Measurement of individual changes in the performance of human stereoscopic vision for disparities at the limits of the zone of comfortable viewing - Paulus, Jan; Michelson, Georg; Hornegger, Joachim; Eskofier, Björn; Schmidt, Michael; et al. (2013 International Conference on 3D Vision - 3DV 2013, 2013)
3D displays enable immersive visual impressions but the impact on the human perception still is not fully understood. Viewing conditions like the convergence-accommodation (C-A) conflict have an unnatural influence on the visual system and might even lead to visual discomfort. As visual perception is individual we assumed the impact of simulated 3D content on the visual system to be as well. In this study we aimed to analyze the stereoscopic visual performance of 17 subjects for disparities inside and outside the in literature defined zone of comfortable viewing to provide an individual evaluation of the impact of increased disparities on the performance of the visual system. Stereoscopic stimuli were presented in a four-alternative forced choice (4AFC) setup in different disparities. The response times as well as the correct decision rates indicated the performance of stereoscopic vision. The results showed that increased disparities lead to a decline in performance. Further, the impact of the presented disparities is dependent on the difficulty of the task. The decline of performance as well as the deciding disparities for the decline were subject dependent. © 2013 IEEE.
UnivIS-Import:2017-01-09:Pub.2013.tech.IMMD.IMMD5.measur_1
Keywords: 3D displays; 3D perception; stereo vision; stereopsis; visual discomfort

Measurement of Individual Changes in the Performance of Human Stereoscopic Vision for Disparities at the Limits of the Zone of Comfortable Viewing - Schmidt, Michael; Hornegger, Joachim; Eskofier, Björn; Barkowsky, Marcus; Paulus, Jan; et al. (Proceedings of the IEEE, 2013)

Measurement of kidney perfusion by magnetic resonance imaging: Comparison of MRI with arterial spin labeling to para-aminohippuric acid plasma clearance in male subjects with metabolic syndrome - Ritt, Martin; Janka, Rolf Matthias; Schneider, Markus P.; Martirosian, Petros; Hornegger, Joachim; Bautz, Werner; Uder, Michael; Schmieder, Roland (Nephrology Dialysis Transplantation, 2010)
Background. Magnetic resonance imaging with arterial spin labeling (MRI-ASL) is a non-invasive approach to measure organ perfusion. We aimed to examine whether MRI-ASL kidney perfusion measurements are related to measurements of renal plasma flow (RPF) by para-aminohippuric acid (PAH) plasma clearance and whether changes of kidney perfusion in response to treatment with telmisartan can be detected by MRI-ASL.Methods. Twenty-four patients with metabolic syndrome and an estimated creatinine clearance according to Cockroft and Gault of ≥60ml/min were included in the study. Kidney perfusion was assessed by MRI-ASL measurements of a single coronal kidney slice (with flow-sensitive alternating inversion recovery and true fast imaging with steady-state processing sequence) and by measurements of RPF using PAH plasma clearance before and after 2 weeks of treatment with the angiotensin receptor blocker telmisartan. All MRI-ASL examinations were performed on a 1.5 T scanner.Results. Two weeks of therapy with telmisartan led to a significant increase of RPF (from 313 ± 47 to 348 ± 69ml/min/m, P = 0.007) and MRI-ASL kidney perfusion measurements (from 253 ± 20 to 268 ± 25ml/min/100g, P = 0.020). RPF measurements were related with MRI-ASL kidney perfusion measurements (r = 0.575, P < 0.001). Changes of RPF measurements and changes of MRI-ASL kidney perfusion measurements in response to treatment with telmisartan revealed a close relationship when expressed in absolute terms (r = 0.548, P = 0.015) and in percentage changes (r = 0.514, P = 0.025).Conclusions. Perfusion measurement of a single coronal kidney slice by MRI-ASL is able to approximate kidney perfusion and to approximate changes in kidney perfusion due to pharmacological intervention. © 2009 The Author.

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

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

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015: 18th International Conference Munich, Germany, October 5-9, 2015 proceedings, part III - Navab, Nassir; Hornegger, Joachim; Wells, William M.; Frangi, Alejandro F. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015)
CRIS-Team Scopus Importer:2023-03-08

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

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

Medium-term speaker states - A review on intoxication, sleepiness and the first challenge - Schuller, Björn; Steidl, Stefan; Batliner, Anton; Schiel, Florian; Krajewski, Jarek; Weninger, Felix; Eyben, Florian (Computer Speech and Language, 2014)
In the emerging field of computational paralinguistics, most research efforts are devoted to either short-term speaker states such as emotions, or long-term traits such as personality, gender, or age. To bridge this gap on the time axis, and hence broaden the scope of the field, the INTERSPEECH 2011 Speaker State Challenge addressed the algorithmic analysis of medium-term speaker states: alcohol intoxication and sleepiness, both of which are highly relevant in high risk environments. Preserving the paradigms of the two previous INTERSPEECH Challenges, researchers were invited to participate in a large-scale evaluation providing unified testing conditions. This article reviews previous efforts to automatically recognise intoxication and sleepiness from speech signals, and gives an overview on the Challenge conditions and data sets, the methods used by the participants, and their results. By fusing participants’ systems, we show that binary classification of alcoholisation and sleepiness from short-term observations, i.e., single utterances, can both reach over 72% accuracy on unseen test data; furthermore, we demonstrate that these medium-term states can be recognised more robustly by fusing short-term classifiers along the time axis, reaching up to 91% accuracy for intoxication and 75% for sleepiness.
Keywords: computational paralinguistics; intoxication; sleepiness; survey; challenge

Medizinische Bildverarbeitung für die minimal-invasive Behandlung von Vorhofflimmern - Brost, Alexander; Raab, Jens; Kleinöder, Andreas; Kurzendorfer, Tanja; Bourier, Felix; Koch, Martin; Hoffmann, Matthias; Strobel, Norbert; Kurzidim, Klaus; Hornegger, Joachim (Deutsche Zeitschrift für Klinische Forschung, 2013)

Mehrstufige zeit- und bewegungsabhängige Rauschreduktion in Echtzeit mittels CUDA - Bührle, Elmar; Keck, Benjamin; Böhm, Stefan; Hornegger, Joachim (Bildverarbeitung für die Medizin 2009 - Algorithmen Systeme Anwendungen, 2009)

Menschliche und automatische Verständlichkeitsbewertung bei tracheoösophagealen Ersatzstimmen - Haderlein, Tino; Steidl, Stefan; Nöth, Elmar; Schuster, Maria (Fortschritte der Akustik: Plenarvorträge und Fachbeiträge der 31. Deutschen Jahrestagung für Akustik DAGA 2005, München, 2005)
Die Anbahnung einer tracheoösophagealen Ersatzstimme (TE-Stimme) ist eine Möglichkeit, Patienten nach einer totalen Laryngektomie, d.h. Kehlkopfentfernung, die Fähigkeit zu sprechen zurück zu geben. Ein Ventil zwischen Luft- und Speiseröhre erlaubt es, den Luftstrom aus der Lunge umzuleiten und Gewebeschwingungen in der Speiseröhre zur Ersatzstimmgebung zu nutzen. Die Betroffenen durchlaufen eine Therapie, in der wiederholt evaluiert werden muss, ob und wie sich ihre Ersatzstimme hinsichtlich Kriterien wie Lautstärke, Verständlichkeit oder Prosodiefähigkeit entwickelt hat. Da die Beurteilung subjektiv erfolgt und das Verfahren für Arzt und Patienten aufwändig ist, erscheint eine Automatisierung und Objektivierung in diesem Bereich sinnvoll. In unserer Arbeit untersuchen wir, wie gut tracheoösophageale Sprache von einem automatischen Spracherkennungssystem erkannt wird und ob die Ermittlung der Qualität einer Ersatzstimme zumindest teilweise automatisiert erfolgen kann. Dazu müssen die Bewertungen der Maschine und einer Vergleichsgruppe von Experten korrelieren. Im folgenden werden wir unsere ersten Ergebnisse zu diesen Arbeitsgebieten vorstellen.

Mensch-Maschine Interaktion für den interventionellen Einsatz - Prümmer, Marcus; Nöth, Elmar; Hornegger, Joachim; Horndasch, Axel (Bildverarbeitung für die Medizin 2005, 2005)

Metal Artifact Reduction of Biopsy Needles in Digital Breast Tomosynthesis - Jiang, Zhengning; Keck, Benjamin; Riess, Christian; Fischer, Daniel; Mertelmeier, Thomas; Hornegger, Joachim (7th Russian-Bavarian Conference on Biomedical Engineering, 2011)

Methods for Quantification and Respiratory Motion Management in SPECT Imaging - Sanders, James Chester (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Single photon emission computed tomography (SPECT) is a medical imaging modality used to visualize the distribution of radioactive tracers in a patient's body. While SPECT's utility as a diagnostic modality has long been established, its use for planning and managing nuclear medicine therapies has grown in recent years. With these new applications comes a need for absolute quantitation of the amount of radioactivity in tissues. When small, detailed structures are being imaged, a major confounding factor for the quantification task is respiratory motion, which blurs images and leads to underestimation. This thesis seeks to contribute new methods for quantitation and respiratory motion management in SPECT imaging. We first briefly describe the underlying principles that enable SPECT image formation, as well as physical nonidealities and physiological aspects of respiratory motion that confound it. Following this, we introduce methods for analytical and iterative image reconstruction before surveying the techniques that have been developed to correct for these confounding processes. A benefit of these corrections is the enabling of absolute quantitation, and in the next chapter we propose a quantification protocol for Lu-177, an isotope frequently used in radionuclide therapies. After characterizing the protocol in a phantom experiment meant to establish a parameter set offering a favorable bias-variance trade-off, we validate the results with an in vivo patient study. We found that our protocol delivered mean errors relative to truth in the bladder of 10.1%. We then move to the task of respiratory motion management, the first step of which is obtaining a surrogate signal representing a patient's respiratory state over time. After describing five data-driven methods for extracting such a signal, we compare their performance in a phantom experiment and with a collective of cardiac patient scans. We then expand upon this by taking the best-performing method – a dimensionality reduction-based approach using Laplacian Eigenmaps (LE) – and augment it with post-processing steps to make it fit for fully-automated operation in clinical practice. Following this, we present results from a follow-up patient validation on a larger collective with 67 scans indicating that the LE-based approach correlates well with a clinically-accepted sensor-based method. To provide an independent assessment of surrogate signal quality, we then analyze respiratory-gated acquisitions from two types of SPECT scans used for therapy planning: selective internal radionuclide therapy (SIRT) planning scans with Tc-99m- MAA and dosimetry acquisitions for Lu-177-based radionuclide therapies. The results show that data-driven LE surrogates allow recovery of meaningful respiratory motion, and we report preliminary results indicating the type of clinical benefits that compensating for this motion might possibly provide. As a final contribution, we propose an algorithm to improve the robustness of respiratory motion estimation in SPECT projections using a sequence-based estimation scheme and a motion model driven by the surrogate signal itself. In a simulation study, we show that our proposed Sequence-based Motion Model (S-MM) algorithm reduces estimation variance compared to two comparison methods. Furthermore, in a collective of 20 patient Tc-99m-MAA liver scans, S-MM reduces respiratory motion blur more consistently and to a greater extent than the comparison methods. We conclude the thesis with a summary and outlook of possible future work.<br
Keywords: SPECT; emission tomography; laplacian eigenmaps; motion correction; quantitative imaging; radionuclide therapy; respiratory gating; respiratory motion

