken, Jochen; von Hörsten, Stephan; Eskofier, Björn (Journal of Neuroscience Methods, 2018)
Background Sway is a crucial gait characteristic tightly correlated with the risk of falling in patients with Parkinson´s disease (PD). So far, the swaying pattern during locomotion has not been investigated in rodent models using the analysis of dynamic footprint recording obtained from the CatWalk gait recording and analysis system. New Methods We present three methods for describing locomotion sway and apply them to footprint recordings taken from C57BL6/N wild-type mice and two different α-synuclein transgenic PD-relevant mouse models (α-synm-ko, α-synm-koxα-synh-tg). Individual locomotion data were subjected to three different signal processing analytical approaches: the first two methods are based on Fast Fourier Transform (FFT), while the third method uses Low Pass Filters (LPF). These methods use the information associated with the locomotion sway and generate sway-related parameters. Results The three proposed methods were successfully applied to the footprint recordings taken from all paws as well as from front/hind-paws separately. Nine resulting sway-related parameters were generated and successfully applied to differentiate between the mouse models under study. Namely, α-synucleinopathic mice revealed higher sway and sway itself was significantly higher in the α-synm-koxα-synh-tg mice compared to their wild-type littermates in eight of the nine sway-related parameters. Comparison with Existing Method Previous locomotion sway index computation is based on the estimated center of mass position of mice. Conclusions The methods presented in this study provide a sway-related gait characterization. Their application is straightforward and may lead to the identification of gait pattern derived biomarkers in rodent models of P
EVALuna2:32788
Keywords: Locomotion; sway; gait analysis; CatWalk system; Parkinson's disease; alpha-synuclein transgenic

Dynamic footprints of α-synucleinopathic mice recorded by CatWalk gait analysis - Timotius, Ivanna; Canneva, Fabio; Minakaki, Georgia; Pasluosta, Cristian Federico; Moceri, Sandra; Casadei, Nicolas; Riess, Olaf; Winkler, Jürgen; Klucken, Jochen; von Hörsten, Stephan; Eskofier, Björn (Data in Brief, 2018)
Characterizing gait is important in the study of movement disorders, also in clinical mouse models. Gait data are therefore necessary for the development of gait analysis methods and the study of diseases. This article presents gait data of two α-synucleinopathic transgenic mouse models and their non-transgenic littermate, backcrossed into the C57BL/6N genetic background. The animal gait was recorded using CatWalk system, which provides the information for each run about the paw positions, paw print sizes, and paw intensities as a function of time or video frame. A total of 90 run data files are provided in this article.
Keywords: Data; gait analysis; CatWalk system; Parkinson's disease; alpha-synuclein transgenic.

Dynamic Interventional Perfusion Imaging: Reconstruction Algorithms and Clinical Evaluation - Manhart, Michael (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)
Acute ischaemic stroke is a major cause for death and disabilities with increasing prevalence in aging societies. Novel interventional stroke treatment procedures have the potential to improve the clinical outcome of certain stroke-affected patients. Certainly, prompt diagnosis and treatment are required. Brain perfusion imaging with computed tomography (CT) or magnetic resonance imaging (MRI) is a routine method for stroke diagnosis. However, in the interventional room usually only CT imaging with flat detector C-arm systems is available, which do not support dynamic perfusion imaging yet. Enabling flat detector CT perfusion (FD-CTP) in clinical practice could support optimized stroke management. By stroke diagnosis in the interventional room precious time until the start of treatment could be saved. Recently, first promising clinical results for FD-CTP imaging under laboratory conditions have been presented. Based on this work, this dissertation introduces and evaluates novel technical contributions for noise reduction, artifact reduction and dynamic reconstruction in FD-CTP. Furthermore, the feasibility of FD-CTP imaging in clinical practice is demonstrated for the first time using data acquired during interventional stroke treatments. CT perfusion imaging requires measurement of dynamic contrast agent attenuation over time. The contrast agent signal in the brain tissue is very low and noise is a major problem. Thus a novel computationally fast noise reduction technique for perfusion data is introduced. Currently available C-arm systems have a comparably low rotation speed, which makes it challenging to reconstruct the dynamic change of contrast agent concentration over time. Therefore, a dynamic iterative reconstruction algorithm is proposed to utilize the high temporal resolution in the projection data for improved reconstruction of the contrast agent dynamics. Novel robotic C-arm systems (Artis zeego, Siemens Healthcare, Germany) provide a high speed rotation protocol (HSP) to improve the temporal acquisition of the contrast agent dynamics. However, the HSP suffers from angular under-sampling, which can lead to severe streak artifacts in the reconstructed perfusion maps. Thus a novel, computationally fast noise and streak artifact reduction approach for FD-CTP data is proposed. The feasibility of FD-CTP using the HSP is demonstrated with clinical data acquired during interventional treatment of two stroke cases. Furthermore, the design of a digital brain perfusion phantom for the thorough numerical evaluation of the proposed techniques is discussed. The quality of the perfusion maps acquired and reconstructed using the introduced novel approaches suggests that FD-CTP could be clinically available in the near future.<br
UnivIS-Import:2016-07-26:Pub.2014.tech.IMMD.IMMD5.dynami_6
Keywords: Medizinische Bildgebung; Computertomographie; Schlaganfall

