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This competition investigates the performance of large-scale retrieval of historical document images based on writing style.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

ICDAR 2021 Competition on Historical Document Classification - Seuret, Mathias; Nicolaou, Anguelos; Rodríguez-Salas, Dalia; Weichselbaumer, Nikolaus; Stutzmann, Dominique; Mayr, Martin; Maier, Andreas;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This competition investigated the performance of historical document classification. The analysis of historical documents is a difficult challenge commonly solved by trained humanists.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Keywords: Dating; Document analysis; Document classification; Historical document images

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

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

Identifying failure root causes by visualizing parameter interdependencies with spectrograms - Baier, Lukas; Frommherz, Julian; Nöth, Elmar; Donhauser, Toni; Schuderer, Peter; Franke, Jörg (Journal of Manufacturing Systems, 2019)
Fast identification of failure root causes is a major task in minimizing rejects in manufacturing. Due to increasing complexity of products and supply chains many interdependencies affect the final product quality. As knowledge about every possible interdependence is hardly held by individuals, data analysis strategies are required to evaluate captured information. Hence, we propose a root cause identification method for determining the main influencing factors on end products failing end of line tests. Additionally, graphical representation of calculation results in spectrograms similar to audio signals facilitates human interpretation. The evaluation of the proposed method on the basis of a use case proves the applicability in a real scenario. The method is able to identify the root cause for rejects within short periods of time. In this specific case it shortened the analysis time by a factor of about 50. In the future, it empowers smart production systems to automatically identify failure root causes and to take countermeasures like adjusting process parameters.<br
Keywords: Quality management; Root cause analysis; Scrap reduction; Visual management

Iliac artery deformation during EVAR - Koutouzi, Giasemi; Pfister, Marcus; Breininger, Katharina; Hellström, Mikael; Roos, Håkan; Falkenberg, Mårten (Vascular, 2019)
Objectives: To quantify the deformation of the common iliac artery caused by stiff guide wires and delivery systems during abdominal endovascular aortic repair (EVAR). Methods: Twenty-two patients treated with abdominal EVAR were included. The following three image data-sets were acquired for each patient: (1) a preoperative computed tomography angiography (CTA), (2) an intraoperative contrast-enhanced cone beam CT (CBCT) obtained after the main trunk of the bifurcated stent graft was released and both iliac limbs were engaged with stiff guide wires, and (3) the first postoperative CTA. These data-sets were merged and compared in an image analysis work station. The length and the tortuosity index of the common iliac artery, the Euclidian displacement of the aortic and the iliac bifurcations, and the optimal C-arm angulation for projection of the iliac bifurcation were computed. Results: The common iliac artery was on average 6.4 mm shorter (p < 0.001) and tortuosity index was lower (p = 0.003) in the intraoperative images compared to preoperative. Some of the foreshortening was reversed postoperatively, remaining mean length difference was 2.9 mm (p = 0.007) compared to preoperative. Intraoperatively, the aortic bifurcation was mostly displaced in a cranial direction (100%) and the iliac bifurcation in a ventral direction (93%). The optimal lateral C-arm angulation for projection of the iliac bifurcation changed. Anterior contralateral angle increased from median 42° (IQR, 27–63) in the preoperative CTA to 62 (49–74) in the intraoperative CBCT (p = 0.02). Optimal cranio-caudal angulation did not change. Conclusion: Stiff guide wires and delivery systems cause significant deformation of the common iliac arteries during EVAR. The aortic bifurcation is more cranial, the common iliac arteries are shorter, and optimal C-arm angulation is more contralateral oblique when the iliac limbs are to be deployed compared to baseline measurements from preoperative CTA. This affects image fusion accuracy and stent graft selection.
CRIS-Team Scopus Importer:2019-07-30
Keywords: anatomy deformation; cone beam CT; endovascular aortic repair; Iliac artery; image fusion

Illuminant color estimation for real-world mixed-illuminant scenes - Riess, Christian; Eibenberger, Eva; Angelopoulou, Elli (IEEE International Conference on Computer Vision Workshops, 2011)

Illuminant Estimation by Voting - Riess, Christian; Eibenberger, Eva; Angelopoulou, Elli (2009)

Illumination invariant color texture analysis based on sum- and difference-histograms - Münzenmayer, Christian; Wilharm, Sylvia; Hornegger, Joachim; Wittenberg, Thomas (27th DAGM (German Association for Pattern Recognition) Symposium, DAGM 2005, 2005)
Color texture algorithms have been under investigation for quite a few years now. However, the results of these algorithms are still under considerable influence of the illumination conditions under which the images were captured. It is strongly desireable to reduce the influence of illumination as much as possible to obtain stable and satisfying classification results even under difficult imaging conditions, as they can occur e.g. in medical applications like endoscopy. In this paper we present the analysis of a well-known texture analysis algorithm, namely the sum- and difference-histogram features, with respect to illumination changes. Based on this analysis, we propose a novel set of features factoring out the illumination influence from the majority of the original features. We conclude our paper with a quantitative, experimental evaluation on artificial and real image samples. © Springer-Verlag Berlin Heidelberg 2005.

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

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

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

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

Image Based Time Series Synchronization for Periodically Moving Targets - Schaller, Christian; Khamene, Ali; Hornegger, Joachim (3rd Russian-Bavarian Conference on Biomedical Engineering, 2007)

Image fusion in X-ray differential phase-contrast imaging - Haas, Wilhelm; Polyanskaya, Maria; Bayer, Florian; Gödel, Karl Christoph; Hofmann, Hannes; Rieger, Jens; Ritter, André; Weber, Thomas; Wucherer, Lukas; Durst, Jürgen; Michel, Thilo; Anton, Gisela; et al. (Medical Imaging 2012: Image Processing, 2012)
Phase-contrast imaging is a novel modality in the field of medical X-ray imaging. The pioneer method is the grating-based interferometry which has no special requirements to the X-ray source and object size. Furthermore, it provides three different types of information of an investigated object simultaneously - absorption, differential phase-contrast and dark-field images. Differential phase-contrast and dark-field images represent a completely new information which has not yet been investigated and studied in context of medical imaging. In order to introduce phase-contrast imaging as a new modality into medical environment the resulting information about the object has to be correctly interpreted. The three output images reflect different properties of the same object the main challenge is to combine and visualize these data in such a way that it diminish the information explosion and reduce the complexity of its interpretation. This paper presents an intuitive image fusion approach which allows to operate with grating-based phase-contrast images. It combines information of the three different images and provides a single image. The approach is implemented in a fusion framework which is aimed to support physicians in study and analysis. The framework provides the user with an intuitive graphical user interface allowing to control the fusion process. The example given in this work shows the functionality of the proposed method and the great potential of phase-contrast imaging in medical practice. © 2012 SPIE.

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

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

Image quality assessment for extended-volume C-arm CT using a multi-turn reverse helix - Yu, Z.; Maier, Anne-Kathrin; Lauritsch, G.; Vogt, F.; Schonborn, M.; Kohler, C.; Hornegger, Joachim; Noo, F. (IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014, 2016)
C-arm computed tomography (CT) with axially extended field-of-view is valuable when imaging a long organ is desired in the interventional room. However, current C-arm CT employs a circular short scan that only provides incomplete data and short axial coverage. To enable long-object 3D imaging capability on a C-arm system, a multi-turn reverse helix is an attractive solution for data acquisition. We have implemented this trajectory on a state-of-the-art multi-axis C-arm system and performed image reconstruction using our Fusion-RFDK method. This work evaluates these reconstruction results by comparing them with those obtained from a circular short scan. We observed comparable image quality between the two source trajectories.

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

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

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

Image warping for 3-D reconstruction: Robustness and efficiency - Hornegger, Joachim; Tomasi, Carlo (Bildverarbeitung für die Medizin 2001, 2001)

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

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

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

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

Impact of the Non-Negativity Constraint in Model-Based Iterative Reconstruction from CT Data - Haase, Viktor; Hahn, Katharina; Schöndube, Harald; Stierstorfer, Karl; Noo, Frédéric (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT Meeting), 2018)
Model-based iterative reconstruction is a promising approach to achieve dose reduction without affecting image quality in diagnostic X-ray computed tomography. In the problem formulation, it is common to enforce non-negative values, which is motivated by physics but narrows down the choice of optimization algorithm. In this work, we report on experiments assessing the impact of the non-negativity constraint on image quality and reconstruction speed. The assessment is performed under eight scenarios that challenge the usefulness of the constraint. These include reconstructions from full and sparse view sampling, with quadratic or edge-preserving regularization, for two different objects. Our results show that improvements due to the nonnegativity constraint are strongly scenario-dependent, and likely negligible for conventional full view CT imaging. This implies that for specific reconstructions, the non-negativity constraint could be disregarded to simplify the optimization proble
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.impact_4

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

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

Implementation of a Talbot-Lau Interferometer in a Clinical-like C-Arm Setup: A Feasibility Study - Horn, Florian; Leghissa, Martino; Käppler, Sebastian; Pelzer, Georg; Rieger, Jens; Seifert, Maria; Wandner, Johannes; Weber, Thomas; Michel, Thilo; Riess, Christian; Anton, Gisela (Scientific Reports, 2018)

Implementation of the FDK algorithm for cone-beam CT on the cell broadband engine architecture - Scherl, Holger; Körner, Mario; Hofmann, Hannes; Eckert, Wieland; Kowarschik, Markus; Hornegger, Joachim (Medical Imaging 2007: Physics of Medical Imaging, 2007)
In most of today's commercially available cone-beam CT scanners, the well known FDK method is used for solving the 3D reconstruction task. The computational complexity of this algorithm prohibits its use for many medical applications without hardware acceleration. The brand-new Cell Broadband Engine Architecture (CBEA) with its high level of parallelism is a cost-efficient processor for performing the FDK reconstruction according to the medical requirements. The programming scheme, however, is quite different to any standard personal computer hardware. In this paper, we present an innovative implementation of the most time-consuming parts of the FDK algorithm: filtering and back-projection. We also explain the required transformations to parallelize the algorithm for the CBEA. Our software framework allows to compute the filtering and back-projection in parallel, making it possible to do an on-the-fly-reconstruction. The achieved results demonstrate that a complete FDK reconstruction is computed with the CBEA in less than seven seconds for a standard clinical scenario. Given the fact that scan times are usually much higher, we conclude that reconstruction is finished right after the end of data acquisition. This enables us to present the reconstructed volume to the physician in real-time, immediately after the last projection image has been acquired by the scanning device.

Implicit active shape model employing boundary classifier - Wimmer, Andreas; Hornegger, Joachim; Soza, Grzegorz (2008 19th International Conference on Pattern Recognition, ICPR 2008, 2008)
We present an algorithm for the segmentation of the liver in 2-D computed tomography slice images. The basis for our algorithm is an implicit active shape model. In order to detect the liver boundary and guide the shape model deformation, a boundary classifier has been integrated into the implicit framework in a novel manner. The accuracy of the algorithm has been evaluated for 20 test cases including both normal and abnormal livers. © 2008 IEEE.