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

MICCAI 2014 special issue - Hata, Nobuhiko; Hornegger, Joachim (2015)

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

Minimization of Respiratory Motion Artifacts for Whole-Heart Coronary MRI: A Combination of Self-navigation and Weighted Compressed Sensing Reconstruction - Forman, Christoph; Piccini, Davide; Hutter, Jana; Grimm, Robert; Hornegger, Joachim; Zenge, Michael O. (Proceedings of International Society for Magnetic Resonance in Medicine, 2012)

miPod 2: a new hardware platform for embedded real-time processing in sports and fitness applications - Blank, Peter; Hofmann, Steffen; Kulessa, Martin; Eskofier, Björn (Proceedings of the 2016 ACM International Symposium on Wearable Computers, 2016)
In this paper we present the new miPod 2 hardware platform for real-time signal processing and embedded classification of pattern recognition problems in sports and fitness applications. A powerful, energy-friendly and state-of-the-art microcontroller for industry standards, different inertial measurement units, a pulse oximeter, data storages, wireless charging and a Bluetooth Low Energy radio are all integrated into a small and lightweight sensor device. This device is able to capture specified sensor data simultaneously and in addition to execute different complex data processing methods while maintaining high sampling rates and its real-time behavior. The sensor prototype is also easily integrable into different clothes, shoes or sports equipment like balls or rackets to ensure unobtrusive measurements.
Keywords: Inertial Sensor; IMU; Microcontroller; Signal Processing; Real-Time; Embedded Classification

miPod - A Wearable Sports and Fitness Sensor - Blank, Peter; Kugler, Patrick; Eskofier, Björn (10. Symposium der dvs Sportinformatik, 2014)
UnivIS-Import:2016-06-01:Pub.2014.tech.IMMD.IMMD5.mipoda_3

Misalignment compensation for ultra-high-resolution and fast CBCT acquisitions - Magdalena, Herbst; Luckner, Christoph; Wicklein, Julia; Grunz, Jan-Peter; Gassenmaier, Tobias; Ritschl, Ludwig; Kappler, Steffen (Medical Imaging 2019: Physics of Medical Imaging, 2019)

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

Mobile biometrische Ganganalyse - Klucken, Jochen; Barth, Jens; Maertens, Katharina; Eskofier, Björn; Kugler, Patrick; Steidl, Ralph; Hornegger, Joachim; Winkler, Jürgen (Nervenarzt, 2011)

Mobile biosensor-based gait analysis: A diagnostic and therapeutic tool in Parkinson's disease - Klucken, Jochen; Barth, Jens; Maertens, Katharina; Eskofier, Björn; Kugler, Patrick; Steidl, Ralph; Hornegger, Joachim; Winkler, Jürgen; Klucken, Jochen (Nervenarzt, 2011)
Summary: Parkinson's disease (PD) is characterized by progressive motor and non-motor symptoms, leading to distinct diagnostic and therapeutic challenges in all stages of the disease. This study investigated a mobile biosensor-based gait analysis system for patients in early and intermediate stages of PD compared to controls. Subjects wearing a motion sensor-equipped shoe performed a standardized gait exercise. Accelerometer-and gyroscope-based signals were analysed using a complex set of pattern recognition algorithms. The analysis was able (1) to distinguish between PD patients and controls, (2) to identify patients at an early stage of the disease and (3) to distinguish between early and intermediate stage patients. Thus, using this mobile biosensor-based analysis system we were able to obtain objective classifications of gait characteristics in PD. Future studies will show that mobile biosensor-based movement detection technology will support identification of early PD stages and allow objective characterization of motor fluctuations in advanced stages of the disease. This will provide an important and supportive tool for patients, caregivers and therapists. © 2011 Springer-Verlag.

Mobile markerless augmented reality and its application in forensic medicine - Kilgus, Thomas; Heim, Eric; Haase, Sven; Pruefer, Sabine; Mueller, Michael; Seitel, Alexander; Fangerau, Markus; Wiebe, Tamara; Iszatt, Justin; Schlemmer, Heinz-Peter; Hornegger, Joachim; Yen, Kathrin; et al. (International Journal of Computer Assisted Radiology and Surgery, 2015)
The system is mobile, markerless, intuitive and real-time capable with sufficient accuracy. It can support the forensic pathologist during autopsy with augmented reality and textured surfaces. Furthermore, the system enables multimodal documentation for presentation in court. Despite its preliminary prototype status, it has high potential due to its low price and simplicity.
Keywords: Mobile augmented reality; Forensic medicine; Range imaging; Kinect; Iterative closest-point algorithm; Mobile application; Surface documentation

MOBSY: Integration of Vision and Dialogue in Service Robots - Denzler, Joachim; Nöth, Elmar; Schmidt, Jochen; Stemmer, Georg; et al. (Machine Vision and Applications, 2003)

Model-based esophagus segmentation from CT scans using a spatial probability map - Feulner, Johannes; Zhou, S. Kevin; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin; Cavallaro, Alexander Josef (Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, 2010)
Automatic segmentation of the esophagus from CT data is a challenging problem. Its wall consists of muscle tissue, which has low contrast in CT. Sometimes it is filled with air or remains of orally given contrast agent. While air holes are a clear hint to a human when searching for the esophagus, we found that they are rather distracting to discriminative models of the appearance because of their similarity to the trachea and to lung tissue. However, air inside the respiratory organs can be segmented easily. In this paper, we propose to combine a model based segmentation algorithm of the esophagus with a spatial probability map generated from detected air. Threefold cross-validation on 144 datasets showed that this probability map, combined with a technique that puts more focus on hard cases, increases accuracy by 22%. In contrast to prior work, our method is not only automatic on a manually selected region of interest, but on a whole thoracic CT scan, while our mean segmentation error of 1.80mm is even better. © 2010 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD5.modelb

Model-based fusion of CT and non-contrasted 3D C-arm CT: Application to transcatheter valve therapies - Grbic, Sasa; Gesell, Christian; Ionasec, Razvan; John, Matthias; Boese, Jan; Hornegger, Joachim; Navab, Nassir; Comaniciu, Dorin (2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012, 2012)
In recent years transcatheter valve therapies are beginning to replace invasive surgical procedures. As there is no direct view and access to the affected anatomy advanced imaging techniques such as 3D rotational angiography (C-arm CT) and real-time fluoroscopy are used for intra-operative guidance. However, intra-operative modalities have limited image quality of the soft tissue and a reliable assessment of the cardiac anatomy can only be made by injecting contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a novel method to align pre-operative and intra-operative data by using surrogate anatomical structures which are visible in both modalities without adding contrast agent. The trachea bifurcation model is used as a surrogate structure and the model parameters are estimated using robust machine learning algorithms. High quality patient specific models can be extracted from the pre-operative CT and mapped to the intra-operative 3D C-arm CT for guidance. In addition we learn a weighted mapping function for the trachea bifurcation model extracted from the pre-operative and intra-operative images which minimizes the mapping error in respect to the anatomy of interest. Experiments performed on 28 patient pairs of CT and contrasted 3D C-arm CT data sets assure an accuracy of the mapped aortic valve model of 9.08 × 7.31 deg and 7.5 × 7 3.22mm. © 2012 IEEE.

Model-Based Motion Artifact Correction in Digital Subtraction Angiography Using Optical-Flow - Hariharan, Sai Gokul; Kaethner, Christian; Strobel, Norbert; Kowarschik, Markus; DiNitto, Julie; Fahrig, Rebecca; Navab, Nassir (Informatik aktuell, 2019)
Digital subtraction angiography is an important method for obtaining an accurate visualization of contrast-enhanced blood vessels. The technique involves the digital subtraction of two X-ray images, one with contrast filled vessels (fill image) and one without (mask image). Unfortunately, artifacts that are introduced due to the subtraction of misaligned mask and fill images may potentially degrade the diagnostic value of an image. The techniques used for correcting such artifacts involve the use of affine image registration techniques for aligning the mask and fill images and image processing techniques for suppressing the artifacts. Although affine registration techniques often yield acceptable results, they may fail when the imaged object undergoes 3D transformations. The techniques used for suppressing artifacts may cause blurring, when a projection image can no longer be corrected using a globally uniform motion model. In this paper, we have introduced an optical-flow based local motion compensation approach, where pixel-wise deformation fields are computed based on an X-ray imaging model. A visual inspection of the results shows a significant improvement in the image quality due to a reduction in the artifacts caused by misregistrations.
CRIS-Team Scopus Importer:2019-05-14

Model-based Postoperative Modeling of Stent-Based Devices from CT: Application to TAVI - Voigt, Ingmar; Assoumou Mengue, Etienne; Ionasec, Razvan; Tommaso, Mansi; Bernhardt, Dominik; Georgescu, Bogdan; Hornegger, Joachim; Comaniciu, Dorin (1st International MICCAI-Workshop on Computer Assisted Stenting, 2012)

Model-based registration for motion compensation during EP ablation procedures - Brost, Alexander; Liao, Rui; Hornegger, Joachim; Strobel, Norbert (Springer-verlag, 2010)
Radio-frequency catheter ablation (RFCA) has become an accepted treatment option for atrial fibrillation (Afib). RFCA of Afib involves isolation of the pulmonary veins under X-ray guidance. For easier navigation, two-dimensional X-ray imaging may take advantage of overlay images derived from static pre-operative 3-D data set to add anatomical details which, otherwise, would not be visible under X-ray. Unfortunately, respiratory and cardiac motion may impair the utility of static overlay images for catheter navigation. We developed a system for image-based 2-D motion estimation and compensation as a solution to this problem. It is based on 2-D catheter tracking facilitated by model-based registration of an ellipse-shaped model to fluorosocpic images. A mono-plane or a bi-plane X-ray C-arm system can be used. In the first step of the method, a 2-D model of the catheter device is computed. Respiratory and cardiac motion at the site of ablation is then estimated by tracking the catheter device in fluoroscopic images. The cost function of the registration step is based on the average distance of the model to the segmented circumferential mapping catheter using a distance map. In our experiments, the circumferential catheter was successfully tracked in 688 fluoroscopic images with an average 2-D tracking error of 0.59 mm ± 0.25 mm. Our presented method achieves a tracking rate of 10 frames-per-second. © 2010 Springer-Verlag Berlin Heidelberg.
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD5.modelb_3

Model-based respiratory motion compensation for image-guided cardiac interventions - Schneider, Max; Sundar, Hari; Liao, Rui; Hornegger, Joachim; Xu, Chenyang (2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010, 2010)
In this paper we propose and validate a PCA-based respiratory motion model for motion compensation during image-guided cardiac interventions. In a preparatory training phase, a preoperative 3-D segmentation of the coronary arteries is automatically registered with a cardiac gated biplane cineangiogram, and used to build a respiratory motion model. This motion model is subsequently used as a prior within the intraoperative registration process for motion compensation to restrict the search space. Our hypothesis is that the use of this model-constrained registration increases the robustness and registration accuracy, especially for weak data constraints such as low signal-to-noise ratio, the lack of contrast information, or an intraoperative monoplane setting. This allows for reducing radiation exposure without compromising on registration accuracy. Synthetic data as well as phantom and clinical datasets have been used to validate the model-based registration in terms of registration accuracy, robustness and speed. We were able to significantly accelerate the intraoperative registration with a 3-D TRE of less than 2mm for both monoplane images and intraprocedure settings with missing contrast information based on 2-D guidewire tracking, which makes it feasible for motion correction in clinical procedures. ©2010 IEEE.