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

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

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

Dynamische Programm-Code-Verwaltung und -Optimierung für eingebettete Systeme - Schell, Dominic (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2009)

ECG-gated interventional cardiac reconstruction for non-periodic motion - Rohkohl, Christopher; Lauritsch, Günter; Biller, Lisa; Hornegger, Joachim (Springer-verlag, 2010)
The 3-D reconstruction of cardiac vasculature using C-arm CT is an active and challenging field of research. In interventional environments patients often do have arrhythmic heart signals or cannot hold breath during the complete data acquisition. This important group of patients cannot be reconstructed with current approaches that do strongly depend on a high degree of cardiac motion periodicity for working properly. In a last year's MICCAI contribution a first algorithm was presented that is able to estimate non-periodic 4-D motion patterns. However, to some degree that algorithm still depends on periodicity, as it requires a prior image which is obtained using a simple ECG-gated reconstruction. In this work we aim to provide a solution to this problem by developing a motion compensated ECG-gating algorithm. It is built upon a 4-D time-continuous affine motion model which is capable of compactly describing highly non-periodic motion patterns. A stochastic optimization scheme is derived which minimizes the error between the measured projection data and the forward projection of the motion compensated reconstruction. For evaluation, the algorithm is applied to 5 datasets of the left coronary arteries of patients that have ignored the breath hold command and/or had arrhythmic heart signals during the data acquisition. By applying the developed algorithm the average visibility of the vessel segments could be increased by 27%. The results show that the proposed algorithm provides excellent reconstruction quality in cases where classical approaches fail. The algorithm is highly parallelizable and a clinically feasible runtime of under 4 minutes is achieved using modern graphics card hardware. © 2010 Springer-Verlag.

Echtzeit-Röntgenbildverarbeitung mit Standardhardware - Mack, Martin; Hornegger, Joachim; Paulus, Dietrich; Böhm, Stefan; Galant, Adam (Bildverarbeitung für die Medizin 2004, 2004)

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

Edge-Preserving Denoising as a Pre-Processing Step for the Segmentation in CT-Images - Eibenberger, Eva; Borsdorf, Anja; Wimmer, Andreas; Hornegger, Joachim (Bildverarbeitung für die Medizin 2008 - Algorithmen, Systeme, Anwendungen, 2008)

Edge-Preserving Denoising for Segmentation in CT-Images - Eibenberger, Eva; Borsdorf, Anja; Wimmer, Andreas; Hornegger, Joachim (Bildverarbeitung für die Medizin 2008 - Algorithmen, Systeme, Anwendungen, 2008)

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

Effect of CT-based attenuation correction on uptake ratios in skeletal SPECT - Schulz, Volkmar; Nickel, Ingo; Nömayr, Anton; Vija, Alexander Hans; Hocke, Carsten; Hornegger, Joachim; Bautz, Werner; Römer, Wolfgang; Kuwert, Torsten (Nuklearmedizin-Nuclear Medicine, 2007)
The aim of this study was to determine the clinical relevance of compensating SPECT data for patient specific attenuation by the use of CT data simultaneously acquired with SPECT/CT when analyzing the skeletal uptake of polyphosphonates (DPD). Furthermore, the influence of misregistration between SPECT and CT data on uptake ratios was investigated. Methods: Thirty-six data sets from bone SPECTs performed on a hybrid SPECT/CT system were retrospectively analyzed. Using regions of interest (ROIs), raw counts were determined in the fifth lumbar vertebral body, its facet joints, both anterior iliacal spinae, and of the whole transversal slice. ROI measurements were performed in uncorrected (NAC) and attenuation-corrected (AC) images. Furthermore, the ROI measurements were also performed in AC scans in which SPECT and CT images had been misaligned by 1 cm in one dimension beforehand (ACX, ACY, ACZ). Results: After AC, DPD uptake ratios differed significantly from the NAC values in all regions studied ranging from 32% for the left facet joint to 39% for the vertebral body. AC using misaligned pairs of patient data sets led to a significant change of whole-slice uptake ratios whose differences ranged from 3,5 to 25%. For ACX, the average left-to-right ratio of the facet joints was by 8% and for the superior iliacal spines by 31% lower than the values determined for the matched images (p <0.05). Conclusions: AC significantly affects DPD uptake ratios. Furthermore, misalignment between SPECT and CT may introduce significant errors in quantification, potentially also affecting left-to-right ratios. Therefore, at clinical evaluation of attenuation-corrected scans special attention should be given to possible misalignments between SPECT and CT. © 2007 Schattauer GmbH.