Improved Pose Detection for Single Camera Real-Time MR Motion Correction Using a Self-Encoded Marker - Forman, Christoph; Aksoy, Murat; Straka, Matus; Hornegger, Joachim; Bammer, Roland (Proceedings of the 18th Annual Meeting of ISMRM & ESMRMB, 2010)

Improved Prospective Optical Motion Correction for DTI Using an Extended-Field-of-View and Self-Encoded Marker - Aksoy, Murat; Forman, Christoph; Straka, Matus; Holdsworth, Samantha; Skare, Stefan; Santos, Juan; Hornegger, Joachim; Bammer, Roland (Proceedings of the 18th Annual Meeting of ISMRM & ESMRMB, 2010)

Improved Reconstruction of Phase-Stepping Data for Talbot-Lau X-Ray Imaging - Käppler, Sebastian; Rieger, Jens; Pelzer, Georg; Horn, Florian; Michel, Thilo; Maier, Andreas; Anton, Gisela; Riess, Christian (Journal of Medical Imaging, 2017)
Keywords: phase contrast; phase stepping; moiré; reconstruction; Talbot-Lau

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

Improved trajectories in C-Arm computed tomography for non-circular fields of view - Herbst, Magdalena; Schebesch, Frank; Berger, Martin; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Proceedings of the third international conference on image formation in x-ray computed tomography, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.improv_5
Keywords: GRK-1773

Improved Workflow for Freehand MR-Guided Percutaneous Needle Interventions: Methods and Validation - Rothgang, Eva; Weiss, Clifford R.; Wacker, Frank; Hornegger, Joachim; Lorenz, Christine H.; Gilson, Wesley D. (Proceedings of International Society for Magnetic Resonance in Medicine, 2012)

Improved X-Ray Bone Segmentation by Normalization and Augmentation Strategies - Kordon, Florian Johannes; Lasowski, Ruxandra; Swartman, Benedict; Franke, Jochen; Fischer, Peter; Kunze, Holger (Bildverarbeitung für die Medizin 2019, 2019)
X-ray images can show great variation in contrast and noise levels. In addition, important subject structures might be superimposed with surgical tools and implants. As medical image datasets tend to be of small size, these image characteristics are often under-represented. For the task of automated, learning-based segmentation of bone structures, this may lead to poor generalization towards unseen images and consequently limits practical application. In this work, we employ various data augmentation techniques that address X-ray-specific image characteristics and evaluate them on lateral projections of the femur bone. We combine those with data and feature normalization strategies that could prove beneficial to this domain. We show that instance normalization is a viable alternative to batch normalization and demonstrate that contrast scaling and the overlay of surgical tools and implants in the image domain can boost the representational capacity of available image data. By employing our best strategy, we can improve the average symmetric surface distance measure by 36.22
Keywords: X-Ray; Femur; Bone segmentation; Orthopedics

Improvement and Evaluation of a Time-of-Flight based patient positioning system - Placht, Simon; Schaller, Christian; Balda, Michael; Adelt, André; Ulrich, Christian; Hornegger, Joachim (Bildverarbeitung für die Medizin, 2010)

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

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

Improving Accuracy in Coronary Lumen Segmentation via Explicit Calcium Exclusion, Learning-based Ray Detection and Surface Optimization - Lugauer, Felix; Zhang, Jingdan; Zheng, Yefeng; Hornegger, Joachim; Kelm, B. Michael (Proceedings SPIE, 2014)
Invasive cardiac angiography (catheterization) is still the standard in clinical practice for diagnosing coronary artery disease (CAD) but it involves a high amount of risk and cost. New generations of CT scanners can acquire high-quality images of coronary arteries which allow for an accurate identification and delineation of stenoses. Recently, computational fluid dynamics (CFD) simulation has been applied to coronary blood flow using geometric lumen models extracted from CT angiography (CTA). The computed pressure drop at stenoses proved to be indicative for ischemia-causing lesions, leading to non-invasive fractional flow reserve (FFR) derived from CTA. Since the diagnostic value of non-invasive procedures for diagnosing CAD relies on an accurate extraction of the lumen, a precise segmentation of the coronary arteries is crucial. As manual segmentation is tedious, time-consuming and subjective, automatic procedures are desirable. We present a novel fully-automatic method to accurately segment the lumen of coronary arteries in the presence of calcified and non-calcified plaque. Our segmentation framework is based on three main steps: boundary detection, calcium exclusion and surface optimization. A learning-based boundary detector enables a robust lumen contour detection via dense ray-casting. The exclusion of calcified plaque is assured through a novel calcium exclusion technique which allows us to accurately capture stenoses of diseased arteries. The boundary detection results are incorporated into a closed set formulation whose minimization yields an optimized lumen surface. On standardized tests with clinical data, a segmentation accuracy is achieved which is comparable to clinical experts and superior to current automatic methods. © 2014 SPIE.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.improv_60
Keywords: lumen segmentation; calicum suppression; CTA; CAD

Improving Children's Speech Recognition by HMM Interpolation with an Adults' Speech Recognizer - Steidl, Stefan; Stemmer, Georg; Hacker, Christian; Nöth, Elmar; Niemann, Heinrich (Pattern Recognition, 25th DAGM Symposium, Magdeburg, Germany, September 2003, Proceedings, 2003)
In this paper we address the problem of building a good speech recognizer if there is only a small amount of training data available. The acoustic models can be improved by interpolation with the well-trained models of a second recognizer from a different application scenario. In our case, we interpolate a children’s speech recognizer with a recognizer for adults’ speech. Each hidden Markov model has its ownset of interpolation partners; experiments were conducted with up to 50 partners. The interpolation weights are estimated automatically on a validation set using the EM algorithm. The word accuracy of the children’s speech recognizer could be improved from 74.6 % to 81.5 %. This is a relative improvement of almost 10 %.

Improving ensemble averaging by epoch detrending in evoked potentials - Torres-Rodríguez, Idileisy; Ferrer Riesgo, Carlos Ariel; Velarde-Reyes, Ernesto; Taboada-Crispi, Alberto (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
The objective of this work is to evaluate different detrending methods in the quality of auditory evoked responses. We compared the average responses obtained by simply removing the DC level and the linear trend, and also the estimated trends using polynomials and Fourier models up to the 8th order. Two quality measures were used to compare the results: The standard deviation ratio, as a measure of the signal-to-noise ratio, and the correlation coefficient between consecutive responses obtained under the same experimental conditions. The best results were obtained using a polynomial model of order 7.
CRIS-Team Scopus Importer:2019-08-13
Keywords: CCR; Detrending; Ensemble averages; Evoked potential; SDR

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

Improving Joint Learning of Suspended and Adherent Cell Detection Using Low-Pass Monogenic Phase and Transport of Intensity Equation - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim (IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.improv_8

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

Improving Parsing of Spontaneous Speech with the Help of Prosodic Boundaries - Kompe, Ralf; Kießling, Andreas; Niemann, Heinrich; Nöth, Elmar; Batliner, Anton; Schachtl, Stefanie; Ruland, Tobias; Block, Hans Ulrich (Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1997)

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

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

Increasing the Credibility of MR Spectroscopy-Based Automatic Brain-Tumor Classification Systems - Berger, Martin; Sembritzki, Klaus; Hornegger, Joachim; Bauer, Christina (2014 IEEE International Symposium on Biomedical Imaging, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.increa_2
Keywords: GRK-1773

Increasing the Flexibility in FPGA-Based Reconfigurable Platforms: The Erlangen Slot Machine - Ahmadinia, Ali; Bobda, Christophe; Haller, Thomas; Linarth, Andre Guilherme; Majer, Mateusz; Teich, Jürgen (IEEE 2005 Conference on Field-Programmable Technology, 2005)
We present a new concept as well as the implementation of an FPGA-based reconfigurable platform, the Erlangen Slot Machine (ESM). One main advantage of this platform is the possibility for each module to access its periphery independent from its location through a programmable crossbar, allowing an unrestricted relocation of modules on the device. Furthermore, we propose different intermodule communication structures. © 2005 IEEE.
UnivIS-Import:2015-04-16:Pub.2005.tech.IMMD.inform.increa

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

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

Inertial Sensor-Based Approach for Shot/Pass Classification During a Soccer Match - Schuldhaus, Dominik; Zwick, Constantin; Koerger, Harald; Dorschky, Eva; Kirk, Robert; Eskofier, Björn (21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2015)

Inertial Sensor based Gait Analysis Discriminates Subjects with and without Visual Impairment Caused by Simulated Macular Degeneration - Kanzler, Christoph M.; Barth, Jens; Klucken, Jochen; Eskofier, Björn (Proceedings of the 38th IEEE Engineering in Medicine and Biology Society Conference, 2016)
Macular degeneration is the third leading cause of blindness worldwide and the leading cause of blindness in the developing world. The analysis of gait parameters can be used to assess the influence of macular degeneration on gait. This study examines the effect of macular degeneration on gait using inertial sensor based 3D spatio-temporal gait parameters. We acquired gait data from 21 young and healthy subjects during a 40m obstacle walk. All subjects had to perform the gait trial with and without macular degeneration simulation glasses. The order of starting with or without glasses alternated between each subject in order to test for training effects. Multiple 3D spatio-temporal gait parameters were calculated for the normal vision as well as the impaired vision groups. The parameters trial time, stride time, stride time coefficient of variation (CV), stance time, stance time CV, stride length, cadence, gait velocity and angle at toe off showed statistically significant differences between the two groups. Training effects were visible for the trials which started without vision impair- ment. Inter-group differences in the gait pattern occurred due to an increased sense of insecurity related with the loss of visual acuity from the simulation glasses. In summary, we showed that 3D spatio-temporal gait pa- rameters derived from inertial sensor data are viable to detect differences in the gait pattern of subjects with and without a macular degeneration simulation. We believe that this study provides the basis for an in-depth analysis regarding the impact of macular degeneration on gait.
EVALuna2:33529

Inferring guidance information in cooperative human-robot tasks - Berger, Erik; Vogt, David; Haji Ghassemi, Nooshin; Jung, Bernhard; Ben Amor, Heni (Inferring guidance information in cooperative human-robot tasks, 2013)
In many cooperative tasks between a human and a robotic assistant, the human guides the robot by exerting forces, either through direct physical interaction or indirectly via a jointly manipulated object. These physical forces perturb the robot's behavior execution and need to be compensated for in order to successfully complete such tasks. Typically, this problem is tackled by means of special purpose force sensors which are, however, not available on many robotic platforms. In contrast, we propose a machine learning approach based on sensor data, such as accelerometer and pressure sensor information. In the training phase, a statistical model of behavior execution is learned that combines Gaussian Process Regression with a novel periodic kernel. During behavior execution, predictions from the statistical model are continuously compared with stability parameters derived from current sensor readings. Differences between predicted and measured values exceeding the variance of the statistical model are interpreted as guidance information and used to adapt the robot's behavior. Several examples of cooperative tasks between a human and a humanoid NAO robot demonstrate the feasibility of our approach.

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

Influence of Photon Count-Level on Semi-Automatic Image Assessment in Myocardial Perfusion Imaging - Sanders, James Chester; Wetzl, Matthias; Götz, Theresa; Kuwert, Torsten; Ritt, Philipp (2018)
EVALuna2:212304

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

Influence of Reverberation on Automatic Evaluation of Intelligibility with Prosodic Features - Haderlein, Tino; Döllinger, Michael; Schützenberger, Anne; Nöth, Elmar (Proc. Text, Speech and Dialogue; 19th International Conference, TSD 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.influe
Keywords: Sprache

Influence of the Interviewer on the Automatic Assessment of Alzheimer’s Disease in the Context of the ADReSSo Challenge - Pérez Toro, Paula Andrea; Bayerl, Sebastian; Arias Vergara, Tomás; Vasquez Correa, Juan; Klumpp, Philipp; Schuster, Maria; Nöth, Elmar; Orozco-Arroyave, Juan Rafael; Riedhammer, Korbinian (Proc. Interspeech 2021, 2021)
Keywords: Alzheimer's disease; Speaker modeling; Speech analysis; Emotional modeling; Natural language processing

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

Instrument Segmentation in Hybrid 3-D Endoscopy using Multi-Sensor Super-Resolution - Haase, Sven; Köhler, Thomas; Kilgus, Thomas; Maier-Hein, Lena; Hornegger, Joachim; Feußner, Hubertus (Computer- und Roboter Assistierte Chirurgie, 2013)

Integrated Recognition of Words and Phrase Boundaries - Gallwitz, Florian; Niemann, Heinrich; Nöth, Elmar; Warnke, Volker (Speech Communication, 2002)