Model-driven physiological assessment of the mitral valve from 4D TEE - Voigt, Ingmar; Ionasec, Razvan Ioan; Georgescu, Bogdan; Houled, Helene; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin (Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2009)
Disorders of the mitral valve are second most frequent, cumulating 14 percent of total number of deaths caused by Valvular Heart Disease each year in the United States and require elaborate clinical management. Visual and quantitative evaluation of the valve is an important step in the clinical workflow according to experts as knowledge about mitral morphology and dynamics is crucial for interventional planning.1, 2 Traditionally this involves examination and metric analysis of 2D images comprising potential errors being intrinsic to the method. Recent commercial solutions are limited to specific anatomic components, pathologies and a single phase of cardiac 4D acquisitions only. This paper introduces a novel approach for morphological and functional quantification of the mitral valve based on a 4D model estimated from ultrasound data. A physiological model of the mitral valve, covering the complete anatomy and eventual shape variations, is generated utilizing parametric spline surfaces constrained by topological and geometrical prior knowledge. The 4D model's parameters are estimated for each patient using the latest discriminative learning and incremental searching techniques. Precise evaluation of the anatomy using model-based dynamic measurements and advanced visualization are enabled through the proposed approach in a reliable, repeatable and reproducible manner.3 The efficiency and accuracy of the method is demonstrated through experiments and an initial validation based on clinical research results. To the best of our knowledge this is the first time such a patient specific 4D mitral valve model is proposed, covering all of the relevant anatomies and enabling to model the common pathologies at once. © 2009 SPIE.

Modeling dysphonia severity as a function of roughness and breathiness ratings in the GRBAS scale - Ferrer, Carlos A.; Aragón, Efren; Hdez-Díaz, María E.; De Bodt, Marc S.; Cmejla, Roman; Englert, Marina; Behlau, Mara; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2021)
Dysphonia comprises many perceptually deviating aspects of voice, and its overall severity perception is made by the listener according to methods of aggregating the single dimensions which are personally conceived and not well studied. Roughness and breathiness are constituent dimensions in most devised rating scales in clinical use. In this paper, we evaluate several ways to model the mapping of the overall severity as a function of the particular ratings of roughness and breathiness. The models include the simple linear averaging as well as several non-linear variants suggested elsewhere, and some minor adjustments. The models are evaluated on four datasets from different countries, allowing a more global evaluation of how the mapping is conceived. Results show the limitations of the most widely assumed linear approach, while also hinting at a need for a more uniform coverage of the sample space in voice pathology datasets. The models explored in this paper can be expanded to higher-dimensional scales.
CRIS-Team Scopus Importer:2021-11-26
Keywords: Breathiness; Dysphonia; Perceptual scales; Roughness; Vocal quality

Monitoring Subjective Perception and Physiological State of Athletes or Patients in Real-Time using a Mobile Phone - Kugler, Patrick; Bührle, Elmar; Eskofier, Björn; Kühner, Pascal; Schlarb, Heiko; Hornegger, Joachim (Proceedings of the Embedded World Conference 2010, 2010)

Monte-Carlo Simulation and Experimental Validation of a Gamma-Camera Prototype Featuring a Novel Collimation Technique - Meinderink, T.; Osterloh, P.; Osterloh, K.; Götz, Theresa; Kuwert, Torsten; Ritt, Philipp (2018)
EVALuna2:212303

Morphological Features of the Porcine Lacrimal Gland and Its Compatibility for Human Lacrimal Gland Xenografting - Henker, Robert; Scholz, Michael; Gaffling, Simone; Nagayoshi, Asano; Hampel, Ulrike; Garreis, Fabian; Hornegger, Joachim; Paulsen, Friedrich (PLoS ONE, 2013)

Mothers, Adults, Children, Pets - Towards the Acoustics of Intimacy - Batliner, Anton; Schuller, Björn; Schaeffler, Sonja; Steidl, Stefan (Proceedings of ICASSP 2008, 2008)
In this paper, we investigate acoustic features which differentiate the two speech registers neutral and intimate within different constellations of speakers and addressees. Three different types of speakers are considered: mothers addressing their own children or an unknown adult, women with no children addressing an imaginary child or an imaginary adult, and children addressing a pet robot using both intimate and neutral speech. We use a large, systematically generated feature vector, upsampling, and SVM and RF for learning. Results are reported for extensive test-runs facing speaker-independency and using PCA-SFFS vs. SVM-SFFS for feature ranking. Classification performance and most relevant feature types are discussed in detail.
Keywords: affective speech; intimacy; emotion; feature selection

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

Motion Compensated Fan-Beam CT by Enforcing Fourier Properties of the Sinogram - Berger, Martin; Maier, Andreas; Xia, Yan; Hornegger, Joachim; Fahrig, Rebecca (Proceedings of the third international conference on image formation in x-ray computed tomography, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.motion_3
Keywords: GRK-1773

Motion compensation by registration-based catheter tracking - Brost, Alexander; Wimmer, Andreas; Liao, Rui; Hornegger, Joachim; Strobel, Norbert (Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 2011)
The treatment of atrial fibrillation has gained increasing importance in the field of computer-aided interventions. State-of-the-art treatment involves the electrical isolation of the pulmonary veins attached to the left atrium under fluoroscopic X-ray image guidance. Due to the rather low soft-tissue contrast of X-ray fluoroscopy, the heart is difficult to see. To overcome this problem, overlay images from pre-operative 3-D volumetric data can be used to add anatomical detail. Unfortunately, these overlay images are static at the moment, i.e., they do not move with respiratory and cardiac motion. The lack of motion compensation may impair X-ray based catheter navigation, because the physician could potentially position catheters incorrectly. To improve overlay-based catheter navigation, we present a novel two stage approach for respiratory and cardiac motion compensation. First, a cascade of boosted classifiers is employed to segment a commonly used circumferential mapping catheter which is firmly fixed at the ostium of the pulmonary vein during ablation. Then, a 2-D/2-D model-based registration is applied to track the segmented mapping catheter. Our novel hybrid approach was evaluated on 10 clinical data sets consisting of 498 fluoroscopic monoplane frames. We obtained an average 2-D tracking error of 0.61 mm, with a minimum error of 0.26 mm and a maximum error of 1.62 mm. These results demonstrate that motion compensation using registration-based catheter tracking is both feasible and accurate. Using this approach, we can only estimate in-plane motion. Fortunately, compensating for this is often sufficient for EP procedures where the motion is governed by breathing. © 2011 SPIE.

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

Motion compensation in digital subtraction angiography using graphics hardware - Deuerling-Zheng, Yu; Lell, Michael; Galant, Adam; Hornegger, Joachim (Computerized Medical Imaging and Graphics, 2006)
An inherent disadvantage of digital subtraction angiography (DSA) is its sensitivity to patient motion which causes artifacts in the subtraction images. These artifacts could often reduce the diagnostic value of this technique. Automated, fast and accurate motion compensation is therefore required. To cope with this requirement, we first examine a method explicitly designed to detect local motions in DSA. Then, we implement a motion compensation algorithm by means of block matching on modern graphics hardware. Both methods search for maximal local similarity by evaluating a histogram-based measure. In this context, we are the first who have mapped an optimizing search strategy on graphics hardware while paralleling block matching. Moreover, we provide an innovative method for creating histograms on graphics hardware with vertex texturing and frame buffer blending. It turns out that both methods can effectively correct the artifacts in most case, as the hardware implementation of block matching performs much faster: the displacements of two 1024 × 1024 images can be calculated at 3 frames/s with integer precision or 2 frames/s with sub-pixel precision. Preliminary clinical evaluation indicates that the computation with integer precision could already be sufficient. © 2006 Elsevier Ltd. All rights reserved.

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

Motion-Corrected Reconstruction in Cone-Beam Computed Tomography for Knees under Weight-Bearing Condition - Berger, Martin (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2016)
Medical imaging plays an important role in diagnosis and grading of knee conditions such as osteoarthritis. In current clinical practice, 2-D radiography is regularly applied under weight-bearing conditions, which is known to improve diagnostic accuracy. However, 2-D images cannot fully cover the complexity of a knee joint, whereas current 3-D imaging modalities are inherently limited to a supine, unloaded patient position. Recently, cone-beam computed tomography (CBCT) scanners for 3-D weight-bearing imaging have been developed. Their specialized acquisition trajectory poses several challenges for image reconstruction. Patient motion caused by standing or squatting positions can substantially deteriorate image quality, such that the motion has to be corrected during reconstruction. Initial work on motion correction is based on fiducial markers, yet, the approach prolonged image acquisition and required a large amount of manual interaction. The goal of this thesis was to further develop innovative motion correction methods for weight-bearing imaging of knees. Within the course of this thesis, the marker-based motion correction was steadily enhanced. Manual annotation of markers has been replaced by a robust, fully automatic detection of markers and their correspondences. A clear disadvantage of markers is the often tedious attachment, which decreases patient comfort and interferes with the acquisition protocol. Also, the method is limited to rigid motion and an extension to nonrigid deformations is nontrivial. To alleviate these drawbacks, we introduce a novel motion estimation approach that makes use of a prior, motion-free reference reconstruction. The motion of femur and tibia is determined individually by rigid 2-D/3-D registration of bone segmentations from the prior scan, to each of the acquired weight-bearing projection images. Reliability of the registration is greatly influenced by the large amount of overlapping structures, especially for lateral view directions. We compare two different similarity measures used for 2-D/3-D registration and also introduce a temporal smoothness regularizer to improve registration accuracy. A common evaluation of marker- and registration-based approach yields superior image quality using 2-D/3-D registration, particularly in presence of severe, nonrigid motion. Further reduction of the algorithm’s runtime and an automation of bone segmentations could allow for a complete replacement of marker-based motion correction in future applications. In case the clinical setup prohibits acquisition of a prior scan, motion correction relies solely on the acquired projection images. We derived a new motion correction method based on Fourier consistency conditions (FCC) which is independent of surrogates or prior acquisitions. So far, FCC have not been used for motion correction and were typically limited to fan-beam geometries. We first introduced the motion estimation for the fan-beam geometry, followed by a practical extension to CBCT. Numerical phantom simulations revealed a particularly accurate estimation of high-frequency motion and of motion collinear to the scanner’s rotation axis. FCC are currently limited to nontruncated, full-scan projection data, and thus, not yet applicable to real weight-bearing acquisitions. However, a dynamic apodization technique is introduced to account for axial truncation, allowing application to a squatting knee phantom with realistic motion. Given the large improvements in image quality, we are confident that FCC is a future candidate for a completely self-contained motion correction approach in CBCT weight-bearing imaging of knees.<br
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.motion_7
Keywords: Cartilage; Cone-Beam Computed Tomography; Image Reconstruction; Image Registration; Knee Joint; Motion-Correction