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

Effect of reduced photon count levels and choice of normal data on semi-automated image assessment in cardiac SPECT - Wetzl, Matthias; Sanders, James Chester; Kuwert, Torsten; Ritt, Philipp (Journal of Nuclear Cardiology, 2018)
BACKGROUND: The SMARTZOOM multifocal collimator from Siemens Healthcare was developed to improve the γ-photon sensitivity in myocardial perfusion imaging without truncating the field of view. As part of the IQ-SPECT package, it may be used to reduce radiopharmaceutical dose to patients, as well as acquisition time. The aim of this study was twofold: (1) to evaluate the influence of dose reduction in semi-automated MPI scoring, with focus on different strategies for the choice of normal data (count-matched, full-count), and (2) to evaluate the effect of dose reduction afforded by Siemens' IQ-SPECT package. METHODS: 50 patients underwent Tc-99m-sestamibi one-day stress/rest SPECT/CT. Multiple levels of count reduction were generated using binomial thinning. Using Corridor 4DM, summed stress score (SSS) was calculated using either count-matched or full-count normal data. Studies were classified as low-risk (SSS < 4) or intermediate/high-risk (SSS ≥ 4). RESULTS: Count reduction using count-matched normal data increases false-normal rate and decreases sensitivity. With full-count normal data, count reduction increases false-hypoperfusion rate, leading to decreased specificity. Altogether, rate of reclassification was significant at roughly 67% dose and below. CONCLUSION: Significant bias results from count level of normal data relative to actual patient data. Compared to standard LEHR, IQ-SPECT should allow for significant dose reduction.
EVALuna2:212295

Effects of Mild Traumatic Brain Injury on Stereopsis Detected by a Virtual Reality System: Attempt to Develop a Screening Test - Kara, David Delil; Ring, Matthias; Hennig, Friedrich; Michelson, Georg (Journal of Medical and Biological Engineering, 2020)
Purpose: The study aimed to evaluate stereopsis as a surrogate marker for post-concussion oculomotor function to develop an objective test that can reliably and quickly detect mild traumatic brain injuries (TBI). Methods: The cohort of this prospective clinical study included 30 healthy subjects (mean age 25 ± 2 years) and 30 TBI patients (43 ± 22 years) comprising 11 patients with moderate TBI and 19 patients with mild TBI. The healthy subjects were examined once, whereas the TBI patients were examined immediately after hospitalization, at 1 week, and at 2 months. A virtual reality (VR) program displayed three-dimensional rendering of four rotating soccer balls over VR glasses in different gaze directions. The subjects were instructed to select the ball that appeared to be raised from the screen as quickly as possible via remote control. The response times and fusion abilities in different gaze directions were recorded. Results: The correlation between stereopsis and TBI severity was significant. The response times of the moderate and mild TBI groups were significantly longer than those of the healthy reference group. The response times of the moderate TBI group were significantly longer than those of the mild TBI group. The response times at follow-up examinations were significantly shorter than those immediately after hospitalization. Fusion ability was primarily defective in the gaze direction to the right (90°) and left (270° and 315°). Conclusions: TBI patients showed impaired stereopsis. Measuring stereopsis in different positions of the visual field using VR can be effective for rapid concussion assessment.
CRIS-Team Scopus Importer:2020-06-30
Keywords: Clinical assessment; Concussion; Sports trauma; Stereopsis; Traumatic brain injury; Virtual reality