Integrated recognition of words and prosodic phrase boundaries - Gallwitz, Florian; Niemann, Heinrich; Nöth, Elmar; Warnke, Volker (Elsevier, 2002)
In this paper, we present an integrated approach for recognizing both the word sequence and the syntactic-prosodic structure of a spontaneous utterance. The approach aims at improving the performance of the understanding component of speech understanding systems by exploiting not only acoustic-phonetic and syntactic information, but also prosodic information directly within the speech recognition process. Whereas spoken utterances are typically modelled as unstructured word sequences in the speech recognizer, our approach includes phrase boundary information in the language model and provides HMMs to model the acoustic and prosodic characteristics of phrase boundaries. This methodology has two major advantages compared to purely word-based speech recognizers. First, additional syntactic-prosodic boundaries are determined by the speech recognizer which facilitates parsing and resolve syntactic and semantic ambiguities. Second - after having removed the boundary information from the result of the recognizer - the integrated model yields a 4% relative word error rate (WER) reduction compared to a traditional word recognizer. The boundary classification performance is equal to that of a separate prosodic classifier operating on the word recognizer output, thus making a separate classifier unnecessary for this task and saving the computation time involved. Compared to the baseline word recognizer, the integrated word-and-boundary recognizer does not involve any computational overhead. (C) 2002 Elsevier Science B.V. All rights reserved.
Keywords: speech recognition; prosody; speech understanding

Integration of Genetic Algorithms and Fuzzy Logic into a Neural Network Simulation Environment, Analysis and Simulation of Computer and Telecommunication Systems - Wilke, Peter; Billing, Gunnar; Mansfeld, Christian; Nilson, Jörg (Proc. Int. Workshop on Modelling, MASCOTS95, 1995)

Integrity/Demyelination of the optic radiation, morphology of the papilla, and contrast sensitivity in glaucoma patients - Michelson, Georg; Wärntges, Simone; Engelhorn, Tobias; El-Rafei, Ahmed Mohamed Ibrahim; Hornegger, Joachim; Dörfler, Arnd (Klinische Monatsblätter für Augenheilkunde, 2012)
Background: In primary open angle glaucoma (POAG) and its non-barotraumatic subgroup, normal tension glaucoma (NTG), the pathophysiological differences are not clear. A participation of the 4th neuron of the visual pathway (optic radiation) appears possible on the basis of related experimental studies. The goal of the present study was the evaluation of the optic radiation by diffusion tensor imaging (DTI), which is based on the magnetic resonance imaging. The diffusion and anisotropy parameters of the optic radiation as a marker of axonal integrity and demyelination/damage of glial cells, respectively, were used to investigate the relation between the morphology of the papilla (BLDF, linear discriminant function of Burk) and the contrast sensitivity (FDT, frequency doubling test). Patients and Methods: In this prospective observational study 13 POAG patients, 13 NTG patients, and 7 control patients of the same mean age were included. For segmentation of the optic radiation a semi-automated algorithm was applied and the diffusion and anisotropy parameters were calculated. The importance of the covariates age, BLDF, and FDT for the DTI parameters was determined using partial correlation analysis. Results: Analysis of the covariates partially showed a clear autocorrelation. The correlations between the DTI parameters and BLDF were significant in all groups after correction of the measurement values for the covariates. FDT correlated with DTI parameters in controls and POAG. The NTG group did not show this correlation due to a strong spreading of the FDT values. Conclusion: After statistical elimination of the autocorrelation of the covariates age, BLDF, and FDT the morphology of the papilla correlated with the axonal integrity and demyelination/glia cell impairment of the optic radiation in controls and glaucoma. In NTG the impaired contrast sensitivity is highly variable and is not associated with the condition of the 3rd or 4th neuron, respectively, as compared to POAG. The autocorrelation between individual covariates represents an important element for the judgement of the visual pathway. © Georg Thieme Verlag KG · Stuttgart · New York.

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

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

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

Intelligibility of laryngectomees' substitute speech: automatic speech recognition and subjective rating - Schuster, Maria; Haderlein, Tino; Nöth, Elmar; Lohscheller, Jörg; Eysholdt, Ulrich; Rosanowski, Frank (European Archives of Oto-Rhino-Laryngology, 2006)

Intelligibility Rating with Automatic Speech Recognition, Prosodic, and Cepstral Evaluation - Haderlein, Tino; Moers, Cornelia; Möbius, Bernd; Rosanowski, Frank; Nöth, Elmar (Proc. Text, Speech and Dialogue; 14th International Conference, 2011)

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

Intensity-Based 3D-Reconstruction of Non-rigid Moving Stenosis from Many Angiographies - Bouattour, Sahla; Heigl, Benno; Hornegger, Joachim; Paulus, Dietrich (Bildverarbeitung für die Medizin 2004, 2004)

Intensity-based 3-D Reconstruction with Non-linear Optimization - Hopfgartner, Christian; Scholz, Ingo; Gugat, Martin; Leugering, Günter; Hornegger, Joachim (ICGST International Journal on Graphics, Vision and Image Processing, 2010)

Intensity based Three-Dimensional Reconstruction with Nonlinear Optimization - Hopfgartner, Christian; Scholz, Ingo; Gugat, Martin; Leugering, Günter; Hornegger, Joachim (2007)
New images of a three-dimensional scene can be generated from known image sequences using lightfields. To get high quality images, it is important to have accurate information about the structure of the scene. In order to optimize this information, we define a residual-function. This function represents the difference between an image, rendered in a known view from neighboured images and the original image at the same position. In order to get optimal results, we minimize the residual-function by defining a nonlinear least-squares problem, which is solved by an appropriate optimization method. We use a nonmonotone variant of the Levenberg-Marquardt method.
Keywords: Nonlinear Optimization; Imaging; Rendering; Nonmonotone Levenberg-Marquardt; 90C30; 68U10; 94A08

Interactive Analysis of Multispectral and Hyperspectral Image Data - Jordan, Johannes Michael (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2017)
A multispectral or hyperspectral sensor captures images of high spectral resolution by dividing the light spectrum into many narrow bands. With the advent of affordable and flexible sensors, the modality is constantly widening its range of applications. This necessitates novel tools that allow general and intuitive analysis of the image data. In this work, a software framework is presented that bundles interactive visualization techniques with powerful analysis capabilities and is accessible through efficient computation and an intuitive user interface. Towards this goal, several algorithmic solutions to open problems are presented in the fields of edge detection, clustering, supervised segmentation and visualization of hyperspectral images. In edge detection, the structure of a scene can be extracted by finding discontinuities between image regions. The high dimensionality of hyperspectral data poses specific challenges for this task. A solution is proposed based on a data-driven pseudometric. The pseudometric is computed through a fast manifold learning technique and outperforms established metrics and similarity measures in several edge detection scenarios. Another approach to scene understanding in the hyperspectral or a derived feature space is data clustering. Through pixel-cluster assignment, a global segmentation of an image is obtained based on reflectance effects and materials in the scene. An established mode-seeking method provides high-quality clustering results, but is slow to compute in the hyperspectral domain. Two methods of speedup are proposed that allow computations for interactive use. A further method is proposed that finds clusters in a learned topological representation of the data manifold. Experimental results demonstrate a quick and accurate clustering of the image data without any assumptions or prior knowledge, and that the proposed methods are applicable for the extraction of material prototypes and for fuzzy clustering of reflectance effects. For supervised image analysis, an algorithm for seed-based segmentation is introduced to the hyperspectral domain. Specific segmentations can be quickly obtained by giving cues about regions to be included in or excluded from a segment. The proposed method builds on established similarity measures and the proposed data-driven pseudometric. A new benchmark is introduced to assess its performance. The aforementioned analysis methods are then combined with capable visualization techniques. A method for non-linear false-color visualization is proposed that distinguishes captured spectra in the spatial layout of the image. This facilitates the finding of relationships between objects and materials in the scene. Additionally, a visualization for the spectral distribution of an image is proposed. Raw data exploration becomes more feasible through manipulation of this plot and its link to traditional displays. The combination of false-color coding, spectral distribution plots, and traditional visualization enables a new workflow in manual hyperspectral image analysis.<br
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.intera
Keywords: Hyperspectral Imaging; Imaging Spectroscopy; Interactive Analysis; Self-organizing Map

Interdisciplinary research! Was it worthwhile? - Wittenberg, Thomas; Nkenke, Emeka; Hornegger, Joachim (35th Jahrestagung der Gesellschaft fur Informatik e.V. (GI): Informatik LIVE!, INFORMATIK 2005 35th Annual Conference of the German Informatics Society (GI): Informatics LIVE!, INFORMATIK 2005, 2005)

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

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

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

Interventional 4-D C-Arm CT Perfusion Imaging Using Interleaved Scanning and Partial Reconstruction Interpolation - Fieselmann, Andreas; Ganguly, Arundhuti; Yu Deuerling-Zheng,; Zellerhoff, Maria; Rohkohl, Christopher; Boese, Jan; Hornegger, Joachim; Fahrig, Rebecca (IEEE Transactions on Medical Imaging, 2012)

Interventional 4D motion estimation and reconstruction of cardiac vasculature without motion periodicity assumption - Rohkohl, Christopher; Lauritsch, G.; Biller, L.; Prümmer, Marcus; Boese, J.; Hornegger, Joachim (Medical Image Analysis, 2010)
Anatomical and functional information of cardiac vasculature is a key component in the field of interventional cardiology. With the technology of C-arm CT it is possible to reconstruct static intraprocedural 3D images from angiographic projection data. Current approaches attempt to add the temporal dimension (4D). In the assumption of periodic heart motion, ECG-gating techniques can be used. However, arrhythmic heart signals and slight breathing motion are degrading image quality frequently.To overcome those problems, we present a reconstruction method based on a 4D time-continuous B-spline motion field. The temporal component of the motion field is parameterized by the acquisition time and does not assume a periodic heart motion. The analytic dynamic FDK-reconstruction formula is used directly for the motion estimation and image reconstruction.In a physical phantom experiment two vessels of size 3.1. mm and 2.3. mm were reconstructed using the proposed method and an algorithm with periodicity assumption. For a periodic motion both methods obtained an error of 0.1. mm. For a non-periodic motion the proposed method was superior, obtaining an error of 0.3. mm/0.2. mm in comparison to 1.2. mm/1.0. mm for the algorithm with periodicity assumption. For a clinical test case of a left coronary artery it could be further shown that the method is capable to produce diameter measurements with an absolute error of 0.1. mm compared to state-of-the-art measurement tools from orthogonal coronary angiography. Further, it is shown for three different clinical cases (left/right coronary artery, coronary sinus) that the proposed method is able to handle a large variability of vascular structures and motion patterns. The complete algorithm is hardware-accelerated using the GPU requiring a computation time of less than 3. min for typical clinical scenarios. © 2010 Elsevier B.V.

Interventional 4-D Motion Estimation and Reconstruction of Cardiac Vasculature without Motion Periodicity Assumption - Rohkohl, Christopher; Lauritsch, Günter; Prümmer, Marcus; Hornegger, Joachim (Springer-verlag, 2009)
Anatomical and functional information of cardiac vasculature is a key component of future developments in the field of interventional cardiology. With the technology of C-arm CT it is possible to reconstruct intraprocedural 3-D images from angiographic projection data. Current approaches attempt to add the temporal dimension (4-D) by ECG-gating in order to distinct physical states of the heart. This model assumes that the heart motion is periodic. However, frequently arrhytmic heart signals are observed in a clinical environment. In addition breathing motion can still occur. We present a reconstruction method based on a 4-D time-continuous motion field which is parameterized by the acquisition time and not the quasi-periodic heart phase. The output of our method is twofold. It provides a motion compensated 3-D reconstruction (anatomic information) and a motion field (functional information). In a physical phantom experiment a vessel of size 3.08 mm undergoing a non-periodic motion was reconstructed. The resulting diameters were 3.42 mm and 1.85 mm assuming non-periodic and periodic motion, respectively. Further, for two clinical cases (coronary arteries and coronary sinus) it is demonstrated that the presented algorithm outperforms periodic approaches and is able to handle realistic irregular heart motion. © 2009 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.interv

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

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

Interventional MR-imaging for thermal ablation therapy - Rothgang, Eva; Gilson, Wesley D.; Strehl, Wilhelm; Pan, Li; Roland, Jörg; Lorenz, Christine H.; Hornegger, Joachim (2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11, 2011)
Magnetic Resonance Imaging (MRI) has several unique advantages for guiding thermal ablation therapies. It not only provides excellent soft-tissue contrast and multiplanar capabilities, but also is sensitive to thermal effects. To make full use of these advantages for thermal ablation procedures, we present an integrated solution for a thermal therapy workflow that combines dedicated MRI pulse sequences and visualization/analysis tools for trajectory planning, automatic slice positioning for image-guided needle placement, and advanced MR thermal mapping. The paper highlights a novel approach to detect the needle in real-time MR images and to automatically realign the scan planes. In addition, a global approach to correct for B field shift during online MR thermometry is introduced. The application has been implemented using the open-source eXtensible Imaging Platform (XIP). © 2011 IEEE.