Motion Correction and Signal Enhancement in Optical Coherence Tomography - Kraus, Martin (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2016)
Optical Coherence Tomography (OCT) is a non-invasive optical imaging modality with micron scale resolution and the ability to generate 2D and 3D images of the human retina. OCT has found widespread use in ophthalmology. However, motion artifacts induced by the scanning nature restrict the ability to have reliable quantification of OCT images. Furthermore, OCT suffers from speckle noise and signal quality issues. This work addresses these issues by treating the motion correction problem as a special image registration problem. Two or more 3D-OCT volumes with orthogonal scan patterns are acquired. A custom objective function is used to register the input volumes. As opposed to standard image registration, there is no reference volume as all volumes are assumed to be distorted by motion artifacts. To improve the robustness of the correction algorithm, multi-stage and multi-resolution optimization, illuminationand tilt-correction and custom similarity measures and regularization are employed. After registration, the corrected volumes are merged and a single volume with less noise is constructed by adaptively combining the registered data. A large-scale quantitative evaluation was performed using data acquired from 73 healthy and glaucomatous eyes. Three independent orthogonal volume pairs for each location of both the optic nerve head and the macula region were acquired. The results of two motion correction algorithm profiles were compared with performing no motion correction. The evaluation measured registration performance, reproducibility performance and signal improvement using mutual information, error maps based on the difference of automatic segmentation of retinal features and a no-reference image quality assessment. In all three of these aspects, the proposed algorithm leads to major improvements, in accordance with visual inspection. For example, the mean blood vessel map reproducibility error over all data is reduced to 47% of the uncorrected error. The algorithm has been deployed to multiple clinical sites so far. In addition, the technique has been commercialized. The main application is structural imaging for clinical practice and research. The removal of motion artifacts enables high quality en face visualization of features. The technique has also been applied to hand held OCT imaging and small animal imaging. Furthermore, applications in functional imaging in the form of intensity based angiography and Doppler OCT have been demonstrated. Overall, the motion correction algorithm can improve both the visual appearance and the reliability of quantitative measurements derived from 3D-OCT data substantially. This promises to improve diagnosis and tracking of retinal diseases using OCT data.<br
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.motion_1

Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns - Kraus, Martin; Potsaid, Benjamin; Mayer, Markus Anton; Bock, Rüdiger; Baumann, Bernhard; Liu, Jonathan J.; Hornegger, Joachim; Fujimoto, James G. (Biomedical Optics Express, 2012)
High speed Optical Coherence Tomography (OCT) has made it possible to rapidly capture densely sampled 3D volume data. One key application is the acquisition of high quality in vivo volumetric data sets of the human retina. Since the volume is acquired in a few seconds, eye movement during the scan process leads to distortion, which limits the accuracy of quantitative measurements using 3D OCT data. In this paper, we present a novel software based method to correct motion artifacts in OCT raster scans. Motion compensation is performed retrospectively using image registration algorithms on the OCT data sets themselves. Multiple, successively acquired volume scans with orthogonal fast scan directions are registered retrospectively in order to estimate and correct eye motion. Registration is performed by optimizing a large scale numerical problem as given by a global objective function using one dense displacement field for each input volume and special regularization based on the time structure of the acquisition process. After optimization, each volume is undistorted and a single merged volume is constructed that has superior signal quality compared to the input volumes. Experiments were performed using 3D OCT data from the macula and optic nerve head acquired with a high-speed ultra-high resolution 850 nm spectral OCT as well as wide field data acquired with a 1050 nm swept source OCT instrument. Evaluation of registration performance and result stability as well as visual inspection shows that the algorithm can correct for motion in all three dimensions and on a per A-scan basis. Corrected volumes do not show visible motion artifacts. In addition, merging multiple motion corrected and registered volumes leads to improved signal quality. These results demonstrate that motion correction and merging improves image quality and should also improve morphometric measurement accuracy from volumetric OCT data. © 2012 Optical Society of America.

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

Motion Estimation Model for Cardiac and Respiratory Motion Compensation - Käppler, Sebastian; Brost, Alexander; Koch, Martin; Wu, Wen; Bourier, Felix; Chen, Terrence; Kurzidim, Klaus; Hornegger, Joachim; Strobel, Norbert (Springer-verlag, 2012)
Catheter ablation is widely accepted as the best remaining option for the treatment of atrial fibrillation if drug therapy fails. Ablation procedures can be guided by 3-D overlay images projected onto live fluoroscopic X-ray images. These overlay images are generated from either MR, CT or C-Arm CT volumes. As the alignment of the overlay is often compromised by cardiac and respiratory motion, motion compensation methods are desirable. The most recent and promising approaches use either a catheter in the coronary sinus vein, or a circumferential mapping catheter placed at the ostium of one of the pulmonary veins. As both methods suffer from different problems, we propose a novel method to achieve motion compensation for fluoroscopy guided cardiac ablation procedures. Our new method localizes the coronary sinus catheter. Based on this information, we estimate the position of the circumferential mapping catheter. As the mapping catheter is placed at the site of ablation, it provides a good surrogate for respiratory and cardiac motion. To correlate the motion of both catheters, our method includes a training phase in which both catheters are tracked together. The training information is then used to estimate the cardiac and respiratory motion of the left atrium by observing the coronary sinus catheter only. The approach yields an average 2-D estimation error of 1.99 ± 1.20 mm. © 2012 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2012.tech.IMMD.IMMD5.motion_2

Motion Gradients for Epipolar Consistency - Preuhs, Alexander; Manhart, Michael; Hoppe, Elisabeth; Kowarschik, Markus; Maier, Andreas (15th International Meeting on Fully Three-Dimensional Image Reconstruction, 2019)

Movement prediction in rowing using a Dynamic Time Warping based stroke detection - Groh, Benjamin; Reinfelder, Samuel; Streicher, Markus; Taraben, Adib; Eskofier, Björn (IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2014), 2014)
In professional rowing competitions, sensor data is transmitted from an on-board sensor unit on the boat to an external computer system. This system calculates the current position of each boat in real-time. However, incomplete localizations occur as a result of radio transmission outages. This paper introduces an algorithm to overcome transmission outages by predicting the rowing movement. The prediction algorithm is based on accelerometer and GPS data that is provided by the on-board unit before an outage occurs. It uses Subsequence Dynamic Time Warping (subDTW) to detect the rowing strokes in the acceleration signal. Knowing the previous strokes, the system predicts the upcoming strokes, as the rowing motion follows a periodic pattern. Thereby, the GPS measured velocity can be extrapolated and the position is predicted. A further outcome of the subDTW stroke detection is an accurate determination of the rowing stroke rate. In our experiment, we evaluate the rowing stroke detection and stroke rate determination based on subDTW as well as the prediction algorithm for simulated outages of professional race data. It shows a subDTW stroke signal detection of 100% after the start phase of the race. The prediction in case of a sensor outage of 5 seconds leads to a correlation between the predicted velocity and the actual velocity of 0.96 and a resulting position error (RMSE) of 0.3 m.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.moveme

Move Over There: One-Click Deformation Correction for Image Fusion During Endovascular Aortic Repair - Breininger, Katharina; Pfister, Marcus; Kowarschik, Markus; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Fusing intraoperative X-ray with information from preoperative computed tomography for endovascular aortic repair has been shown to reduce radiation exposure, need for contrast agent, and procedure time. However, due to the instruments inserted during the intervention, the vasculature deforms and the fusion loses accuracy. In this paper, we propose an approach to use minimal user input obtained from a single 2D image for deformation correction in 3D. We integrate the 2D positional information as a projective constraint in an as-rigid-as-possible deformation model that allows for intraoperative correction of the deformation. Our method achieves clinically relevant accuracies while keeping user inputs to a minimum. We are able to recover the deformation at the right and left internal iliac bifurcation up to a 3D error of 1.9mm, with an error of 0.5mm orthogonal to the viewing direction, and an error of 1.7mm in depth, while keeping the mean computation time below 6s.
CRIS-Team Scopus Importer:2020-11-20
Keywords: Deformation modeling; Endovascular aortic repair; EVAR; Fluoroscopy; Image fusion

Move Over There One-click Deformation Correction for Image Fusion during Endovascular Aortic Repair - Breininger, Katharina; Pfister, Marcus; Kowarschik, Markus; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

MR-based Attenuation Correction for PET/MR Hybrid Imaging - Navalpakkam, Krishnan Bharath (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2016)
The recent and successful integration of positron emission tomography (PET) and magnetic resonance imaging (MRI) modalities in one device has gained wide attention. This new hybrid imaging modality now makes it possible to image the functional metabolism from PET in conjunction with MRI with its excellent soft tissue contrast. Besides providing specific anatomical detail, MRI also eliminates any ionizing radiation from eg. computed tomography (CT) examinations that is otherwise performed in standard PET/CT hybrid imaging systems. However, an unsolved problem is the question of how to correct for the PET attenuation in an PET/MR system. In this respect, the knowledge of the spatial distribution of linear attenuation coefficients (LAC) of the patient at the PET energy level of 511 keV is required. In standalone PET systems, transmission scans using radioactive sources were used for PET attenuation correction (AC) and if needed were scaled to the PET photon energy level. While in PET/CT systems, the CT information was scaled to PET energies for the same purpose. However, in PET/MR hybrid imaging systems, this approach is not feasible as MR and CT measure aspects of proton and electron densities respectively. Therefore alternate approaches to extract attenuation information have to be pursued. One such approach is to use MR information to estimate the distribution of attenuation coefficients within the imaging subject. This is done by using a simple limited class segmentation procedure to delineate air, soft tissue, fat and lung classes and subsequent assignment of their respective attenuation coefficients at PET energy of 511 keV. This way of generating attenuation maps (μ-maps) is however far from ideal as the most attenuating medium such as cortical bone is ignored. They are instead replaced by the attenuation coefficient of a soft tissue. While this approximation has been widely accepted for PET quantification in whole-body research, it has severe underestimation effects for brain studies. In this thesis, we propose an improved MR-based μ-map generation approach. We demonstrate that dedicated MR sequences such as ultrashort echo time sequences (UTE) are useful for the purpose of attenuation correction. From a multitude of MR images, we generate μ-maps that include cortical bone and contain continuous Hounsfield units (HU) akin to a patient CT. These are then compared against segmentation based approaches. The efficacy of continuous valued μ-maps towards PET quantification is analyzed against different μ-maps such as patient CT, segmented patient CT with bone and segmented patient CT without bone. Results indicate that the proposed MR-based μ-maps provide a less than 5% error in PET quantification than any segmentation based μ-maps for brain studies.<br
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.mrbase
Keywords: MRI; PET; multimodal imaging; attenuation correction; pattern recognition; image processing; PET/MR

MR-projection imaging with perspective distortion as in X-ray fluoroscopy for interventional X/MR-hybrid applications - Lommen, Jonathan; Syben-Leisner, Christopher; Stimpel, Bernhard; Bayer, Siming; Nagel, Armin Michael; Fahrig, Rebecca; Dörfler, Arnd; Maier, Andreas (MR-projection imaging with perspective distortion as in X-ray fluoroscopy for interventional X/MR-hybrid applications, 2018)

MR to X-ray Projection Image Synthesis - Stimpel, Bernhard; Syben-Leisner, Christopher; Würfl, Tobias; Mentl, Katrin; Dörfler, Arnd; Maier, Andreas (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.mrtoxr_0