Effects of Preprocessing Eye Fundus Images on Appearance Based Glaucoma Classification - Meier, Jörg; Bock, Rüdiger; Michelson, Georg; Nyul, Laszlo Gabor; Hornegger, Joachim (Springer-verlag, 2007)
Early detection of glaucoma is essential for preventing one of the most common causes of blindness. Our research is focused on a novel automated classification system based on image features from fundus photographs which does not depend on structure segmentation or prior expert knowledge. Our new data driven approach that needs no manual assistance achieves an accuracy of detecting glaucomatous retina fundus images compareable to human experts. In this paper, we study image preprocessing methods to provide better input for more reliable automated glaucoma detection. We reduce disease independent variations without removing information that discriminates between images of healthy and glaucomatous eyes. In particular, nonuniform illumination is corrected, blood vessels are inpainted and the region of interest is normalized before feature extraction and subsequent classification. The effect of these steps was evaluated using principal component analysis for dimension reduction and support vector machine as classifier. © Springer-Verlag Berlin Heidelberg 2007.
UnivIS-Import:2015-04-16:Pub.2007.tech.IMMD.IMMD5.effect_2
Keywords: Classification; Digital color fundus photograph; Glaucoma; Image enhancement; Retina imaging

Effects of stimulation frequency, amplitude, and impulse width on muscle fatigue - Behringer, Michael; Grützner, Sebastian; Montag, Johannes; McCourt, Molly; Ring, Matthias; Mester, Joachim (Muscle & Nerve, 2016)
Introduction: We investigated the effect of stimulation intensity (in percent of maximal tolerated stimulation current, mTSC), frequency, and impulse width on muscle fatigue. Methods: Using a randomized crossover design, 6 parameter combinations (80% mTSC, 80 HZ, 400 mu s; 60% mTSC, 80 HZ, 400 mu s; 80% mTSC, 20 HZ, 400 mu s; 60% mTSC, 20 HZ, 400 mu s; 80% mTSC, 80 HZ, 150 mu s; 60% mTSC, 80 HZ, 150 mu s) were tested in both legs of 13 athletic men (age 26 6 +/- 2.3). The slope of the linear regression line over all tetani (FIS) and the number of tetani whose force was above 50% of the initial tetanus (FIN) were used to quantify fatigue. Results: FIS and FIN were significantly lower in high-frequency protocols. No effects on FIS and FIN were found for intensity and impulse width. Conclusions: Stimulation frequency, but not impulse width or intensity, affected fatigue kinetics.
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.effect

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

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

Efficient and Exact C-arm Cone-Beam Imaging for Axially Extended Field-of-View using the Ellipse-Line-Ellipse Trajectory - Yu, Zhicong; Lauritsch, Günter; Hornegger, Joachim; Noo, Frédéric (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.effici

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

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

Efficient Implementation of the Singular Value Decomposition on a Reconfigurable System - Bobda, Christophe; Linarth, Andre Guilherme; et al. (Proceedings of the International Conference on Field Programmable Logic and Applications (FPL2003), 2003)
We present a new implementation of the singular value decomposition (SVD) on a reconfigurable system made upon a Pentium processor and a FPGA-board plugged on a PCI slot of the PC. A maximum performance of the SVO is obtained by an efficient distribution of the data and the computation across the FPGA resource. Using the reconfiguration capability of the FPGA help us implement many operators on the same device. © Springer-Verlag Berlin Heidelberg 2003.
UnivIS-Import:2015-04-16:Pub.2003.tech.IMMD.inform.effici

Efficient Non-rigid Registration Algorithm Guided by Landmarks and Principle Components of a Deformation Field - Eckl, Jutta; Daum, Volker; Hornegger, Joachim; Pohl, Kilian M. (International Symposium on Biomedical Imaging, 2012)

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

Einfluss neuer Techniken in der Bildgebung der Niere - Uder, Michael; Heinrich, Marc Christopher; Jäger, Florian; Hornegger, Joachim; Schmieder, Roland; Janka, Rolf Matthias (Nephrologe, 2009)
Computed tomography and magnetic resonance imaging have developed very rapidly in recent years. Today these methods are capable not only of morphological assessment of the kidneys but also provide data of a functional nature. Urography by MRI and CT have long been established in clinical routine. Other techniques such as blood level oxygenation-dependent (BOLD), diffusion-weighted, and renal perfusion imaging using MRI or CT are used almost exclusively for research purposes, but they also have many possibilities for renal applications under study conditions in diagnostics and for monitoring vascular and parenchymatous disorders. © 2009 Springer-Verlag.