Interventionelle 4-dimensionale Darstellung der Hirnperfusion - Fieselmann, Andreas; Hornegger, Joachim; Fahrig, Rebecca; Deuerling-Zheng, Yu; Zellerhoff, Michael; Boese, Jan; Ganguly, Arundhuti; Rohkohl, Christopher (RöFo : Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2012)
UnivIS-Import:2016-02-10:Pub.2012.tech.IMMD.IMMD5.interv_2

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

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

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

Introduction to the Special Issue on Broadening the View on Speaker Analysis (Editorial) - Schuller, Björn; Steidl, Stefan; Batliner, Anton; Schiel, Florian; Krajewski, Jarek (Computer Speech and Language, 2014)

Introduction to the Special Issue on Next Generation Computational Paralinguistics (Editorial) - Schuller, Björn; Steidl, Stefan; Batliner, Anton; Vinciarelli, Alessandro; Burkhardt, Felix; van Son, Rob (Computer Speech and Language, 2015)
UnivIS-Import:2015-03-11:Pub.2015.tech.IMMD.IMMD5.introd

Introduction to the Special Issue on Paralinguistics in Naturalistic Speech and Language (Editorial) - Schuller, Björn; Steidl, Stefan; Batliner, Anton (Computer Speech and Language, 2013)

Introduction to the Special Issue on Sensing Emotion and Affect - Facing Realism in Speech Processing (Editorial) - Schuller, Björn; Batliner, Anton; Steidl, Stefan (Speech Communication, 2011)

Intuitive and Smart Editing of Three-Dimensional Geometric Heart Valve Apparatus Models from Cardiac CT Data - Lades, Félix; Wels, Michael; Steidl, Stefan; Sühling, Michael (The 2nd Interactive Medical Image Computing Workshop, 2015)
In cases of aortic valve diseases, diagnosis and subsequent interventional planning are highly facilitated by deriving exact three-dimensional geometric models of a patient's aortic valve apparatus from Cardiac Computed Tomography Angiography data. Fully-automatic approaches to do so however lack in absolute reliability and manual editing of the initially detected geometric model is often required. We therefore present an interactive editing method for this scenario - in particular for editing the aortic root model - based on the As-Rigid-As-Possible (ARAP) surface modeling paradigm, which allows efficient, robust, intuitive and physically plausible deformations of three-dimensional geometric models. The user constrains a model's surface by setting and moving handles - the so-called constraints - while the remaining, and only the remaining, free part of the surface is deformed automatically in a way such that the global shape is preserved in real-time. We extended the classical ARAP approach for our scenario by an energy smoothness regularization to overcome non-smooth artifacts at constrained positions. We furthermore incorporated the use of image evidence-based cues in the interactive work ow such that handles can be made "snap" into visible 3-D surface indicators. We evaluated our method in a user study regarding intuitiveness, achievable accuracy, inter-user variability, required time and robustness. The participants started with an initial average mesh-to-mesh surface error of 1.65 mm and achieved after 50 mouse clicks on average and less than 3.5 minutes an average error of 0.48 mm with respect to an expert-de ned ground truth. The inter-user variability was 0.43 mm.
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.intuit

Inverse C-arm positioning for interventional procedures using real-time body part detection - Schaller, Christian; Rohkohl, Christopher; Penne, Jochen; Stürmer, Michael; Hornegger, Joachim (Springer-verlag, 2009)
The automation and speedup of interventional therapy and diagnostic workflows is a crucial issue. One way to improve these workflows is to accelerate the image acquisition procedures by fully automating the patient setup. This paper describes a system that performs this task without the use of markers or other prior assumptions. It returns metric coordinates of the 3-D body shape in real-time for inverse positioning. This is achieved by the application of an emerging technology, called Time-of-Flight (ToF) sensor. A ToF sensor is a cost-efficient, off-the-shelf camera which provides more than 40,000 3-D points in real-time. The first contribution of this paper is the incorporation of this novel imaging technology (ToF) in interventional imaging. The second contribution is the ability of a C-arm system to position itself with respect to the patient prior to the acquisition. We are using the 3-D surface information of the patient to partition the body into anatomical sections. This is achieved by a fast two-stage classification process. The system computes the ISO-center for each detected region. To verify our system we performed several tests on the ISO-center of the head. Firstly, the reproducibility of the head ISO-center computation was evaluated. We achieved an accuracy of (x: 1.73±1.11 mm/y: 1.87±1.31 mm/z: 2.91±2.62 mm). Secondly, a C-arm head scan of a body phantom was setup. Our system automatically aligned the ISO-center of the head with the C-arm ISO-center. Here we achieved an accuracy of ± 1 cm, which is within the accuracy of the patient table control. © 2009 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.invers_6

Inverse root sampling pattern for iterative reconstruction in non-CE MR angiography - Hutter, Jana; Grimm, Robert; Forman, Christoph; Hornegger, Joachim; Schmitt, Peter (Magnetic Resonance Materials in Physics, Biology and Medicine, 2011)

Investigating Contrast Settlement Using Virtual Angiography - Endres, Jürgen; Redel, Thomas; Kowarschik, Markus; Hornegger, Joachim (Bildverarbeitung für die Medizin: Algorithmen, Systeme, Anwendungen, 2014)
Hemodynamic parameters based on the temporal behavior of contrast agent flow in cerebral aneurysms represent important indicators of the effectiveness of deployed micro devices. These measurements are also interesting for the assessment of virtual treatment planning strategies such as virtual device implantation combined with CFD simulations of blood flow and subsequently generated synthetic angiograms (virtual angiography). Due to settlement effects, contrast agent residence time may increase. As of today, virtual angiography does not explicitly model these effects such that differences between real and virtual angiograms are existent. Hence, we present an approach to examine this contrast agent settlement in virtual angiograms by adding a gravitational effect on simulated contrast agent. The model is evaluated on several cases with different characteristics by generating virtual angiograms with and without the proposed gravity model and comparisons against acquired, real angiograms. Primarily with regards to wash-out behavior and residence time, virtual angiograms including a gravity component show a significantly improved concordance with acquired angiograms.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.invest_8

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

In vitro evaluation of the imaging accuracy of C-Arm conebeam CT in cerebral perfusion imaging - Ganguly, A.; Fieselmann, Andreas; Boese, J.; Rohkohl, Christopher; Hornegger, Joachim; Fahrig, R. (Medical Physics, 2012)
Purpose: The authors have developed a method to enable cerebral perfusion CT imaging using C-Arm based conebeam CT (CBCT). This allows intraprocedural monitoring of brain perfusion during treatment of stroke. Briefly, the technique consists of acquiring multiple scans (each scan comprised of six sweeps) acquired at different time delays with respect to the start of the x-ray contrast agent injection. The projections are then reconstructed into angular blocks and interpolated at desired time points. The authors have previously demonstrated its feasibility in vivo using an animal model. In this paper, the authors describe an in vitro technique to evaluate the accuracy of their method for measuring the relevant temporal signals. Methods: The authors' evaluation method is based on the concept that any temporal signal can be represented by a Fourier series of weighted sinusoids. A sinusoidal phantom was developed by varying the concentration of iodine as successive steps of a sine wave. Each step corresponding to a different dilution of iodine contrast solution contained in partitions along a cylinder. By translating the phantom along the axis at different velocities, sinusoidal signals at different frequencies were generated. Using their image acquisition and reconstruction algorithm, these sinusoidal signals were imaged with a C-Arm system and the 3D volumes were reconstructed. The average value in a slice was plotted as a function of time. The phantom was also imaged using a clinical CT system with 0.5 s rotation. C-Arm CBCT results using 6, 3, 2, and 1 scan sequences were compared to those obtained using CT. Data were compared for linear velocities of the phantom ranging from 0.6 to 1 cms. This covers the temporal frequencies up to 0.16 Hz corresponding to a frequency range within which 99 of the spectral energy for all temporal signals in cerebral perfusion imaging is contained. Results: The errors in measurement of temporal frequencies are mostly below 2 for all multiscan sequences. For single scan sequences, the errors increase sharply beyond 0.10 Hz. The amplitude errors increase with frequency and with decrease in the number of scans used. Conclusions: Our multiscan perfusion CT approach allows low errors in signal frequency measurement. Increasing the number of scans reduces the amplitude errors. A two-scan sequence appears to offer the best compromise between accuracy and the associated total x-ray and iodine dose. © 2012 American Association of Physicists in Medicine.

In-vitro Evaluation von endoskopischer Oberflächenrekonstruktion mittels Time-of-Flight-Kameratechnik - Groch, Anja; Hempel, Sarah-Marie; Speidel, Stefanie; Höller, Kurt Emmerich; Engelbrecht, Rainer; Penne, Jochen; Seitel, Alexander; Röhl, Sebastian; Yung, Kwong; Bodenstedt, Sebastian; Pflaum, Felix; Kilgus, Thomas; et al. (Workshop Bildverarbeitung für die Medizin, 2011)
UnivIS-Import:2015-04-16:Pub.2011.tech.IE.LEH.invitr

In vivo imaging of the rodent eye with swept source/Fourier domain OCT - Liu, Jonathan J.; Grulkowski, Ireneusz; Kraus, Martin; Potsaid, Benjamin; Lu, Chen D.; Baumann, Bernhard; Duker, Jay S.; Hornegger, Joachim; Fujimoto, James G. (Biomedical Optics Express, 2013)
Swept source/Fourier domain OCT is demonstrated for in vivo imaging of the rodent eye. Using commercial swept laser technology, we developed a prototype OCT imaging system for small animal ocular imaging operating in the 1050 nm wavelength range at an axial scan rate of 100 kHz with 6 μm axial resolution. The high imaging speed enables volumetric imaging with high axial scan densities, measuring high flow velocities in vessels, and repeated volumetric imaging over time. The 1050 nm wavelength light provides increased penetration into tissue compared to standard commercial OCT systems at 850 nm. The long imaging range enables multiple operating modes for imaging the retina, posterior eye, as well as anterior eye and full eye length. A registration algorithm using orthogonally scanned OCT volumetric data sets which can correct motion on a per A-scan basis is applied to compensate motion and merge motion corrected volumetric data for enhanced OCT image quality. Ultrahigh speed swept source OCT is a promising technique for imaging the rodent eye, proving comprehensive information on the cornea, anterior segment, lens, vitreous, posterior segment, retina and choroid. © 2013 Optical Society of America.