M = Syntax + Prosody: A syntactic-prosodic labelling scheme for large spontaneous speech databases - Batliner, Anton; Kompe, Ralf; KIESSLING, Andreas; Mast, Marion; Niemann, Heinrich; Nöth, Elmar (Speech Communication, 1998)
In automatic speech understanding, division of continuous running speech into syntactic chunks is a great problem. Syntactic boundaries are often marked by prosodic means. For the training of statistical models for prosodic boundaries large databases are necessary. For the German Verbmobil (VM) project (automatic speech-to-speech translation), we developed a syntactic-prosodic labelling scheme where different types of syntactic boundaries are labelled for a large spontaneous speech corpus. This labelling scheme is presented and compared with other labelling schemes for perceptual-prosodic, syntactic, and dialogue act boundaries. Interlabeller consistencies and estimation of effort needed are discussed. We compare the results of classifiers (multi-layer perceptrons (MLPs) and n-gram language models) trained on these syntactic-prosodic boundary labels with classifiers trained on perceptual-prosodic and pure syntactic labels. The main advantage of the rough syntactic-prosodic labels presented in this paper is that large amounts of data can be labelled with relatively little effort. The classifiers trained with these labels turned out to be superior with respect to purely prosodic or syntactic labelling schemes, yielding recognition rates of up to 96% for the two-class-problem 'boundary versus no boundary'. The use of boundary information leads to a marked improvement in the syntactic processing of the VM system. (C) 1998 Elsevier Science B.V. All rights reserved.
Keywords: syntax; dialogue; prosody; phrase boundaries; prosodic labelling; automatic boundary classification; spontaneous speech; large databases; neural networks; stochastic language models

Multi-channel Convolutional Neural Networks for Automatic Detection of Speech Deficits in Cochlear Implant Users - Arias Vergara, Tomás; Vasquez Correa, Juan; Gollwitzer, Sandra; Orozco Arroyave, Juan Rafael; Schuster, Maria; Nöth, Elmar (CIARP 2019: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2019)
This paper proposes a methodology for automatic detection of speech disorders in Cochlear Implant users by implementing a multi-channel Convolutional Neural Network. The model is fed with a 2-channel input which consists of two spectrograms computed from the speech signals using Mel-scaled and Gammatone filter banks. Speech recordings of 107 cochlear implant users (aged between 18 and 89 years old) and 94 healthy controls (aged between 20 and 64 years old) are considered for the tests. According to the results, using 2-channel spectrograms improves the performance of the classifier for automatic detection of speech impairments in Cochlear Implant users.<br
Keywords: Speech processing; Time-frequency analysis; Multi-channel CNN; Deep learning; Cochlear Implants

Multi-channel Convolutional Neural Networks for Automatic Detection of Speech Deficits in Cochlear Implant Users - Arias Vergara, Tomás; Vasquez Correa, Juan; Gollwitzer, Sandra; Orozco-Arroyave, Juan Rafael; Schuster, Maria; Nöth, Elmar (Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2019)

Multi-channel spectrograms for speech processing applications using deep learning methods - Arias-Vergara, T.; Klumpp, Philipp; Vasquez-Correa, J. C.; Nöth, Elmar; Orozco-Arroyave, J. R.; Schuster, M. (Pattern Analysis and Applications, 2020)
Time–frequency representations of the speech signals provide dynamic information about how the frequency component changes with time. In order to process this information, deep learning models with convolution layers can be used to obtain feature maps. In many speech processing applications, the time–frequency representations are obtained by applying the short-time Fourier transform and using single-channel input tensors to feed the models. However, this may limit the potential of convolutional networks to learn different representations of the audio signal. In this paper, we propose a methodology to combine three different time–frequency representations of the signals by computing continuous wavelet transform, Mel-spectrograms, and Gammatone spectrograms and combining then into 3D-channel spectrograms to analyze speech in two different applications: (1) automatic detection of speech deficits in cochlear implant users and (2) phoneme class recognition to extract phone-attribute features. For this, two different deep learning-based models are considered: convolutional neural networks and recurrent neural networks with convolution layers.
CRIS-Team Scopus Importer:2020-10-02
Keywords: Cochlear implants; Multi-channel spectrograms; Phoneme recognition; Speech processing

Multi-class Detection of Pathological Speech with Latent Features: How does it perform on unseen data? - Wagner, Dominik; Baumann, Ilja; Braun, Franziska; Bayerl, Sebastian P.; Nöth, Elmar; Riedhammer, Korbinian; Bocklet, Tobias (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023)
The detection of pathologies from speech features is usually defined as a binary classification task with one class representing a specific pathology and the other class representing healthy speech. In this work, we train neural networks, large margin classifiers, and tree boosting machines to distinguish between four pathologies: Parkinson's disease, laryngeal cancer, cleft lip and palate, and oral squamous cell carcinoma. We show that latent representations extracted at different layers of a pre-trained wav2vec 2.0 system can be effectively used to classify these types of pathological voices. We evaluate the robustness of our classifiers by adding room impulse responses to the test data and by applying them to unseen speech corpora. Our approach achieves unweighted average F1-Scores between 74.1% and 97.0%, depending on the model and the noise conditions used. The systems generalize and perform well on unseen data of healthy speakers sampled from a variety of different sources.
CRIS-Team Scopus Importer:2023-09-29
Keywords: classification; latent representations; multi-class; pathological speech

Multi-Dimensional Flow-Adapted Compressed Sensing (MDFCS) for time-resolved velocity-encoded Phase Conrast MRA - Hutter, Jana; Grimm, Robert; Forman, Christoph; Greiser, Andreas; Hornegger, Joachim; Schmitt, Peter (Proceedings of the 10th IEEE International Symposium on Biomedical Imaging, 2013)

Multi-Dimensional Flow-Preserving Compressed Sensing (MuFloCoS) for Time-Resolved Velocity-Encoded Phase Contrast MRI - Hutter, Jana; Schmitt, Peter; Saake, Marc; Stübinger, Axel; Grimm, Robert; Forman, Christoph; Greiser, Andreas; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Medical Imaging, 2015)
4-D time-resolved velocity-encoded phase-contrast MRI (4-D PCI) is a fully non-invasive technique to assess hemodynamics in vivo with a broad range of potential applications in multiple cardiovascular diseases. It is capable of providing quantitative flow values and anatomical information simultaneously. The long acquisition time, however, still inhibits its wider clinical use. Acceleration is achieved at present using parallel MRI (pMRI) techniques which can lead to substantial loss of image quality for higher acceleration factors. Both the high-dimensionality and the significant degree of spatio-temporal correlation in 4-D PCI render it ideally suited for recently proposed compressed sensing (CS) techniques. We propose the Multi-Dimensional Flow-preserving Compressed Sensing (MuFloCoS) method to exploit these properties. A multi-dimensional iterative reconstruction is combined with an interleaved sampling pattern (I-VT), an adaptive masked and weighted temporal regularization (TMW) and fully automatically obtained vessel-masks. The performance of the novel method was analyzed concerning image quality, feasibility of acceleration factors up to 15, quantitative flow values and diagnostic accuracy in phantom experiments and an in vivo carotid study with 18 volunteers. Comparison with iterative state-of-the-art methods revealed significant improvements using the new method, the temporal normalized root mean square error of the peak velocity was reduced by 45.32% for the novel MuFloCoS method with acceleration factor 9. The method was furthermore applied to two patient cases with diagnosed high-grade stenosis of the ICA, which confirmed the performance of MuFloCoS to produce valuable results in the presence of pathological findings in 56 s instead of over 8 min (full sampling).
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.multid
Keywords: Compressed sensing; hemodynamics; magnetic resonance imaging (MRI); phase contrast MRI

Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging - Köhler, Thomas (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2017)
The optical resolution of a digital camera is one of its most crucial parameters with broad relevance for consumer electronics, surveillance systems, remote sensing, or medical imaging. However, resolution is physically limited by the optics and sensor characteristics. In addition, practical and economic reasons often stipulate the use of out-dated or low-cost hardware. Super-resolution is a class of retrospective techniques that aims at high-resolution imagery by means of software. Multi-frame algorithms approach this task by fusing multiple low-resolution frames to reconstruct high-resolution images. This work covers novel super-resolution methods along with new applications in medical imaging. The first contribution of this thesis concerns computational methods to super-resolve image data of a single modality. The emphasis lies on motion-based algorithms that are derived from a Bayesian statistics perspective, where subpixel motion of low-resolution frames is exploited to reconstruct a high-resolution image. More specifically, we introduce a confidence-aware Bayesian observation model to account for outliers in the image formation, e.g. invalid pixels. In addition, we propose an adaptive prior for sparse regularization to model natural images appropriately. We then develop a robust optimization algorithm for super-resolution using this model that features a fully automatic selection of latent hyperparameters. The proposed approach is capable of meeting the requirements regarding robustness of super-resolution in real-world systems including challenging conditions ranging from inaccurate motion estimation to space variant noise. For instance, in case of inaccurate motion estimation, the proposed method improves the peak-signal-to-noise ratio (PSNR) by 0.7 decibel (dB) over the state-of-the-art. The second contribution concerns super-resolution of multiple modalities in the area of hybrid imaging. We introduce novel multi-sensor super-resolution techniques and investigate two complementary problem statements. For super-resolution in the presence of a guidance modality, we introduce a reconstruction algorithm that exploits guidance data for motion estimation, feature driven adaptive regularization, and outlier detection to reliably super-resolve a second modality. For super-resolution in the absence of guidance data, we generalize this approach to a reconstruction algorithm that jointly super-resolves multiple modalities. These multi-sensor methodologies boost accuracy and robustness compared to their single-sensor counterparts. The proposed techniques are widely applicable for resolution enhancement in a variety of multi-sensor vision applications including color-, multispectral- and range imaging. For instance in color imaging as a classical application, joint super-resolution of color channels improves the PSNR by 1.5 dB compared to conventional channel-wise processing. The third contribution transfers super-resolution to workflows in healthcare. As one use case in ophthalmology, we address retinal video imaging to gain spatio-temporal measurements on the human eye background non-invasively. In order to enhance the diagnostic usability of current digital cameras, we introduce a framework to gain high-resolution retinal images from low-resolution video data by exploiting natural eye movements. This framework enhances the mean sensitivity of automatic blood vessel segmentation by 10 % when using super-resolution for image preprocessing. As a second application in image-guided surgery, we investigate hybrid range imaging. To overcome resolution limitations of current range sensor technologies, we propose multi-sensor super-resolution based on domain-specific system calibrations and employ high-resolution color images to steer range super-resolution. In ex-vivo experiments for minimally invasive and open surgery procedures using Time-of-Flight (ToF) sensors, this technique improves the reliability of surface and depth discontinuity measurements compared to raw range data by more than 24 % and 68 %, respectively.<br
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.multif
Keywords: Verfahren zur Auflösungserhöhung; Super-resolution