Einfluss von Lesefehlern auf die textbasierte automatische Verständlichkeitsanalyse - Haderlein, Tino; Schuster, Maria; Nöth, Elmar; Rosanowski, Frank (Aktuelle phoniatrisch-pädaudiologische Aspekte 2008, 2008)

Ein System zur Interpretation einer Äußerung: Methoden - Kuhn, Thomas; Kunzmann, Siegfried; Kummert, Franz; Mast, Marion; Niemann, Heinrich; Nöth, Elmar; Prechtel, Reinhard; Rieck, Stefan; Reißer, Anita; Sagerer, Gerhard (Fortschritte der Akustik - DAGA'91, 1991)

Electromagnets for an endoscopic anastomosis tool in the colon - Steger, Jana; Zimmermann, Andreas; Wittenberg, Thomas; Wilhelm, Dirk (Current Directions in Biomedical Engineering, 2021)
The goal of our research work is the development of a novel endoscopic anastomosis device for the colon. One of the main challenges in this context is the application of forces at the endoscope tip to rejoin the two bowel endings. Thus, we focus on a magnetic two-part compression implant approach. The implant halves are detached from the applicator units by means of electromagnets. In this contribution we present the results of our experiments to determine the implant design with special focus on tissue compression forces and the resultant electromagnet dimensioning to estimate size requirements of the application/detachment system. To achieve the targeted compression forces derived from literature, we used cubic N52 magnetized neodymium magnets1 with a side length of 5 mm and mild steel screws. For these magnets, we evaluated a required electromagnetic repulsion force of 4.1 N. For the electromagnetic detachment system this led to the need for 166 windings for the coils on oral side, and 146 windings for the coils at the aboral side. Based on these requirements, a colonoscope diameter (∼14 mm) increase of 10.6 mm on the oral side and of 12 mm on the aboral side due to the application device must be assumed. Nevertheless, this diameter still remains within the size range of other colonoscopic tools, such as e.g., circular staplers.
CRIS-Team Scopus Importer:2021-09-17
Keywords: Anastomosis; colon; electromagnets; endoscopic intervention; magnets

Electrophysiological and Mechanical Approaches to the Swallowing Analysis - Restrepo-Uribe, Juan Pablo; Roldan-Vasco, Sebastian; Perez-Giraldo, Estefania; Orozco Arroyave, Juan Rafael; Orozco-Duque, Andres (Communications in Computer and Information Science, 2020)
Alterations of the swallowing process at neurological and muscular levels can cause dysphagia. Its instrumental diagnosis is carried out by videofluoroscopy, which is invasive, expensive, and sometimes unavailable. Inexpensive and non-invasive alternatives have been proposed in several studies considering different biosignals including surface electromyography and accelerometry. Automatic approaches have been focused on unimodal analysis, disregarding the high complexity of the process. In this way, we performed a multimodal analysis of the swallowing process in 30 healthy individuals and 30 dysphagic patients, using surface electromyography and accelerometry-based cervical auscultation. Features in time, frequency, and time-frequency domains were extracted and selected for classification purposes. A support vector machine was used as a classifier, and hyperparameters were optimized by a grid search. The proposed scheme achieved an accuracy of 0.92 ± 0.07 in the best case. The performed method achieved promising results in terms of classification of dysphagic and healthy individuals, which could contribute to the development of non-invasive techniques of dysphagia screening.
CRIS-Team Scopus Importer:2020-11-06
Keywords: Accelerometry; Classification; Dysphagia; Feature selection; Surface electromyography; Swallowing

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

Ellipse-line-ellipse source trajectory and its R-line coverage for long-object cone-beam imaging with a C-arm system - Yu, Zhicong; Noo, F.; Lauritsch, G.; Dennerlein, Frank; Hornegger, Joachim (Medical Imaging 2012: Physics of Medical Imaging, 2012)
Over the last decade, significant progress has been made in terms of treatment of diseases using minimallyinvasive procedures. This progress was facilitated through multiple refinements of the imaging capabilities of C-arm systems in the interventional room, and more sophisticated procedures may become feasible by further refining the performance of these systems. Our primary focus is to eliminate two strong limitations of the current circular cone-beam imaging approach: cone-beam artifacts and limited extent of the volume covered in the direction of the patient bed. To solve this problem, we seek a source trajectory that (i) is complete in terms of Tuy's condition, (ii) can be periodically-repeated without discontinuities to allow long-object imaging, (iii) is practical, and (iv) offers full R-line coverage (an R-line is a line that connects any two source positions). A trajectory that satisfies all of our constraint is the Arc-Extended-Line-Arc(AELA) trajectory. Unfortunately, this trajectory does not allow smooth, continuous scanning at reasonable dose. In this work, we propose a new data acquisition geometry: the Ellipse-Line-Ellipse (ELE) trajectory. This geometry satisfies all of our constraints along with the attractive feature that smooth, continuous scanning at reasonable dose is enabled. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Embedded QRS Detection for Noisy ECG Sensor Data Using a Matched Filter and Directed Graph Search - Eskofier, Björn; Kornhuber, Johannes; Hornegger, Joachim (Russian-Bavarian Conference on Biomedical Engineering, 2008)