In Vivo Lamina Cribrosa Micro-Architecture in Healthy and Glaucomatous Eyes as Assessed by Optical Coherence Tomography - Wang, Bo; Nevins, Jessica E.; Nadler, Zach; Wollstein, Gadi; Ishikawa, Hiroshi; Bilonick, Richard A.; Kagemann, Larry; Sigal, Ian A.; Grulkowski, Ireneusz; Liu, Jonathan J.; Kraus, Martin; Lu, Chen D.; et al. (Investigative Ophthalmology & Visual Science, 2013)
Keywords: lamina cribrosa; optical coherence tomography; glaucoma

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

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

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

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

Isotropic reconstruction of SPECT data using OSEM3D: Correlation with CT - Römer, Wolfgang; Reichel, Nicky; Vija, Hans; Nickel, Ingo; Hornegger, Joachim; Bautz, Werner; Kuwert, Torsten (Academic Radiology, 2006)
Rationale and Objectives. A statistical based iterative single-photon emission-computed tomography (SPECT) reconstruction algorithm (OSEM) modeling the depth-dependent collimator response in three dimensions has recently been introduced (OSEM3D). The aim of this study was to evaluate the axial shape fidelity of OSEM3D in comparison to OSEM, not taking this variable into account (OSEM2D). Materials and Methods. SPECT and separate spiral CT were performed in a phantom containing spheres filled with In-111. In-111-pentetreotide-SPECT and separate spiral-CT imaging were also performed in 22 patients with neuroendocrine tumors. Using window settings adapting the transversal size of the SPECT hot spots to that on CT and the 50% isocontour as boundary, the three-dimensional extensions (dx, dy, dz) of the SPECT representation of the structures under study were measured. These variables were also determined for CT. Furthermore, an index of eccentricity was calculated by averaging the ratios between dz and dx and dz and dy (IE). For isotropically imaged spheres, IE is 1. Results. For OSEM2D, IE was significantly different from 1 in the phantom data (P < .05); this was not the case for OSEM3D and CT. This finding was accounted for by a significantly greater dz on the OSEM2D-SPECT images. In the patient data, dz was by approximately 15.5% greater for OSEM2D than for the other two modalities (P < .05). Conclusions. The use of OSEM3D avoids deformation of hot SPECT lesions in z-direction. This may be of particular importance in SPECT/CT hybrid imaging capitalizing on the exact match of both modalities. © AUR, 2006.

Is There Any Additional Information in a Neural Network Trained for Pathological Speech Classification? - Rios-Urrego, C. D.; Vásquez-Correa, J. C.; Orozco-Arroyave, J. R.; Nöth, Elmar (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021)
Speech is a biomarker extensively explored by the scientific community for different health-care applications because its reduced cost and non-intrusiveness. Specifically, in Parkinson’s disease, speech signals and deep learning methods have been explored for the automatic assessment and monitoring of patients. Related studies have shown to be very accurate to discriminate pathological vs. healthy speech. In spite of the high accuracies observed to detect the presence of diseases from speech, it is not clear which additional information about the speakers or the environment is implicitly learned by the deep learning systems. This study proposes a methodology to evaluate intermediate representations of a neural network in order to find out which other speaker traits and aspects are learned by the system during the training process. We trained models to detect the presence of Parkinson’s disease from speech. Then, we used intermediate representations of the network to classify additional speaker traits such as gender, age, and the native language. It is important to detect which information is available inside the neural network that can lead to open the black-box and to detect possible algorithmic biases. The results indicate that the network, in addition to adjusting its parameters for disease classification, also acquires knowledge about gender of the speakers in the first layers, and about speech tasks and the native language in the last layers of the network.
CRIS-Team Scopus Importer:2021-10-01
Keywords: Deep learning; Neural networks; Parkinson’s disease; Pathological speech; Speech processing

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

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

Iterative CT reconstruction with small pixel size: distance-driven forward projector versus Joseph's - Hahn, K.; Rassner, U.; Davidson, H. C.; Sehoendube, H.; Stierstorfer, K.; Hornegger, Joachim; Noo, F. (Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94123D (March 18, 2015), 2015)
Over the last few years, iterative reconstruction methods have become an important research topic in x-ray CT imaging. This effort is motivated by increasing evidence that such methods may enable significant savings in terms of dose imparted to the patient. Conceptually, iterative reconstruction methods involve two important ingredients: the statistical model, which includes the forward projector, and a priori information in the image domain, which is expressed using a regularizer. Most often, the image pixel size is chosen to be equal (or close) to the detector pixel size (at field-of-view center). However, there are applications for which a smaller pixel size is desired. In this investigation, we focus on reconstruction with a pixel size that is twice smaller than the detector pixel size. Using such a small pixel size implies a large increase in computational effort when using the distance-driven method for forward projection, which models the detector size. On the other hand, the more efficient method of Joseph will create imbalances in the reconstruction of each pixel, in the sense that there will be large differences in the way each projection contributes to the pixels. The purpose of this work is to evaluate the impact of these imbalances on image quality in comparison with utilization of the distance-driven method. The evaluation involves computational effort, bias and noise metrics, and LROC analysis using human observers. The results show that Joseph's method largely remains attractive.

Iterative Multi-Slice Compressed Sensing Reconstruction for Peripheral MRA - Hutter, Jana; Grimm, Robert; Ciocan, Monica; Hornegger, Joachim; Schmitt, Peter (Proceedings of the annual conference of the ESMRMB, 2013)
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.iterat_17

Iterative Reconstruction Methods for Quantitative Abdominal MR Imaging - Lugauer, Felix (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Abdominal pathologies, in particular, diffuse liver disorders are affecting a rapidly increasing group of people worldwide. Non-alcoholic Fatty Liver Disease (NAFLD) parallels a global epidemic and is the most common liver disease in societies with westernized lifestyles, affecting in between 20 and 30 % of adults. It is closely connected to obesity, whose prevalence almost tripled since 1975. NAFLD comprises a wide range of disease stages, which range from steatosis over steatohepatitis to fibrosis and, eventually, cirrhosis. Diagnosis and disease stratification still requires invasive biopsy, which bears the risk of complications. With magnetic resonance imaging (MRI), a noninvasive and non-ionizing technology has evolved that is able to yield biomarkers such as fat fraction (FF), R2* or T1, allowing the diagnosis of steatosis, fibrosis and cirrhosis. A major limitation for its widespread use is that single contrasts are per se not quantitative. Instead, a series of acquisitions is required in order to derive a quantitative MR (qMR) parameter based on an appropriate signal model. Consequently, qMRI is even more time-consuming than simple soft-tissue or morphological imaging. Further challenges arise from breathing motion accompanied by any abdominal examination. Despite steady advances in accelerating Cartesian MR measurements such as partial Fourier or parallel imaging (PI), lengthy scan times either entirely prohibit qMRI in clinical practice or drastically limit spatial coverage, resolution or accuracy in order to meet a patient’s breath-hold abilities. In this thesis, we address the above limitations by combining PI with compressed sensing (CS) and domain-specific regularization in order to accelerate and enhance qMRI applications of water-fat and relaxation quantification. To this end, a local low-rank (LLR) prior is identified as a common sparsity basis among images required for water-fat quantification. Implemented as an automated and robust denoiser for multi-contrast images, it strongly reduces noise floor effects and uncertainty in FF and R2* quantification without degrading structural information. Combining this LLR constraint with spatial sparsity regularization in an iterative reconstruction algorithm for sparsely sampled multi-echo data achieves strong noise reduction and image enhancement despite a 30 % decrease in scan time as compared to the state-of-the-art. In the context of abdominal T1 mapping where accurate techniques currently cover only few slices, we developed a segmented 3-D imaging sequence based on spatiotemporally sparse sampling, which achieves whole-liver coverage within a single breath-hold. Acceleration factors of 12 and beyond are possible through a novel spatiotemporally incoherent Poisson sampling with variable density and signal enhancing through-contrast regularization. In summary, the described techniques encompass advances in domain-specific reconstruction techniques, sampling strategies and sequence programs. All algorithms were validated by volunteer studies with at least ten volunteers and integrated into the MR scanner software as application extension.<br
Keywords: Iterative Bildrekonstruktion; MRT; Medizinische Bildgebung; Compressed Sensing; Iterative Reconstruction; Quantitative MRI

Iterative Reconstruction Methods to Reduce Respiratory Motion Artifacts in Cartesian Coronary MRI - Forman, Christoph (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2017)
Cardiovascular diseases and coronary artery disease (CAD) in particular are the leading cause of death in most developed countries worldwide. Although CAD progresses slowly over several years, it often remains unnoticed and may lead to myocardial infarction in a sudden event. For this reason, there is a strong clinical need for the development of non-invasive and radiation-free screening methods allowing for an early diagnosis of these diseases. In this context, magnetic resonance imaging (MRI) represents a promising imaging modality. However, the slow acquisition process and the consequent susceptibility to artifacts due to cardiac and respiratory motion are major challenges for coronary MRI and have so far hindered its routine application in clinical examinations. Commonly, respiratory motion is addressed during free-breathing acquisitions by gating the scan to a consistent respiratory phase in end-expiration with a navigator monitoring the patient's diaphragm. Acceptance rates below 40% lead to a prolonged total acquisition time that is also not predictable in advance. In this work, a novel variable-density spiral phyllotaxis pattern is introduced for free-breathing whole-heart coronary MRI. It provides an incoherent sub-sampling of the Cartesian phase-encoding plane and allows for highly accelerated data acquisition when combined with compressed sensing reconstruction. With this sampling pattern, sub-sampling rates up to 10.2 enable a significant reduction of the total acquisition time. Furthermore, this sampling pattern is well-prepared for respiratory self-navigation, which performs respiratory motion compensation solely relying on the acquired imaging data and promises full scan efficiency. In this context, a novel motion detection approach is proposed that provides a robust tracking of respiration. However, 1-D motion compensation based on respiratory self-navigation was found to be not always sufficient for Cartesian imaging. Hence, an alternative method is presented to reduce the effects of respiration following the concept of weighted iterative reconstruction. In this technique, inconsistencies introduced by respiratory motion during data acquisition are addressed by weighting the least squares optimization according to a data consistency measure that is obtained from respiratory self-navigation. This approach forms the basis for an extension to a motion-compensated reconstruction with a dense, non-rigid motion model. With the proposed method, motion-compensated reconstruction was enabled for the first time in 3-D whole-heart imaging without the need for either additional calibration data or manual user interaction. The techniques presented in this thesis were fully integrated in a clinical MR scanner and tested in 14 volunteers. These results were compared to a navigator-gated reference acquisition. The acquisition time of the navigator-gated reference acquisition of 10.1 ± 2.3 min was reduced by one third to 6.3 ± 0.9 min with the proposed method utilizing respiratory self-navigation. After motion-compensated reconstruction, assessment of image quality revealed no significant differences com- pared to the images of the reference acquisition. Vessel sharpness was measured as 0.44 ± 0.05 mm−1 and 0.45 ± 0.05 mm−1 for RCA, and 0.39 ± 0.04 mm−1 and 0.40 ± 0.05 mm−1 for LAD, respectively.<br
Keywords: 3D-Rekonstruktion; MRT; Medizinische Bildgebung; Bewegungskompensation

Iterative reconstruction using a pyramid-shaped basis function - Schmitt, Katharina; Noo, Frédéric; Hornegger, Joachim; Stierstorfer, Karl; Schöndube, Harald (2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2012)

Java Visual Speech Components for Rapid Application Development of GUI based Speech Processing Applications - Steidl, Stefan; Riedhammer, Korbinian Thomas; Bocklet, Tobias; Hönig, Florian Thomas; Nöth, Elmar (Proceedings of the 12th Annual Conference of the International Speech Communication Association (INTERSPEECH 2011), 2011)

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

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

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

Joint Surface Reconstruction and 4D Deformation Estimation from Sparse Data and Prior Knowledge for Marker-Less Respiratory Motion Tracking - Berkels, Benjamin; Bauer, Sebastian; Ettl, Svenja; Arold, Oliver; Hornegger, Joachim; Rumpf, Martin (Medical Physics, 2013)

Joint ToF Image Denoising and Registration with a CT Surface in Radiation Therapy - Bauer, Sebastian; Berkels, Benjamin; Hornegger, Joachim; Rumpf, Martin (International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2011)
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD5.jointt

Joint ToF image denoising and registration with a CT surface in radiation therapy - Bauer, Sebastian; Berkels, Benjamin; Hornegger, Joachim; Rumpf, Martin (Springer-verlag, 2012)
The management of intra-fractional respiratory motion is becoming increasingly important in radiation therapy. Based on in advance acquired accurate 3D CT data and intra-fractionally recorded noisy time-of-flight (ToF) range data an improved treatment can be achieved. In this paper, a variational approach for the joint registration of the thorax surface extracted from a CT and a ToF image and the denoising of the ToF image is proposed. This enables a robust intra-fractional full torso surface acquisition and deformation tracking to cope with variations in patient pose and respiratory motion. Thereby, the aim is to improve radiotherapy for patients with thoracic, abdominal and pelvic tumors. The approach combines a Huber norm type regularization of the ToF data and a geometrically consistent treatment of the shape mismatch. The algorithm is tested and validated on synthetic and real ToF/CT data and then evaluated on real ToF data and 4D CT phantom experiments. © 2012 Springer-Verlag.