Multi-Frame Super-Resolution with Quality Self-Assessment for Retinal Fundus Videos - Köhler, Thomas; Brost, Alexander; Mogalle, Katja; Zhang, Qianyi; Köhler, Christiane; Michelson, Georg; Hornegger, Joachim; Tornow, Ralf-Peter (Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, 2014)
This paper proposes a novel super-resolution framework to reconstruct high-resolution fundus images from multiple low-resolution video frames in retinal fundus imaging. Natural eye movements during an examination are used as a cue for super-resolution in a robust maximum a-posteriori scheme. In order to compensate heterogeneous illumination on the fundus, we integrate retrospective illumination correction for photometric registration to the underlying imaging model. Our method utilizes quality self-assessment to provide objective quality scores for reconstructed images as well as to select regularization parameters automatically. In our evaluation on real data acquired from six human subjects with a low-cost video camera, the proposed method achieved considerable enhancements of low-resolution frames and improved noise and sharpness characteristics by 74%. In terms of image analysis, we demonstrate the importance of our method for the improvement of automatic blood vessel segmentation as an example application, where the sensitivity was increased by 13% using super-resolution reconstruction. © 2014 Springer International Publishing.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.multif

Multi-GPU Based Evaluation and Analysis of Prehistoric Ice Cores Using OpenCL - Ditter, Alexander; Schaffert, Roman; Fey, Dietmar; Schön, Tobias; Gruber, Roland (IST 2015 Proceedings, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD3.multig
Keywords: climate research; Computed tomography; connected-component analysis; non-destructive testing

Multi-Illuminant Estimation With Conditional Random Fields - Beigpour, Shida; Riess, Christian; van de Weijer, Joost; Angelopoulou, Elli (IEEE Transactions on Image Processing, 2014)

Multi-Lingual Prosodic Processing - Buckow, Jan-Constantin; Warnke, Volker; Huber, Richard; Batliner, Anton; Nöth, Elmar; Niemann, Heinrich (Proc. ESCA Workshop on Dialogue and Prosody, 1999)

Multilingual Speech Recognition - Harbeck, Stefan; Nöth, Elmar; Niemann, Heinrich (Proc. of the 2nd SQEL Workshop on Multi- Lingual Information Retrieval Dialogs, 1997)

Multilingual Weighted Codebooks for Non-native Speech Recognition - Raab, Martin; Gruhn, Rainer; Nöth, Elmar (Proceedings of TSD 2008, 2008)

Multi-Material Beam Hardening Correction (MMBHC) in Computed Tomography - Yang, Qiao; Maass, Nicole; Tian, Mengqiu; Elter, Matthias; Schasiepen, Ingo; Maier, Andreas; Hornegger, Joachim (Proceedings of the 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013)

Multi-modal 2D-3D non-rigid registration - Prümmer, Marcus; Hornegger, Joachim; Pfister, Marcus; Dörfler, Arnd (Medical Imaging 2006: Image Processing, 2006)
In this paper, we propose a multi-modal non-rigid 2D-3D registration technique. This method allows a non-rigid alignment of a patient pre-operatively computed tomography (CT) to few intra operatively acquired fluoroscopic X-ray images obtained with a C-arm system. This multi-modal approach is especially focused on the 3D alignment of high contrast reconstructed volumes with intra-interventional low contrast X-ray images in order to make use of up-to-date information for surgical guidance and other interventions. The key issue of non-rigid 2D-3D registration is how to define the distance measure between high contrast 3D data and low contrast 2D projections. In this work, we use algebraic reconstruction theory to handle this problem. We modify the Euler-Lagrange equation by introducing a new 3D force. This external force term is computed from the residual of the algebraic reconstruction procedures. In the multi-modal case we replace the residual between the digitally reconstructed radiographs (DRR) and observed X-ray images with a statistical based distance measure. We integrate the algebraic reconstruction technique into a variational registration framework, so that the 3D displacement field is driven to minimize the "reconstruction distance" between the volumetric data and its 2D projections using mutual information (MI). The benefits of this 2D-3D registration approach are its scalability in the number of used X-ray reference images and the proposed distance that can handle low contrast fluoroscopies as well. Experimental results are presented on both artificial phantom and 3D C-arm CT images.

Multimodal assessment of Parkinson's disease: a deep learning approach - Vasquez Correa, Juan; Arias Vergara, Tomás; Rafael Orozco-Arroyave, Juan; Eskofier, Björn; Klucken, Jochen; Nöth, Elmar (IEEE Journal of Biomedical and Health Informatics, 2018)
Keywords: Parkinson's Disease; Deep Learning; Convolutional Neural Networks; Speech; Handwriting; Gait

Multi-Modal Biomarker Extraction Framework for Therapy Monitoring of Social Anxiety and Depression Using Audio and Video - Yang, Seung Hee; Pérez Toro, Paula Andrea; Weise, Tobias; Hoffmann, Bettina; Demir, Kubilay; Nöth, Elmar; Kallert, Thomas; Maier, Andreas (International Conference on Machine Learning (Workshop on Machine Learning for Multimodal Healthcare), 2023)
This paper introduces a framework that can be used for feature extraction, relevant to monitoring the speech therapy progress of individuals suffering from social anxiety or depression. It operates multi-modal (decision fusion) by incorporating audio and video recordings of a patient and the corresponding interviewer, at two separate test assessment sessions. The used data is provided by an ongoing project in a day-hospital and outpatient setting in Germany, with the goal of investigating whether an established speech therapy group program for adolescents, which is implemented in a stationary and semi-stationary setting, can be successfully carried out via telemedicine. The features proposed in this multi-modal approach could form the basis for interpretation and analysis by medical experts and therapists, in addition to acquired data in the form of questionnaires. Extracted audio features focus on prosody (intonation, stress, rhythm, and timing), as well as predictions from a deep neural network model, which is inspired by the Pleasure, Arousal, Dominance (PAD) emotional model space. Video features are based on a pipeline that is designed to enable visualization of the interaction between the patient and the interviewer in terms of Facial Emotion Recognition (FER), utilizing the mini-Xception network architecture.<br

Multi-Modal Deep Guided Filtering for Comprehensible Medical Image Processing Supplementary Material - Stimpel, Bernhard; Syben-Leisner, Christopher; Schirrmacher, Franziska; Hölter, Philip; Dörfler, Arnd; Maier, Andreas (IEEE Transactions on Medical Imaging, 2019)
EVALuna2:212806

Multimodal i-vectors to Detect and Evaluate Parkinson's Disease - Garcia, Nicanor; Vasquez Correa, Juan; Arroyave, Juan Rafael Orozco; Nöth, Elmar (Proceedings of INTERSPEECH, 2018)

Multi-Modal Super-Resolution with Deep Guided Filtering - Stimpel, Bernhard; Syben-Leisner, Christopher; Schirrmacher, Franziska; Dörfler, Arnd; Maier, Andreas; Hölter, Philip (Informatik aktuell, 2019)
Despite the visually appealing results, most Deep Learningbased super-resolution approaches lack the comprehensibility that is required for medical applications. We propose a modified version of the locally linear guided filter for the application of super-resolution in medical imaging. The guidance map itself is learned end-to-end from multimodal inputs, while the actual data is only processed with known operators. This ensures comprehensibility of the results and simplifies the implementation of guarantees. We demonstrate the possibilities of our approach based on multi-modal MR and cross-modal CT and MR data. For both datasets, our approach performs clearly better than bicubic upsampling. For projection images, we achieve SSIMs of up to 0.99, while slice image data results in SSIMs of up to 0.98 for four-fold upsampling given an image of the respective other modality at full resolution. In addition, end-to-end learning of the guidance map considerably improves the quality of the results.
CRIS-Team Scopus Importer:2019-05-14

Multi-modal surface registration for markerless initial patient setup in radiation therapy using microsoft's Kinect sensor - Bauer, Sebastian; Wasza, Jakob; Haase, Sven; Marosi, Natalia; Hornegger, Joachim (2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011, 2011)
In radiation therapy, prior to each treatment fraction, the patient must be aligned to computed tomography (CT) data. Patient setup verification systems based on range imaging (RI) can accurately verify the patient position and adjust the treatment table at a fine scale, but require an initial manual setup using lasers and skin markers. We propose a novel markerless solution that enables a fully-automatic initial coarse patient setup. The table transformation that brings template and reference data in congruence is estimated from point correspondences based on matching local surface descriptors. Inherently, this point-based registration approach is capable of coping with gross initial misalignments and partial matching. Facing the challenge of multi-modal surface registration (RI/CT), we have adapted state-of-the-art descriptors to achieve invariance to mesh resolution and robustness to variations in topology. In a case study on real data from a low-cost RI device (Microsoft Kinect), the performance of different descriptors is evaluated on anthropomorphic phantoms. Furthermore, we have investigated the system's resilience to deformations for mono-modal RI/RI registration of data from healthy volunteers. Under gross initial misalignments, our method resulted in an average angular error of 1.5 and an average translational error of 13.4 mm in RI/CT registration. This coarse patient setup provides a feasible initialization for subsequent refinement with verification systems. © 2011 IEEE.

Multiplaner reconstructions and three-dimensional imaging (computed rotational osteography) of complex fractures by using a c-arm system: Initial results - El-Sheik, Michael; Heverhagen, Johannes T.; Alfke, Heiko; Froelich, Jens J.; Hornegger, Joachim; Brunner, Thomas; Klose, Klaus Jochen; Wagner, Hans-Joachim (Radiology, 2001)

Multiple CT-reconstructions for locally adaptive anisotropic wavelet denoising - Borsdorf, Anja; Raupach, Rainer; Hornegger, Joachim (International Journal of Computer Assisted Radiology and Surgery, 2008)
Objective: The signal-to-noise ratio in computed tomography (CT) data should be improved by using adaptive noise estimation for level-dependent threshold determination in the wavelet domain. Method: The projection data measured in CT and, thus, the slices reconstructed from these data are noisy. For a reliable diagnosis and subsequent image processing, like segmentation, the ratio between relevant tissue contrasts and the noise amplitude must be sufficiently large. By separate reconstructions from disjoint subsets of projections, e.g. even and odd numbered projections, two CT volumes can be computed, which only differ with respect to noise. We show that these images allow a position and orientation adaptive noise estimation for level-dependent threshold determination in the wavelet domain. The computed thresholds are applied to the averaged wavelet coefficients of the input data. Results: The final result contains data from the complete set of projections, but shows approximately 50% improvement in signal-to-noise ratio. Conclusions: The proposed noise reduction method adapts itself to the noise power in the images and allows for the reduction of spatially varying and oriented noise. © CARS 2007.

Multiple Device Segmentation for Fluoroscopic Imaging Using Multi-task Learning - Breininger, Katharina; Würfl, Tobias; Kurzendorfer, Tanja; Albarqouni, Shadi; Pfister, Marcus; Kowarschik, Markus; Navab, Nassir; Maier, Andreas (Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 2018)
For endovascular aortic repair (EVAR), integrating preoperative information of the aortic anatomy with intraoperative fluoroscopy can aid in reducing radiation exposure, contrast agent and procedure time. However, the quality of this fusion may deteriorate over the course of the intervention due to patient movement or deformation of the vasculature caused by interventional tools. Automatically detecting the instruments present in the X-ray image can help to assess the degree of deterioration, trigger automatic re-registration or aid in automatic workflow phase detection and process modeling. In this work, we investigate a flexible approach to segment different devices based on fully convolutional neural networks using multi-task learning. We evaluate the proposed approach on a set of 38 X-ray images acquired during EVAR interventions by targeting the segmentation of aortic stents, stiff guidewires and pigtail catheters. We compare the results to the performance of single-task networks. We manage to keep similar performance compared to single-task networks with Dice coefficients between 0.95 and 0.80 depending on the device, while speeding up computation by a factor of two.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.multip
Keywords: Catheters; Convolutional Neural Networks; Endovascular Aortic Repair; Guidewires; Multi-task learning; Stents; X-ray

Multiple Source Translation Micro-CT Improves Both Field-of-View and Spatial Resolution - Yu, Haijun; Ni, Song; Thies, Mareike; Wagner, Fabian; Huang, Yixing; Liu, Fenglin; Maier, Andreas (IEEE Transactions on Instrumentation and Measurement, 2024)
Keywords: Data sampling; field-of-view (FOV); micro-computed tomography (micro-CT); scanning mode; spatial resolution.