Embedded surface classification in digital sports - Eskofier, Björn; Oleson, M.; Dibenedetto, C.; Hornegger, Joachim (Pattern Recognition Letters, 2009)
In this presentation, we give a detailed analysis of the considerations needed for mapping the complete pattern classification chain to the restricted embedded system hardware environment. We describe the methodology of the design, realization and testing process that takes these hardware limitations into account. For this purpose, we consider a particular embedded application from the field of digital sports: a novel running shoe that is capable of sensing run-specific parameters and adapting the cushioning setting accordingly. Of utmost importance in this context is the classification of the current surface condition in order to enable optimal adaptation to the prevailing situation. Following our design approach, we provide a classification system with a runner-independent surface classification rate of more than 80%. This system is implemented in the current version of the aforementioned running shoe. The presented methodology is quite general as it makes no system-dependent assumptions and can thus be transferred to many other embedded classification applications. © 2009 Elsevier B.V. All rights reserved.

Emotional State Modeling for the Assessment of Depression in Parkinson’s Disease - Pérez Toro, Paula Andrea; Vásquez-Correa, Juan Camilo; Arias Vergara, Tomás; Klumpp, Philipp; Schuster, M.; Nöth, Elmar; Orozco-Arroyave, Juan Rafael (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021)
Parkinson’s disease (PD) results from the degeneration of dopamine in the substantia nigra, which plays a role in motor control, mood, and cognitive functions. Some processes in the brain of a PD patient can be overlapped with non-motor functions, where some of the same brain circuitry that is related to mood regulation is also affected. Commonly, most patients experience motor symptoms such as speech impairments, bradykinesia, or resting tremor; while non-motor symptoms such as sleep disorders or depression may also appear in PD. Depression is one of the most common non-motor symptoms developed by patients and is also associated with the rapid progression of motor impairments. This study proposes the use of the “Pleasure, Arousal, and Dominance Emotional State Model” (PAD) to capture similar aspects related to mood and affective states in PD patients. The PAD representation is commonly used to quantify and represent emotions in a multidimensional space. Acoustic information is used as input to feed a deep learning model based on convolutional and recurrent neural networks, which are trained to model the PAD representation. The proposed approach consists of performing transfer knowledge from the PAD model for the classification and the assessment of depression in PD. F1-scores of up to 0.69 are obtained for the classification of PD patients vs. healthy controls and of up to 0.85 for the discrimination between depressive PD vs. non-depressive PD patients, which confirms that there is information embedded in the PAD model that can be used to detect depression in PD.
CRIS-Team Scopus Importer:2021-10-01
Keywords: Speech analysis; Depression; Parkinson’s disease; PAD emotional state model

Emotion Identification for Evaluation of Synthesized Emotional Speech - Steidl, Stefan; Polzehl, Tim; Bunnell, H. Timothy; Dou, Ying; Muthukumar, Prasanna Kumar; Perry, Daniel; Prahallad, Kishore S.; Vaughn, Callie; Black, Alan W.; Metze, Florian (Proc. Speech Prosody 2012, 2012)
In this paper, we propose to evaluate the quality of emotional speech synthesis by means of an automatic emotion identification system. We test this approach using five different parametric speech synthesis systems, ranging from plain non-emotional synthesis to full re-synthesis of pre-recorded speech. We compare the results achieved with the automatic system to those of human perception tests. While preliminary, our results indicate that automatic emotion identification can be used to assess the quality of emotional speech synthesis, potentially replacing time consuming and expensive human perception test.
Keywords: emotion; speech synthesis; automatic quality assessment; human perception

Emotion Recognition from Speech: Putting ASR in the Loop - Schuller, Björn; Batliner, Anton; Steidl, Stefan; Seppi, Dino (Proc. Int. Conf. on Acoustics, Speech, and Signal Processing ICASSP 2009, 2009)
This paper investigates the automatic recognition of emotion from spoken words by vector space modeling vs. string kernels which have not been investigated in this respect, yet. Apart from the spoken content directly, we integrate Part-of-Speech and higher semantic tagging in our analyses. As opposed to most works in the field, we evaluate the performance with an ASR engine in the loop. Extensive experiments are run on the FAU Aibo Emotion Corpus of 4k spontaneous emotional child-robot interactions and show surprisingly low performance degradation with real ASR over transcription-based emotion recognition. In the result, bag of words dominate over all other modeling forms based on the spoken content, directly.
Keywords: speech analysis; speech recognition; feature extraction