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

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

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

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

Korrelative Bildgebung in der Kardiologie; Theoretische Ansätze und klinische Anwendung - Kuwert, Torsten; Römer, Wolfgang; Hornegger, Joachim (Herz, 2007)

KSoF: The Kassel State of Fluency Dataset - A Therapy Centered Dataset of Stuttering - Bayerl, Sebastian P.; Von Gudenberg, Alexander Wolff; Hoenig, Florian; Nöth, Elmar; Riedhammer, Korbinian (LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, 2022)
CRIS-Team WoS Importer:2023-02-24

Landmark-constrained 3-D Histological Imaging: A Morphology-preserving Approach - Gaffling, Simone; Daum, Volker; Hornegger, Joachim (VMV 2011: Vision, Modeling & Visualization, 2011)

Language-Independent Age Estimation from Speech Using Phonological and Phonemic Features - Haderlein, Tino; Middag, Catherine; Hönig, Florian Thomas; Martens, Jean-Pierre; Döllinger, Michael; Schützenberger, Anne; Nöth, Elmar (Springer-verlag, 2015)
Language-independent and alignment-free phonological and phonemic features were applied for automatic age estimation based on voice and speech properties. 110 persons (average: 75.7 years) read the German version of the text "The North Wind and the Sun". For comparison with the automatic approach, five listeners estimated the speakers' age perceptually. Support Vector Regression and feature selection were used to compute the best model of aging. This model was found to use the following features: (a) the percentage of voiced frames, (b) eight phonological features, representing vowel height, nasality in consonants, turbulence, and position of the lips, and finally, (c) seven phonemic features. The latter features might be relevant due to altered articulation because of dentures. The mean absolute error between computed and chronological age was 5.2 years (RMSE: 7.0). It was 7.7 years (RMSE: 9.6) for an optimistic trivial estimator and 10.5 years (RMSE: 11.9) for the average listener.
Keywords: Age estimation; Phonological features; Phonemic features; SVR

Language-Independent Automatic Evaluation of Intelligibility of Chronically Hoarse Persons - Haderlein, Tino; Middag, Catherine; Martens, Jean-Pierre; Döllinger, Michael; Nöth, Elmar (Folia Phoniatrica Et Logopaedica, 2014)
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.langua

Language-independent method for analysis of German stuttering recordings - Lustyk, Tomas; Bergl, Petr; Haderlein, Tino; Nöth, Elmar; Cmejla, Roman (16th Annual Conference of the International Speech Communication Association, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.langua_6

Laparoscopic instrument localization using a 3-D Time-of-Flight/RGB endoscope - Haase, Sven; Wasza, Jakob; Kilgus, Thomas; Hornegger, Joachim (2013 IEEE Workshop on Applications of Computer Vision, WACV 2013, 2013)
Minimally invasive procedures are of importance in modern surgery due to reduced operative trauma and recovery time. To enable robot assisted interventions, automatic tracking of endoscopie tools is an essential task. State-of-the-art techniques rely on 2-D color information only which is error prone for varying illumination and unpredictable color distribution within the human body. In this paper, we use a novel 3-D Time-of-Flight/RGB endoscope that allows to use both color and range information to locate laparoscopic instruments in 3-D. Regarding color and range information the proposed technique calculates a score to indicate which information is more reliable and adopts the next steps of the localization procedure based on this reliability. In experiments on real data the tool tip is located with an average 3-D distance error of less than 4 mm compared to manually labeled ground truth data with a frame-rate of 10 fps. © 2013 IEEE.

Large-Scale Graph Networks and AI Applied to Medical Image Data Processing - Meyer-Baese, Anke; Foo, Simon; Tahmassebi, Amirhessam; Meyer-Baese, Uwe; Amani, Ali Moradi; Götz, Theresa; Leithner, Doris; Stadlbauer, Andreas; Pinker-Domenig, Katja (COMPUTATIONAL IMAGING V, 2020)
CRIS-Team WoS Importer:2021-07-30

Laryngealizations and Emotions: How Many Babushkas? - Batliner, Anton; Steidl, Stefan; Nöth, Elmar (Proceedings of the International Workshop on Paralinguistic Speech - between Models and Data, 2007)

Late Fusion of Individual Engines for Improved Recognition of Negative Emotion in Speech - Learning vs. Democratic Vote - Schuller, Björn; Metze, Florian; Steidl, Stefan; Batliner, Anton; Eyben, Florian; Polzehl, Tim (Proceedings of ICASSP, 2010)
The fusion of multiple recognition engines is known to be able to outperform individual ones, given sufficient independence of methods, models, and knowledge sources. We therefore investigate late fusion of different speech-based recognizers of emotion. Two generally different streams of information are considered: acoustics and linguistics fed by state-of-the-art automatic speech recognition. A total of five emotion recognition engines from different sites that provide heterogeneous output information are integrated by either simple democratic vote or learning ‘which predictor to trust when’. We are able to significantly outperform the best individual engine by fusion, and the so far best reported result on the recently introduced Emotion Challenge task.
Keywords: emotion recognition; late fusion; speech analysis

Lattice Boltzmann methods in porous media simulations: From laminar to turbulent flow - Fattahi, Ehsan; Waluga, Christian; Wohlmuth, B. I.; Rüde, Ulrich; Manhart, Michael; Helmig, Rainer (Computers & Fluids, 2016)

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

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

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

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

Learning based non-rigid multi-modal image registration using Kullback-Leibler divergence - Gütter, Christoph; Xu, Chenyang; Sauer, Frank; Hornegger, Joachim (Springer-verlag, 2005)
The need for non-rigid multi-modal registration is becoming increasingly common for many clinical applications. To date, however, existing proposed techniques remain as largely academic research effort with very few methods being validated for clinical product use. It has been suggested by Crum et al. [1] that the context-free nature of these methods is one of the main limitations and that moving towards context-specific methods by incorporating prior knowledge of the underlying registration problem is necessary to achieve registration results that are accurate and robust enough for clinical applications. In this paper, we propose a novel non-rigid multi-modal registration method using a variational formulation that incorporates a prior learned joint intensity distribution. The registration is achieved by simultaneously minimizing the Kullback-Leibler divergence between an observed and a learned joint intensity distribution and maximizing the mutual information between reference and alignment images. We have applied our proposed method on both synthetic and real images with encouraging results. © Springer-Verlag Berlin Heidelberg 2005.
UnivIS-Import:2015-04-16:Pub.2005.tech.IMMD.IMMD5.learni

Learning-based occupational x-ray scatter estimation - Maul, Noah; Roser, Philipp; Birkhold, Annette; Kowarschik, Markus; Zhong, Xia; Strobel, Norbert; Maier, Andreas (Physics in Medicine and Biology, 2022)
Objective. During x-ray-guided interventional procedures, the medical staff is exposed to scattered ionizing radiation caused by the patient. To increase the staff's awareness of the invisible radiation and monitor dose online, computational scatter estimation methods are convenient. However, such methods are usually based on Monte Carlo (MC) simulations, which are inherently computationally expensive. Yet, in the interventional environment, immediate feedback to the personnel is desirable. Approach. In this work, we propose deep neural networks to mitigate the computational effort of MC simulations. Our learning-based models consider detailed models of the (outer) patient shape and (inner) anatomy, additional objects in the room, and the x-ray tube spectrum to cover imaging settings encountered in real interventional settings. We investigate two cases of scatter prediction. First, we employ network architectures to estimate the full three-dimensional (3D) scatter distribution. Second, we investigate the prediction of two-dimensional (2D) intensity projections that facilitate the intra-procedural visualization. Main results. Depending on the dimensionality of the estimated scatter distribution and the network architecture, the mean relative error of each network is in the range of 12% and 14% compared to MC simulations. However, 3D scatter distributions can be estimated within 60 ms and 2D distributions within 15 ms. Significance. Overall, our method is suitable to support the online assessment of scattered ionizing radiation in the interventional environment and can help to lower the occupational radiation risk.
CRIS-Team WoS Importer:2022-04-01

Learning discriminative distance functions for valve retrieval and improved decision support in valvular heart disease - Voigt, Ingmar; Vitanovski, Dime; Ionasec, Razvan Ioan; Tsymbal, Alexey; Georgescu, Bogdan; Zhou, S. Kevin; Huber, Martin; Navab, Nassir; Hornegger, Joachim; Comaniciu, Dorin (Medical Imaging 2010: Image Processing, 2010)
Disorders of the heart valves constitute a considerable health problem and often require surgical intervention. Recently various approaches were published seeking to overcome the shortcomings of current clinical practice,that still relies on manually performed measurements for performance assessment. Clinical decisions are still based on generic information from clinical guidelines and publications and personal experience of clinicians. We present a framework for retrieval and decision support using learning based discriminative distance functions and visualization of patient similarity with relative neighborhood graphsbased on shape and derived features. We considered two learning based techniques, namely learning from equivalence constraints and the intrinsic Random Forest distance. The generic approach enables for learning arbitrary user-defined concepts of similarity depending on the application. This is demonstrated with the proposed applications, including automated diagnosis and interventional suitability classification, where classification rates of up to 88.9% and 85.9% could be observed on a set of valve models from 288 and 102 patients respectively. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Learning distance function for regression-based 4D pulmonary trunk model reconstruction estimated from sparse MRI data - Vitanovski, Dime; Tsymbal, Alexey; Ionasec, Razvan; Georgescu, Bogdan; Zhou, Shaohua K.; Hornegger, Joachim; Comaniciu, Dorin (Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 2011)
Congenital heart defect (CHD) is the most common birth defect and a frequent cause of death for children. Tetralogy of Fallot (ToF) is the most often occurring CHD which affects in particular the pulmonary valve and trunk. Emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. While minimal invasive methods become common practice, imaging and non-invasive assessment tools become crucial components in the clinical setting. Cardiac computed tomography (CT) and cardiac magnetic resonance imaging (cMRI) are techniques with complementary properties and ability to acquire multiple non-invasive and accurate scans required for advance evaluation and therapy planning. In contrary to CT which covers the full 4D information over the cardiac cycle, cMRI often acquires partial information, for example only one 3D scan of the whole heart in the end-diastolic phase and two 2D planes (long and short axes) over the whole cardiac cycle. The data acquired in this way is called sparse cMRI. In this paper, we propose a regression-based approach for the reconstruction of the full 4D pulmonary trunk model from sparse MRI. The reconstruction approach is based on learning a distance function between the sparse MRI which needs to be completed and the 4D CT data with the full information used as the training set. The distance is based on the intrinsic Random Forest similarity which is learnt for the corresponding regression problem of predicting coordinates of unseen mesh points. Extensive experiments performed on 80 cardiac CT and MR sequences demonstrated the average speed of 10 seconds and accuracy of 0.1053mm mean absolute error for the proposed approach. Using the case retrieval workflow and local nearest neighbour regression with the learnt distance function appears to be competitive with respect to "black box" regression with immediate prediction of coordinates, while providing transparency to the predictions made. © 2011 SPIE.