Multiple Time Resolutions for Derivatives of Mel-Frequency Cepstral Coefficients - Stemmer, Georg; Hacker, Christian; Nöth, Elmar; Niemann, Heinrich (Proceedings of the Automatic Speech Recognition and Understanding Workshop (ASRU'01), 2001)

Multiscale Blood Vessel Segmentation in Retinal Fundus Images - Budai, Attila; Michelson, Georg; Hornegger, Joachim (Bildverarbeitung für die Medizin 2010 - Algorithmen, Systeme, Anwendungen,, 2010)

Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans - Ghesu, Florin-Cristian; Georgescu, Bogdan; Zheng, Yefeng; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019)
Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and interventional medical image analysis. Current solutions for anatomy detection are typically based on machine learning techniques that exploit large annotated image databases in order to learn the appearance of the captured anatomy. These solutions are subject to several limitations, including the use of suboptimal feature engineering techniques and most importantly the use of computationally suboptimal search-schemes for anatomy detection. To address these issues, we propose a method that follows a new paradigm by reformulating the detection problem as a behavior learning task for an artificial agent. We couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis. In other words, an artificial agent is trained not only to distinguish the target anatomical object from the rest of the body but also how to find the object by learning and following an optimal navigation path to the target object in the imaged volumetric space. We evaluated our approach on 1487 3D-CT volumes from 532 patients, totaling over 500,000 image slices and show that it significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective, while also achieving a 20-30 percent higher detection accuracy. Most importantly, we improve the detection-speed of the reference methods by 2-3 orders of magnitude, achieving unmatched real-time performance on large 3D-CT scans.
CRIS-Team Scopus Importer:2019-06-07
Keywords: Deep learning; deep reinforcement learning; intelligent localization; medical image analysis; multi-scale; real-time detection; scale-space modeling; three-dimensional (3D) object detection

Multi-scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset - Wilm, Frauke; Fragoso, Marco; Bertram, Christof A.; Stathonikos, Nikolas; Öttl, Mathias; Qiu, Jingna; Klopfleisch, Robert; Maier, Andreas; Breininger, Katharina; Aubreville, Marc (Informatik aktuell, 2023)
In histopathology, scanner-induced domain shifts are known to impede the performance of trained neural networks when tested on unseen data. Multidomain pre-training or dedicated domain-generalization techniques can help to develop domain-agnostic algorithms. For this, multi-scanner datasets with a high variety of slide scanning systems are highly desirable. We present a publicly available multi-scanner dataset of canine cutaneous squamous cell carcinoma histopathology images, composed of 44 samples digitized with five slide scanners. This dataset provides local correspondences between images and thereby isolates the scanner-induced domain shift from other inherent, e.g. morphology-induced domain shifts. To highlight scanner differences, we present a detailed evaluation of color distributions, sharpness, and contrast of the individual scanner subsets. Additionally, to quantify the inherent scanner-induced domain shift, we train a tumor segmentation network on each scanner subset and evaluate the performance both in - and cross-domain. We achieve a class-averaged in-domain intersection over union coefficient of up to 0.86 and observe a cross-domain performance decrease of up to 0.38, which confirms the inherent domain shift of the presented dataset and its negative impact on the performance of deep neural networks.
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Multi-Sensor Super-Resolution for Hybrid Range Imaging with Application to 3-D Endoscopy and Open Surgery - Köhler, Thomas; Haase, Sven; Bauer, Sebastian; Wasza, Jakob; Kilgus, Thomas; Maier-Hein, Lena; Stock, Christian; Hornegger, Joachim; Feussner, Hubertus (Medical Image Analysis, 2015)
In this paper, we propose a multi-sensor super-resolution framework for hybrid imaging to super-resolve data from one modality by taking advantage of additional guidance images of a complementary modality. This concept is applied to hybrid 3-D range imaging in image-guided surgery, where high-quality photometric data is exploited to enhance range images of low spatial resolution. We formulate super-resolution based on the maximum a-posteriori (MAP) principle and reconstruct high-resolution range data from multiple low-resolution frames and complementary photometric information. Robust motion estimation as required for super-resolution is performed on photometric data to derive displacement fields of subpixel accuracy for the associated range images. For improved reconstruction of depth discontinuities, a novel adaptive regularizer exploiting correlations between both modalities is embedded to MAP estimation. We evaluated our method on synthetic data as well as ex-vivo images in open surgery and endoscopy. The proposed multi-sensor framework improves the peak signal-to-noise ratio by 2 dB and structural similarity by 0.03 on average compared to conventional single-sensor approaches. In ex-vivo experiments on porcine organs, our method achieves substantial improvements in terms of depth discontinuity reconstruction.
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.multis
Keywords: 3-D endoscopy; Hybrid range imaging; Open surgery; Sensor fusion; Super-resolution

Multi-Task Framework for X-Ray Guided Planning in Knee Surgery - Kordon, Florian Johannes; Fischer, Peter; Privalov, Maxim; Swartman, Benedict; Schnetzke, Marc; Franke, Jochen; Lasowski, Ruxandra; Maier, Andreas; Kunze, Holger (Bildverarbeitung für die Medizin 2020, 2020)
Keywords: Landmark Localization; Multi-label Bone Segmentation; MPFL X-Ray Guidance; Orthopaedics; Surgical Planning

Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels - Gündel, Sebastian; Ghesu, Florin-Cristian; Grbic, Sasa; Gibson, Eli; Georgescu, Bogdan; Maier, Andreas; Comaniciu, Dorin (2019)

Multi-task Localization and Segmentation for X-Ray Guided Planning in Knee Surgery - Kordon, Florian Johannes; Fischer, Peter; Privalov, Maxim; Swartman, Benedict; Schnetzke, Marc; Franke, Jochen; Lasowski, Ruxandra; Maier, Andreas; Kunze, Holger (Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 2019)
X-ray based measurement and guidance are commonly used tools in orthopaedic surgery to facilitate a minimally invasive workflow. Typically, a surgical planning is first performed using knowledge of bone morphology and anatomical landmarks. Information about bone location then serves as a prior for registration during overlay of the planning on intra-operative X-ray images. Performing these steps manually however is prone to intra-rater/inter-rater variability and increases task complexity for the surgeon. To remedy these issues, we propose an automatic framework for planning and subsequent overlay. We evaluate it on the example of femoral drill site planning for medial patellofemoral ligament reconstruction surgery. A deep multi-task stacked hourglass network is trained on 149 conventional lateral X-ray images to jointly localize two femoral landmarks, to predict a region of interest for the posterior femoral cortex tangent line, and to perform semantic segmentation of the femur, patella, tibia, and fibula with adaptive task complexity weighting. On 38 clinical test images the framework achieves a median localization error of 1.50 mm for the femoral drill site and mean IOU scores of 0.99, 0.97, 0.98, and 0.96 for the femur, patella, tibia, and fibula respectively. The demonstrated approach consistently performs surgical planning at expert-level precision without the need for manual correctio
Keywords: Landmark localization; Multi-label bone segmentation; MPFL X-ray guidance; Orthopaedics; Surgical planning

Multi-View Depth-Aware Rigid 2-D/3-D Registration - Schaffert, Roman; Wang, Jian; Fischer, Peter; Borsdorf, Anja; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2018-02-22:Pub.2017.tech.IMMD.IMMD5.multiv

Mumford-Shah model for one-to-one edge matching - Han, Jingfeng; Berkels, Benjamin; Droske, Marc; Hornegger, Joachim; Rumpf, Martin; Schaller, Carlo; Scorzin, Jasmin; Urbach, Horst (IEEE Transactions on Image Processing, 2007)
This paper presents a new algorithm based on the Mumford-Shah model for simultaneously detecting the edge features of two images and jointly estimating a consistent set of transformations to match them. Compared to the current asymmetric methods in the literature, this fully symmetric method allows one to determine one-to-one correspondences between the edge features of two images. The entire variational model is realized in a multiscale framework of the finite element approximation. The optimization process is guided by an estimation minimization-type algorithm and an adaptive generalized gradient flow to guarantee a fast and smooth relaxation. The algorithm is tested on T1 and T2 magnetic resonance image data to study the parameter setting. We also present promising results of four applications of the proposed algorithm: interobject monomodal registration, retinal image registration, matching digital photographs of neurosurgery with its volume data, and motion estimation for frame interpolation. © 2007 IEEE.

Myocardial Scar Segmentation in LGE-MRI using Fractal Analysis and Random Forest Classification - Kurzendorfer, Tanja; Breininger, Katharina; Steidl, Stefan; Brost, Alexander; Forman, Christoph; Maier, Andreas (Proceedings - International Conference on Pattern Recognition, 2018)
Late-gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the clinical gold standard to visualize myocardial scarring. The gadolinium based contrast agent accumulates in the damaged cells and leads to various enhancements in the LGE-MRI scan. The quantification of the scar tissue is very important for diagnosis, treatment planning, and guidance during the procedure. In clinical routine, the scar is often segmented manually. However, manual segmentation is prone to inter- and intra-observer variability and very time consuming. In this work a new texture based scar quantification is proposed. For texture characterization, segmentation based fractal analysis is used. First, the image is decomposed into a set of binary images by applying a two-threshold binary decomposition. Second, a set of features are extracted for each of the binary images, namely the fractal dimension, the mean gray value, and the size of the binary object. In addition, the local and global intensity of each patch is added to the feature vector. In the next step, the features are classified using a random forest classifier. The scar quantification is evaluated on 30 clinical LGE-MRI data sets. In addition, the results are compared to the x-fold standard deviation approach and the full-width-at-half-max method, which are implemented in a fully automatic manner. The proposed scar quantification achieved a mean Dice coefficient of 0.64±0.17 and outperforms the x-fold standard deviation approach.
CRIS-Team Scopus Importer:2020-11-20

Myocardial Twist from X-ray Angiography - Geimer, Tobias; Unberath, Mathias; Höhn, Johannes; Achenbach, Stephan; Maier, Andreas (Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen, 2018)
We present preliminary evidence that left ventricular twist can be observed and thus estimated from rotational coronary angiography. Our method is based on an ellipsoidal parametric model initially developed for functional analysis of cardiac tagged MRI. First, we fit the model to 3D coronary artery centerlines reconstructed from rotational angiography and then use 3D/2D registration to optimize for the functional parameters driving the model. On two clinical acquisitions, we show that our method is able to recover cardiac motion indicated by an average reduction in reprojection error of 28.1±3.0 %. Analysis of the functional progression of the functional parameters over time reveals radial and longitudinal contraction, and left ventricular twist. We believe that these results are exciting and encourage improvement of the proposed method in future work.<br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.myocar