Emotion Recognition from Speech under Environmental Noise Conditions using Wavelet Decomposition - Vasquez-Correa, J. C.; Garcia, N.; Orozco-Arroyave, J. R.; Arias-Londono, J. D.; Vargas-Bonilla, J. F.; Nöth, Elmar (2015)
Automatic emotion recognition considering speech signals has attracted the attention of the research community in the last years. One of the main challenges is to find suitable features to represent the affective state of the speaker. In this paper, a new set of features derived from the wavelet packet transform is proposed to classify different negative emotions such as anger, fear, and disgust, and to differentiate between those negative emotions and neutral state, or positive emotions such as happiness. Different wavelet decompositions are considered both for voiced and unvoiced segments, in order to determine a frequency band where the emotions are concentrated. Several measures are calculated in the wavelet decomposed signals, including log-energy, entropy measures, mel frequency cepstral coefficients, and the Lempel-Ziv complexity. The experiments consider two different databases extensively used in emotion recognition: the Berlin emotional database, and the enterface05 database. Also, in order to approximate to real-world conditions in terms of the quality of recorded speech, such databases are degraded using different environmental noise such as cafeteria babble, and street noise. The addition of noise is performed considering several signal to noise ratio levels which range from -3 to 6 dB. Finally, the effect produced by two different speech enhancement methods is evaluated. According to results, the features calculated from the lower frequency wavelet decomposition coefficients are able to recognize the fear-type emotions in speech. Also, one of the speech enhancement algorithms has proven to be useful to improve of the accuracy in cases of speech signals affected by highly background noise.
Keywords: Emotion recognition from speech; Wavelet packet transform; Low frequency evaluation; Noisy speech processing; Speech enhancement

Emotion Recognition Using Imperfect Speech Recognition - Metze, Florian; Batliner, Anton; Eyben, Florian; Polzehl, Tim; Schuller, Björn; Steidl, Stefan (Proceedings of Interspeech, 2010)
This paper investigates the use of speech-to-text methods for assigning an emotion class to a given speech utterance. Previous work shows that an emotion extracted from text can convey complementary evidence to the information extracted by classifiers based on spectral, or other non-linguistic features. As speech-to-text usually presents significantly more computational effort, in this study we investigate the degree of speech-to-text accuracy needed for reliable detection of emotions from an automatically generated transcription of an utterance. We evaluate the use of hypotheses in both training and testing, and compare several classification approaches on the same task. Our results show that emotion recognition performance stays roughly constant as long as word accuracy doesn’t fall below a reasonable value, making the use of speech-to-text viable for training of emotion classifiers based on linguistics.
Keywords: speech-to-text; emotion detection; meta-data extraction; rich transcription; children’s speech

Empirical Mode Decomposition articulation feature extraction on Parkinson's Diadochokinesia - Rueda, Alice; Vasquez Correa, Juan; Orozco Arroyave, Juan Rafael; Nöth, Elmar; Krishnan, Sridhar (Computer Speech and Language, 2022)
Empirical Mode Decomposition (EMD) was designed to analyze nonlinear and non-stationary signals. EMD voice analysis had been applied to Parkinson's sustained vowels, but very limited studies have been done on highly dynamic Diadochokinesia (DDK) utterances. This paper applies the EMD's dyadic filterbank characteristics to extract DDK features and an in-depth study on the efficacy of two segmentation strategies. The EMD analysis on DDK looks at the spectrum characteristics of Intrinsic Mode Functions (IMF) and the handling of mode mixing conditions. DDK recordings of Healthy Control (HC) subjects and patients with Parkinson's disease (PD) were segmented using various fixed frame sizes compared to dynamic segmentation based on/pa-ta-ka/ triad length, and also the signal envelope as a whole. An overlapping windowing of 2/3 was used in the fixed frame size segmentation to augment and to capture the redundant and transition information. No overlapping was used in the/pa-ta-ka/ triad segmentation. For the fixed frame size segmentation, we found that there is a region of consistency. Within this region, the IMF center frequencies and bandwidths maintained the same but varied outside the region. The segmentation comparisons used a basic set of EMD features with and without DeltaEMD features that capture segment-to-segment deviations. Using the basic EMD dyadic features, fixed frame size segmentation out-performed/pa-ta-ka/ triad segmentation. When DeltaEMD features were added to provide segment deviation information,/pa-ta-ka/ triad out-performed fixed frame segmentation. Additional segment-magnitude amplification factor and segment length were found to improve the performance of the/pa-ta-ka/ triad segmentation. With the added features,/pa-ta-ka/ triad out-performed the others and had an improved accuracy of 78%. Additional features have also increased the envelope discrimination to 76%. The results also indicated the potentials of using voice envelopes for PD analysis.
CRIS-Team WoS Importer:2021-12-24