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

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

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

Learning of Domain Dependent Knowledge in Semantic Networks - Deinzer, Frank; Fischer, Julia; Ahlrichs, Ulrike; Nöth, Elmar (Proc. European Conf. on Speech Communication and Technology, 1999)

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

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

Learning, Tracking and Recognition of 3D Objects - Denzler, Joachim; Beß, Rüdiger; Hornegger, Joachim; Niemann, Heinrich; Paulus, Dietrich (International Conference on Intelligent Robots and Systems, 1994)

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

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

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

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

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

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

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

Linear approximation of sensitivity curve calibration - Paulus, Dietrich; Hornegger, Joachim; Csink, Laszlo (8. Workshop Farbbildverarbeitung, 2002)

LMELectures: a Multimedia Corpus of Acedemic Spoken English - Riedhammer, Korbinian Thomas; Gropp, Martin; Bocklet, Tobias; Hönig, Florian Thomas; Nöth, Elmar; Steidl, Stefan (Proceedings of the First Workshop on Speech, Language and Audio in Multimedia, 2013)
This paper describes the acquisition, transcription and annotation of a multi-media corpus of academic spoken English, the LMELectures. It consists of two lecture series that were read in the summer term 2009 at the computer science department of the University of Erlangen- Nuremberg, covering topics in pattern analysis, machine learning and interventional medical image processing. In total, about 40 hours of high-definition audio and video of a single speaker was acquired in a constant recording environment. In addition to the recordings, the presentation slides are available in machine readable (PDF) format. The manual annotations include a suggested segmentation into speech turns and a complete manual transcription that was done using BLITZSCRIBE2, a new tool for the rapid transcription. For one lecture series, the lecturer assigned key words to each recordings; one recording of that series was further annotated with a list of ranked key phrases by five human annotators each. The corpus is available for non-commercial purpose upon request.
Keywords: Academic spoken English; Corpus description; E-learning

Localization and classification based on projections - Hornegger, Joachim; Welker, Volkmar; Niemann, Heinrich (Pattern Recognition, 2002)
Due to the loss of range information, projections as input data for a 3-D object recognition algorithm are expected to increase the computational complexity. In this work, however, we demonstrate that this deficiency carries potential for complexity reduction of major vision problems. We show that projections provide a reduction of feature dimensions, and lead to structures exhibiting simple combinatorial properties. The theoretical framework is embedded in a probabilistic setting which deals with uncertainties and variations of observed features. In statistics marginal densities and the assumption of independency prove to be the key tools when one encounters projections. The examples discussed in this paper include feature matching, pose estimation as well as classification of 3-D objects. The final experimental evaluation demonstrates the practical importance of the marginalization concept and independency assumptions. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Local orientation-dependent noise propagation for anisotropic denoising of CT-Images - Borsdorf, Anja; Kappler, Steffen; Raupach, Rainer; Noo, Frederic; Hornegger, Joachim (2009 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2009, 2009)
In X-ray Computed Tomography (CT) the measured projections and consequently the reconstructed CT images are subject to quantum and electronics noise. While noise in the projections can be well described and estimated with a corresponding physics model, the distribution of noise in the reconstructed CT images is not directly evident. Due to attenuation variations along different directions, the nature of noise in CT images is non-stationary and non-isotropic. This complicates the direct application of standard post-processing methods like bilateral filtering. In this article we describe a possibility to compute precise orientation dependent noise estimates for every pixel position. This is done by analytic propagation of projection noise estimates through indirect fan-beam filtered backprojection reconstruction. The resulting orientation dependent image noise estimates are subsequently used in adaptive bilateral filters. Taking into account the non-stationary and non-isotropic nature of noise in CT images, a reduction in image noise of about 55% compared to 39% of the standard approach is achieved with much less variability over different image regions. ©2009 IEEE.

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

Longitudinal analysis of bone metabolism using SPECT/CT and (99m)Tc-diphosphono-propanedicarboxylic acid: comparison of visual and quantitative analysis - Beck, Michael; Sanders, James Chester; Ritt, Philipp; Reinfelder, Julia; Kuwert, Torsten (EJNMMI Research, 2016)
The therapy response of osseous metastases (OM) is commonly monitored by bone scintigraphies (BS). The aim of this study was to compare visual evaluation of changes in tracer uptake with quantitation in absolute units in OMs; 52 OMs from 19 patients who underwent BS with SPECT/CT at time points one and two (TP1/2) were analyzed retrospectively, with an average of 10.3 months between TP1 and 2. Tracer uptake in lesions was visually compared by two independent readers in both planar scintigraphies and SPECT/CT across both TPs and classified as regressive, stable, or progressive. Quantitative analysis was performed by measuring peak standardized uptake values (SUV). Based on quantitation, lesions were similarly classified as regressive (>30 % decrease), progressive (>30 % increase), or stable (rest). If available, uptake in reference regions in the lower thoracic or lumbar spine was used for normalization.In OMs at TP1 and TP2, mean SUVpeak (±SD) was found to be 20.4 (±20.8) and 16.4 (±11.5), respectively. For the reference region, mean SUVmean was 5.6 (±1.9) and 4.9 (±2.2). Agreement between quantitative and visual assessment was only moderate, with an average Cohen's kappa of 0.42 for planar scintigraphy and 0.62 for SPECT/CT. Discrepancies occurred in between 11 and 22 of the 52 lesions, depending on the reader and whether planar or SPECT imaging was considered.Compared to measuring uptake in absolute units, visual evaluation of skeletal scintigraphies for change in tumor metabolism yields inconsistent results in roughly one third of the cases.
EVALuna2:13860

Looking at the Last two Turns, I'd Say this Dialogue is Doomed - Measuring Dialogue Success - Steidl, Stefan; Hacker, Christian; Ruff, Christine; Batliner, Anton; Nöth, Elmar; Haas, Jürgen (Text, Speech and Dialogue, 7th International Conference, TSD 2004, Czech Republic, September 8-11, 2004, Proceedings, 2004)
Tw Two sets of linguistic features are developed: The first one to estimate if a single step in a dialogue between a human being and a machine is successful or not. The second set to classify dialogues as a whole. The features are based on Part-of-Speech-Labels (POS), word statistics and properties of turns and dialogues. Experiments were carried out on the SympaFly corpus, data from a real application in the flight booking domain. A single dialogue step could be classified with an accuracy of 83 % (class-wise averaged recognition rate). The recognition rate for whole dialogues was 85%.

"Looks do matter"-visual attentional biases in adolescent girls with eating disorders viewing body images - Horndasch, Stefanie; Kratz, Oliver; Holczinger, Anna; Heinrich, Hartmut; Hönig, Florian Thomas; Nöth, Elmar; Moll, Gunther (Psychiatry Research, 2012)
Visual attention allocation of adolescent girls with and without an eating disorder while viewing body images of underweight, normal-weight and overweight women was studied using eye tracking. While all girls attended more to specific body parts (e.g. hips, upper legs), eating-disordered girls showed an attentional bias towards unclothed body parts.
Keywords: Eating disorder; Attentional bias; Eye tracking

Look-up table-based simulation of scintillation detectors in computed tomography - Balda, Michael; Wirth, Stefan; Niederlöhner, Daniel; Heismann, Björn; Hornegger, Joachim (2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008, 2008)
The design of a CT detector requires a precise detector model, since building prototypes for many different proposed detector geometries is too costly. We introduce a lookup table-based simulation of scintillation detectors on X-ray photon level. It uses energy-resolved sinograms of incoming X-ray intensities as input data and generates photon counts for each channel and reading. The effects of X-ray- and optical cross-talk, temporal cross-talk between readings, Poisson noise and electronics effects are covered. The photon interaction data as well as optical cross-talk distribution are provided in the form of detector specific look-up tables. Unlike standard MonteCarlo simulations of X-ray interaction processes, our approach is capable of simulating whole sinograms in a reasonable amount of time and still offers a very high precision of the detector model. This way the influence of detector effects can be investigated in the reconstructed image data. The simulation is verified against data measured with a CT scanner and data from a fully single photon-based Monte-Carlo simulation in terms of image modulation transfer function (MTF) and detector noise power spectrum (NPS). © 2008 IEEE.

Low-frequency components analysis in running speech for the automatic detection of Parkinson's disease - Villa-Cañas, T.; Arias-Londoño, J. D.; Orozco-Arroyave, J. R.; Vargas-Bonilla, J. F.; Nöth, Elmar (16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015, 2015)
This paper explores the analysis of low-frequency components of continuous speech signals from people with Parkinson's disease, in order to detect changes in the spectrum that could be associated to the presence of tremor in the speech. Different time-frequency (TF) techniques are used for the characterization of the low frequency content of the speech signals, by paying special attention on the ability to work in non-stationary frameworks, due to the need for the analysis of long enough time segments, where the assumptions of stationary can not be met. The set of variables extracted from the TF representations includes centroids and the energy content of different frequency bands, along with entropy measures and nonlinear energy operators, which are used as features for the automatic detection of people with Parkinson's disease vs healthy controls. The discrimination capability of the estimated features is evaluated using three different classification strategies: GMM, GMM-UBM, and SVM. Furthermore, the information provided by different TF techniques is combined using a second classification stage. The results show that the changes in the low frequency components are able to discriminate between people with Parkinson's and healthy speakers with an accuracy of 77%, using one single sentence.
Keywords: Continuous speech characterization; Parkinson's disease; Pathological voice detection; Time-frequency analysis

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

Lymph node detection and segmentation in chest CT data using discriminative learning and a spatial prior - Feulner, Johannes; Zhou, S. Kevin; Hammon, Matthias; Hornegger, Joachim; Comaniciu, Dorin (Medical Image Analysis, 2012)
Lymph nodes have high clinical relevance and routinely need to be considered in clinical practice. Automatic detection is, however, challenging due to clutter and low contrast. In this paper, a method is presented that fully automatically detects and segments lymph nodes in 3-D computed tomography images of the chest. Lymph nodes can easily be confused with other structures, it is therefore vital to incorporate as much anatomical prior knowledge as possible in order to achieve a good detection performance. Here, a learned prior of the spatial distribution is used to model this knowledge. Different prior types with increasing complexity are proposed and compared to each other. This is combined with a powerful discriminative model that detects lymph nodes from their appearance. It first generates a number of candidates of possible lymph node center positions. Then, a segmentation method is initialized with a detected candidate. The graph cuts method is adapted to the problem of lymph nodes segmentation. We propose a setting that requires only a single positive seed and at the same time solves the small cut problem of graph cuts. Furthermore, we propose a feature set that is extracted from the segmentation. A classifier is trained on this feature set and used to reject false alarms. Cross-validation on 54 CT datasets showed that for a fixed number of four false alarms per volume image, the detection rate is well more than doubled when using the spatial prior. In total, our proposed method detects mediastinal lymph nodes with a true positive rate of 52.0% at the cost of only 3.1 false alarms per volume image and a true positive rate of 60.9% with 6.1 false alarms per volume image, which compares favorably to prior work on mediastinal lymph node detection. © 2012 Elsevier B.V. All rights reserved.

Lymph node detection in 3-D chest CT using a spatial prior probability - Feulner, Johannes; Zhou, S. Kevin; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin; Cavallaro, Alexander Josef (2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010, 2010)
Lymph nodes have high clinical relevance but detection is challenging as they are hard to see due to low contrast and irregular shape. In this paper, a method for fully automatic mediastinal lymph node detection in 3-D computed tomography (CT) images of the chest area is proposed. Discriminative learning is used to detect lymph nodes based on their appearance. Because lymph nodes can easily be confused with other structures, it is vital to incorporate as much anatomical knowledge as possible to achieve good detection rates. Here, a learned prior of the spatial distribution is proposed to model this knowledge. As atlas matching is generally inaccurate in the chest area because of anatomical variations, this prior is not learned in the space of a single atlas, but in the space of multiple ones that are attached to anatomical structures. During test, the priors are weighted and merged according to spatial distances. Cross-validation on 54 CT datasets showed that the prior based detector yields a true positive rate of 52.3% for seven false positives per volume image, which is about two times better than without a spatial prior. ©2010 IEEE.