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

Empowering Knowledge Based Speech Understanding through Statistics - Fischer, Julia; Haas, Jürgen; Nöth, Elmar; Niemann, Heinrich; Deinzer, Frank (Proc. Int. Conf. on Spoken Language Processing, 1998)

End-2-End Modeling of Speech and Gait from Patients with Parkinson’s Disease: Comparison Between High Quality Vs. Smartphone Data - Vasquez Correa, Juan; Arias Vergara, Tomás; Klumpp, Philipp; Pérez Toro, Paula Andrea; Orozco-Arroyave, Juan Rafael; Nöth, Elmar (ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021)
Keywords: Smartphones; Speech analysis; Gait analysis; Parkinson's disease; Deep learning

Endoscopic image rectification using gravity - Höller, Kurt Emmerich; Schneider, Armin; Jahn, Jasper; Gutierrez, Javier; Wittenberg, Thomas; Feußner, Hubertus; Hornegger, Joachim (International Journal of Computer Assisted Radiology and Surgery, 2010)

Endoscopic orientation correction - Höller, Kurt Emmerich; Penne, Jochen; Schneider, Armin; Jahn, Jasper; Guttierrez, Javier; Wittenberg, Thomas; Feußner, Hubertus; Hornegger, Joachim (Springer-verlag, 2009)
An open problem in endoscopic surgery (especially with flexible endoscopes) is the absence of a stable horizon in endoscopic images. With our "Endorientation" approach image rotation correction, even in non-rigid endoscopic surgery (particularly NOTES), can be realized with a tiny MEMS tri-axial inertial sensor placed on the tip of an endoscope. It measures the impact of gravity on each of the three orthogonal accelerometer axes. After an initial calibration and filtering of these three values the rotation angle is estimated directly. Achievable repetition rate is above the usual endoscopic video frame rate of 30Hz; accuracy is about one degree. The image rotation is performed in real-time by digitally rotating the analog endoscopic video signal. Improvements and benefits have been evaluated in animal studies: Coordination of different instruments and estimation of tissue behavior regarding gravity related deformation and movement was rated to be much more intuitive with a stable horizon on endoscopic images. © 2009 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.endosc_6
Keywords: EndoSens; Endorientation; Endoscopic Image Rectification; MEMS; NOTES; Natural Orifice Transluminal Endoscopic Surgery

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

End-to-End Parkinson’s Disease Detection Using a Deep Convolutional Recurrent Network - Rios-Urrego, Cristian David; Moreno-Acevedo, Santiago Andres; Nöth, Elmar; Orozco Arroyave, Juan Rafael (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
Deep Learning (DL) has enabled the development of accurate computational models to evaluate and monitor the neurological state of different disorders including Parkinson’s Disease (PD). Although researchers have used different DL architectures including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) units, fully connected networks, combinations of them, and others, but few works have correctly analyzed and optimized the input size of the network and how the network processes the information. This study proposes the classification of patients suffering from PD vs. healthy subjects using a 1D CNN followed by an LSTM. We show how the network behaves when its input and the kernel size in different layers are modified. In addition, we evaluate how the network discriminates between PD patients and healthy controls based on several speech tasks. The fusion of tasks yielded the best results in the classification experiments and showed promising results when classifying patients in different stages of the disease, which suggests the introduced approach is suitable to monitor the disease progression.
CRIS-Team Scopus Importer:2022-10-14
Keywords: Convolutional Neural Networks; Long Short-Term Memory; Parkinson’s Disease; Speech Processing

En Face Imaging of the Choroid in Polypoidal Choroidal Vasculopathy Using Swept-Source Optical Coherence Tomography - Alasil, Tarek; Ferrara, Daniela; Adhi, Mehreen; Brewer, Erika; Kraus, Martin; Baumal, Caroline R.; Hornegger, Joachim; Fujimoto, James G.; Witkin, Andre J.; Reichel, Elis; Duker, Jay S.; Waheed, Nadia K. (American Journal of Ophthalmology, 2015)
CONCLUSIONS: En face SS OCT provides an in vivo tool to visualize the pathologic features and the choroidal vasculature in PCV. (C) 2015 by Elsevier Inc. All rights reserved.

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

Entwicklung eines Software-Werkzeuges für Risiko-Management - Wilke, Peter; Schnaittinger, T.; Werner, F.; Lampka, K.; Grosser, N. (2001)
UnivIS-Import:2016-06-30:Pub.2001.tech.IMMD.IMMD2._entwi
Keywords: Entwicklung; Software; Werkzeug; Risiko-Management

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