Machine learning based analysis of speech dimensions in functional oropharyngeal dysphagia - Roldan-Vasco, Sebastian; Orozco-Duque, Andres; Suarez-Escudero, Juan Camilo; Orozco Arroyave, Juan Rafael (Computer Methods and Programs in Biomedicine, 2021)
Background and objective: The normal swallowing process requires a complex coordination of anatomical structures driven by sensory and cranial nerves. Alterations in such coordination cause swallowing malfunctions, namely dysphagia. The dysphagia screening methods are quite subjective and experience dependent. Bearing in mind that the swallowing process and speech production share some anatomical structures and mechanisms of neurological control, this work aims to evaluate the suitability of automatic speech processing and machine learning techniques for screening of functional dysphagia. Methods: Speech recordings were collected from 46 patients with functional oropharyngeal dysphagia produced by neurological causes, and 46 healthy controls. The dimensions of speech including phonation, articulation, and prosody were considered through different speech tasks. Specific features per dimension were extracted and analyzed using statistical tests. Machine learning models were applied per dimension via nested cross-validation. Hyperparameters were selected using the AUC - ROC as optimization criterion. Results: The Random Forest in the articulation related speech tasks retrieved the highest performance measures (AUC=0.86±0.10, sensitivity=0.91±0.12) for individual analysis of dimensions. In addition, the combination of speech dimensions with a voting ensemble improved the results, which suggests a contribution of information from different feature sets extracted from speech signals in dysphagia conditions. Conclusions: The proposed approach based on speech related models is suitable for the automatic discrimination between dysphagic and healthy individuals. These findings seem to have potential use in the screening of functional oropharyngeal dysphagia in a non-invasive and inexpensive way.
CRIS-Team Scopus Importer:2021-08-13
Keywords: Dysphagia; Feature extraction; Machine learning; Speech analysis; Speech processing; Voice changes

Machine Learning-based Detection of Spherical Markers in CT Volumes - Rao, Disha; Maass, Nicole; Dennerlein, Frank; Maier, Andreas; Huang, Yixing (Informatik aktuell, 2022)
CRIS-Team Scopus Importer:2022-05-06

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

Magnetic Resonance Imaging for Percutaneous Interventions - Rothgang, Eva (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)
The fundamental motivation for all percutaneous interventions is to improve patient care by reducing the invasiveness of the procedure. An increasing number of percu- taneous interventions from biopsies, targeted drug delivery to thermal ablations are performed under magnetic resonance (MR) guidance. Its excellent soft-tissue con- trast and multiplanar imaging capabilities make MRI an attractive alternative to computed tomography or ultrasound for real-time image-guided needle placement, in particular for targets requiring a highly angulated approach and non-axial scan pla- nes. MRI further provides the unique ability to monitor spatial temperature changes in real-time. The research efforts of this dissertation were focused on improving and simplifying the workflow of MR-guided percutaneous procedures by introducing novel image- based methods without the need for any additional equipment. For safe and efficient MR-guided percutaneous needle placement, a set of methods was developed that allows the user to: 1) plan an entire procedure, 2) directly apply this plan to skin entry site localization without further imaging, and 3) place a needle under real-time MR guidance with automatic image plane alignment along a planned trajectory with preference to the principal patient axes. Methods for enhanced MR thermometry visualization and treatment monitoring were also developed to support an effective thermal treatment facilitating the ablation of tumor tissue without damaging adjacent healthy structures. To allow for an extensive in-vitro and in-vivo validation, the proposed methods for both needle guidance and MR thermometry were implemented in an integrated prototype. The clinical applicability was demonstrated for a wide range of MR-guided percutaneous interventions emphasizing the relevance and impact of the conducted research.
UnivIS-Import:2016-07-26:Pub.2014.tech.IMMD.IMMD5.magnet

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

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

Manganese-Enhanced Magnetic Resonance Imaging for Mapping of Whole Brain Activity Patterns Associated with the Intake of Snack Food in Ad Libitum Fed Rats - Hoch, Tobias; Kreitz, Silke; Gaffling, Simone; Pischetsrieder, Monika; Heß, Andreas (PLoS ONE, 2013)
Non-homeostatic hyperphagia, which is a major contributor to obesity-related hyperalimentation, is associated with the diet's molecular composition influencing, for example, the energy content. Thus, specific food items such as snack food may induce food intake independent from the state of satiety. To elucidate mechanisms how snack food may induce non-homeostatic food intake, it was tested if manganese-enhanced magnetic resonance imaging (MEMRI) was suitable for mapping the whole brain activity related to standard and snack food intake under normal behavioral situation. Application of the MnCl2 solution by osmotic pumps ensured that food intake was not significantly affected by the treatment. After z-score normalization and a non-affine three-dimensional registration to a rat brain atlas, significantly different grey values of 80 predefined brain structures were recorded in ad libitum fed rats after the intake of potato chips compared to standard chow at the group level. Ten of these areas had previously been connected to food intake, in particular to hyperphagia (e.g. dorsomedial hypothalamus or the anterior paraventricular thalamic nucleus) or to the satiety system (e.g. arcuate hypothalamic nucleus or solitary tract); 27 areas were related to reward/addiction including the core and shell of the nucleus accumbens, the ventral pallidum and the ventral striatum (caudate and putamen). Eleven areas associated to sleep displayed significantly reduced Mn2+-accumulation and six areas related to locomotor activity showed significantly increased Mn2+-accumulation after the intake of potato chips. The latter changes were associated with an observed significantly higher locomotor activity. Osmotic pump-assisted MEMRI proved to be a promising technique for functional mapping of whole brain activity patterns associated to nutritional intake under normal behavior.

Man-machine interaction for the interventional application - Prümmer, Marcus; Nöth, Elmar; Hornegger, Joachim; Horndasch, Axel (Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2005 - Workshop on Image Processing for Medicine: Algorithms - Systems - Applications, BVM 2005, 2005)

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

Marginal Space Deep Learning: Efficient Architecture for Detection in Volumetric Image Data - Ghesu, Florin C.; Georgescu, Bogdan; Zheng, Yefeng; Hornegger, Joachim; Comaniciu, Dorin (18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I, 2015)
Current state-of-the-art techniques for fast and robust parsing of volumetric medical image data exploit large annotated image databases and are typically based on machine learning methods. Two main challenges to be solved are the low efficiency in scanning large volumetric input images and the need for manual engineering of image features. This work proposes Marginal Space Deep Learning (MSDL) as an effective solution, that combines the strengths of efficient object parametrization in hierarchical marginal spaces with the automated feature design of Deep Learning (DL) network architectures. Representation learning through DL automatically identifies, disentangles and learns explanatory factors directly from low-level image data. However, the direct application of DL to volumetric data results in a very high complexity, due to the increased number of transformation parameters. For example, the number of parameters defining a similarity transformation increases to 9 in 3D (3 for location, 3 for orientation and 3 for scale). The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in high probability regions in spaces of gradually increasing dimensionality, for example starting from location only (3D) to location and orientation (6D) and full parameter space (9D). In addition, for parametrized feature computation, we propose to simplify the network by replacing the standard, pre-determined feature sampling pattern with a sparse, adaptive, self-learned pattern. The MSDL framework is evaluated on detecting the aortic heart valve in 3D ultrasound data. The dataset contains 3795 volumes from 150 patients. Our method outperforms the state-of-the-art with an improvement of 36%, running in less than one second. To our knowledge this is the first successful demonstration of the DL potential to detection in full 3D data with parametrized representations.

Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing - Ghesu, Florin C.; Krubasik, Edward; Georgescu, Bogdan; Singh, Vivek; Zheng, Yefeng; Hornegger, Joachim; Comaniciu, Dorin (IEEE Transactions on Medical Imaging, 2016)
Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45.2% over the state-of-the-art. To our knowledge, this is the first successful demonstration of the DL potential to detection and segmentation in full 3D data with parametrized representations.
Keywords: Deep learning; image parsing; marginal space learning; sparse representations; three-dimensional (3D) object detection and segmentation

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

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

Markerless estimation of patient orientation, posture and pose using range and pressure imaging: For automatic patient setup and scanner initialization in tomographic imaging - Grimm, Robert; Bauer, Sebastian; Sukkau, Johann; Hornegger, Joachim; Greiner, Günther (International Journal of Computer Assisted Radiology and Surgery, 2012)
Purpose In diagnostic tomographic imaging, patient setup and scanner initialization is a manual, tedious procedure in clinical practice. A fully-automatic detection of the patient's position, orientation, posture and pose on the patient table holds great potential for optimizing this part of the imaging workflow. We propose a markerless framework that is capable of extracting this information within seconds from either range imaging (RI) or pressure imaging (PI) data. Methods The proposed method is composed of three stages: First, the position and orientation of the reclined patient are determined. Second, the patient's posture is classified. Third, based on the estimated orientation and posture, an approximate body pose is recovered by fitting an articulated model to the observed RI/PI data. Being a key issue for clinical application, our approach does not require an initialization pose. Results In a case study on real data from 16 subjects, the performance of the proposed system was evaluated quantitatively with a 3-D time-of-flight RI camera and a pressure sensing mattress (PI). The patient orientation was successfully determined for all subjects, independent of the modality. At the posture recognition stage, our method achieved mean classification rates of 79.4% for RI and 95.5% for PI data, respectively. Concerning the approximate body pose estimation, anatomical body landmarks were localized with an accuracy of ±5.84 cm (RI) and ±5.53 cm (PI). Conclusions The results indicate that an estimation of the patient's position, orientation, posture and pose using RI and PI sensors, respectively, is feasible, and beneficial for optimizing the workflow in diagnostic tomographic imaging. Both modalities achieved comparable pose estimation results using different models that account for modality-specific characteristics. PI outperforms RI in discriminating between prone and supine postures due to the distinctive pressure distribution of the human body. © CARS 2012.

Marker-less reconstruction of dense 4-D surface motion fields using active laser triangulation for respiratory motion management - Bauer, Sebastian; Berkels, Benjamin; Ettl, Svenja; Arold, Oliver; Hornegger, Joachim; Rumpf, Martin (15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part I, 2012)
To manage respiratory motion in image-guided interventions a novel sparse-to-dense registration approach is presented. We apply an emerging laser-based active triangulation (AT) sensor that delivers sparse but highly accurate 3-D measurements in real-time. These sparse position measurements are registered with a dense reference surface extracted from planning data. Thereby a dense displacement field is reconstructed which describes the 4-D deformation of the complete patient body surface and recovers a multi-dimensional respiratory signal for application in respiratory motion management. The method is validated on real data from an AT prototype and synthetic data sampled from dense surface scans acquired with a structured light scanner. In a study on 16 subjects, the proposed algorithm achieved a mean reconstruction accuracy of +/- 0.22 mm w.r.t. ground truth data.
UnivIS-Import:2015-04-16:Pub.2012.tech.IMMD.IMMD5.marker

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

Marktübersicht Simulatoren für Soft-Computing-Systeme auf PCs - Wilke, Peter; Sonnefeld, Ralf; Metin, M. (1996)

Marktübersicht Simulatoren für Soft-Computing-Systeme auf PCs, Workstations und verteilten Rechnersystemen - Wilke, Peter; Sonnefeld, Ralf (1995)

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

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

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

Maximum likelihood estimation of a reverberation model for robust distant-talking speech recognition - Sehr, Armin; Zheng, Yuanhang; Nöth, Elmar; Kellermann, Walter (Proc. European Signal Processing Conference, 2007)
We propose a novel approach for estimating a reverberation model for a robust recognizer according to [1], which is designed to allow distant-talking automatic speech recognition (ASR) in reverberant environments. Based on a few calibration utterances with known transcriptions recorded in the target environment, a maximum likelihood estimator is used to find the means and variances of the reverberation model. In contrast to [1] and to HMM training on artificially reverberated training data (e. g. [2]), measurements of room impulse responses become unnecessary, and the effort for training is greatly reduced. Simulations of a connected digit recognition task show that, in highly reverberant environments, the reverberation models estimated by the proposed approach achieve significantly higher recognition rates than HMMs trained on reverberant data. © 2007 EURASI

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

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