Myocardial twist from X-ray angiography: Can we observe left ventricular twist in rotational coronary angiography? - Geimer, Tobias; Unberath, Mathias; Höhn, Johannes; Achenbach, Stephan; Maier, Andreas (Informatik aktuell, 2018)
We present preliminary evidence that left ventricular twist can be observed and thus estimated from rotational coronary angiography. Our method is based on an ellipsoidal parametric model initially developed for functional analysis of cardiac tagged MRI. First, we fit the model to 3D coronary artery centerlines reconstructed from rotational angiography and then use 3D/2D registration to optimize for the functional parameters driving the model. On two clinical acquisitions, we show that our method is able to recover cardiac motion indicated by an average reduction in reprojection error of 28.1±3.0%. Analysis of the functional progression of the functional parameters over time reveals radial and longitudinal contraction, and left ventricular twist. We believe that these results are exciting and encourage improvement of the proposed method in future work.
CRIS-Team Scopus Importer:2022-06-05

N2NSR-OCT: Simultaneous denoising and super-resolution in optical coherence tomography images using semisupervised deep learning - Qiu, Bin; You, Yunfei; Huang, Zhiyu; Meng, Xiangxi; Jiang, Zhe; Zhou, Chuanqing; Liu, Gangjun; Yang, Kun; Ren, Qiushi; Lu, Yanye (Journal of Biophotonics, 2020)
Optical coherence tomography (OCT) imaging shows a significant potential in clinical routines due to its noninvasive property. However, the quality of OCT images is generally limited by inherent speckle noise of OCT imaging and low sampling rate. To obtain high signal-to-noise ratio (SNR) and high-resolution (HR) OCT images within a short scanning time, we presented a learning-based method to recover high-quality OCT images from noisy and low-resolution OCT images. We proposed a semisupervised learning approach named N2NSR-OCT, to generate denoised and super-resolved OCT images simultaneously using up- and down-sampling networks (U-Net (Semi) and DBPN (Semi)). Additionally, two different super-resolution and denoising models with different upscale factors (2× and 4×) were trained to recover the high-quality OCT image of the corresponding down-sampling rates. The new semisupervised learning approach is able to achieve results comparable with those of supervised learning using up- and down-sampling networks, and can produce better performance than other related state-of-the-art methods in the aspects of maintaining subtle fine retinal structures.
CRIS-Team Scopus Importer:2020-10-23
Keywords: denoising; optical coherence tomography; semisupervised deep learning; super-resolution

Nanoparticle precipitation in a T-mixer: Coupling experimentaland numerical investigations of the fluid dynamics with the solid formation processes - Peukert, Wolfgang; Gradl, Johannes; Schwertfirm, Florian; Schwarzer, Hans-Christoph; Schmid, Hans-Joachim; Manhart, Michael (ASME 2006 2nd Joint U.S.-European Fluids Engineering Summer Meeting Collocated With the 14th International Conference on Nuclear Engineering, 2006)
Mixing and consequently fluid dynamic is a key parameter to tailor the particle size distribution (PSD) in nanoparticle precipitation. Due to fast and intensive mixing a static T-mixer configuration is capable .for synthesizing continuously nanoparticles. The flow and concentration field of the applied mixer is investigated experimentally at different flow rates by Particle Image Velocimetry (PIV) and Laser Induced Fluorescence (LIF). Due to the PIV measurements the flow field in the mixer was characterized qualitatively and the mixing process itself is quantified by the subsequent LIF-measurements. A special feature of the LIF set up is to detect structures in the flow field, which are smaller than the Batchelor length. Thereby a detailed insight into the mixing process in a static T-Mixer is given. In this study a CFD-based approach using Direct Numerical Simulation (DNS) in combination with the solid formation kinetics solving population balance equations (PBE) is applied, using barium sulfate as modeling material. A Lagrangian Particle Tracking strategy is used to couple the flow field information with a micro mixing model and with the classical theory of nucleation. We found that the DNS-PBE approach including macro and micro mixing, combined with the population balance is capable of predicting the full PSD in nanoparticle precipitation for different operating parameters. Additionally to the resulting PSD, this approach delivers a 3D-information about all running subprocesses in the mixer, i.e. supersaturation built-up or nucleation, which is visualized for different process variables. Copyright © 2006 by ASME.
Keywords: mixing; fluid; dynamic; nanoparticle; precipitation

Natural element analysis of the Cahn-Hilliard phase-field model - Rajagopal, Amirtham; Fischer, Peter; Kuhl, Ellen; Steinmann, Paul (Computational Mechanics, 2010)
We present a natural element method to treat higher-order spatial derivatives in the Cahn–Hilliard equation. The Cahn–Hilliard equation is a fourth-order nonlinear partial differential equation that allows to model phase separation in binary mixtures. Standard classical C0-continuous finite element solutions are not suitable because primal variational formulations of fourth-order operators are well-defined and integrable only if the finite element basis functions are piecewise smooth and globally C1-continuous. To ensure C1-continuity, we develop a natural-element-based spatial discretization scheme. The C1-continuous natural element shape functions are achieved by a transformation of the classical Farin interpolant, which is basically obtained by embedding Sibsons natural element coordinates in a Bernstein–Bézier surface representation of a cubic simplex. For the temporal discretization, we apply the (second-order accurate) trapezoidal time integration scheme supplemented with an adaptively adjustable time step size. Numerical examples are presented to demonstrate the efficiency of the computational algorithm in two dimensions. Both periodic Dirichlet and homogeneous Neumann boundary conditions are applied. Also constant and degenerate mobilities are considered. We demonstrate that the use of C1-continuous natural element shape functions enables the computation of topologically correct solutions on arbitrarily shaped domains.
UnivIS-Import:2015-04-14:Pub.2010.tech.FT.FT-TM.natura

Natural Language Analysis to Detect Parkinson's Disease - Perez-Toro, Paula Andrea; Vasquez Correa, Juan; Strauß, Martin; Orozco Arroyave, Juan Rafael; Nöth, Elmar (SPRINGER INTERNATIONAL PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND, 2019)
Parkinson's disease (PD) is a neuro-degenerative disorder that produces motor and non-motor impairments. Non-motor impairments include communication and mood disorders. Most of the studies in the literature have been focused on the analysis of motor symptoms. However, non-motor signs are also present in most of the cases. This paper addresses the study of language production as a potential tool to diagnose and monitor the neurological state of PD patients. The study proposes the use of natural language processing methods to extract features from transcriptions obtained from spontaneous speech recordings to discriminate between healthy control people and PD patients. The analysis considered classical features such as Bag of Words and Term Frequency-Inverse Document Frequency, along with methods based on word-embeddings. Accuracies of up to 72% are obtained when discriminating between PD patients and healthy subjects, which confirms that there is information embedded in the language production that can be used for the assessment of the disease.
Keywords: Parkinson's disease; Natural language processing; Text processing; Language analysis; Classification

Natural Language Analysis to Detect Parkinson’s Disease - Pérez-Toro, P. A.; Vasquez Correa, Juan; Strauß, Martin; Orozco Arroyave, Juan Rafael; Nöth, Elmar (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
Parkinson’s disease (PD) is a neuro-degenerative disorder that produces motor and non-motor impairments. Non-motor impairments include communication and mood disorders. Most of the studies in the literature have been focused on the analysis of motor symptoms. However, non-motor signs are also present in most of the cases. This paper addresses the study of language production as a potential tool to diagnose and monitor the neurological state of PD patients. The study proposes the use of natural language processing methods to extract features from transcriptions obtained from spontaneous speech recordings to discriminate between healthy control people and PD patients. The analysis considered classical features such as Bag of Words and Term Frequency-Inverse Document Frequency, along with methods based on word-embeddings. Accuracies of up to 72% are obtained when discriminating between PD patients and healthy subjects, which confirms that there is information embedded in the language production that can be used for the assessment of the disease.
CRIS-Team Scopus Importer:2019-10-15
Keywords: Classification; Language analysis; Natural language processing; Parkinson’s disease; Text processing

Navigation for fluoroscopy-guided cryo-balloon ablation procedures of atrial fibrillation - Bourier, Felix; Brost, Alexander; Kleinöder, Andreas; Kurzendorfer, Tanja; Koch, Martin; Kiraly, Attila P.; Schneider, Hans-Jürgen; Hornegger, Joachim; Strobel, Norbert; Kurzidim, Klaus (Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 2012)
Atrial fibrillation (AFib), the most common arrhythmia, has been identified as a major cause of stroke. The current standard in interventional treatment of AFib is the pulmonary vein isolation (PVI). PVI is guided by fluoroscopy or non-fluoroscopic electro-anatomic mapping systems (EAMS). Either classic point-to-point radio-frequency (RF)- catheter ablation or so-called single-shot-devices like cryo-balloons are used to achieve electrically isolation of the pulmonary veins and the left atrium (LA). Fluoroscopy-based systems render overlay images from pre-operative 3-D data sets which are then merged with fluoroscopic imaging, thereby adding detailed 3-D information to conventional fluoroscopy. EAMS provide tracking and visualization of RF catheters by means of electro-magnetic tracking. Unfortunately, current navigation systems, fluoroscopy-based or EAMS, do not provide tools to localize and visualize single shot devices like cryo-balloon catheters in 3-D. We present a prototype software for fluoroscopy-guided ablation procedures that is capable of superimposing 3-D datasets as well as reconstructing cyro-balloon catheters in 3-D. The 3-D cyro-balloon reconstruction was evaluated on 9 clinical data sets, yielded a reprojected 2-D error of 1.72 mm ± 1.02 mm. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Navigation System with Contact Force Assessment to Guide Pulmonary Vein Isolation Procedures - Koch, Martin; Langenkamp, Arne; Kirally, Atilla; Brost, Alexander; Hornegger, Joachim; Strobel, Norbert (23rd Conference of the Society for Medical Innovation and Technology (SMIT), 2011)

Neue Information im Sprachsignal: Die prosodische Markierung der Fokusstruktur - Batliner, Anton; Oppenrieder, Willi; Nöth, Elmar; Stallwitz, Gabriele (Fortschritte der Akustik, DAGA 90, 1990)

Neural Networks Matching Contours in CAD Models - Wilke, Peter; Greska, Wolfgang; Geiger, Manfred; Berentz, Jörg (1994)

Neurochemical dementia diagnostics: Assays in CSF and blood - Lewczuk, Piotr; Hornegger, Joachim; Zimmermann, Rüdiger; Otto, Markus; Wiltfang, Jens; Kornhuber, Johannes (European archives of psychiatry and clinical neuroscience, 2008)
In this review, current neurochemical dementia diagnostics (NDD) procedures are presented with a focus on biomarkers in the cerebrospinal fluid (CSF) and blood: amyloid β peptides, tau protein, and its phosphorylated form (pTau). CSF analysis is an increasingly important tool for early and differential diagnosis of dementia syndromes. Although lumbar puncture is a mildly invasive procedure with a low incidence of complications, establishing blood assays capable of reliably measuring NDD biomarkers is an aim of several studies worldwide. © 2008 Springer.

NeuroGraph 2.0 Handbuch - Wilke, Peter (1993)
UnivIS-Import:2016-06-23:Pub.1993.tech.IMMD.IMMD2._neuro_35

NeuroGraph - An Integrated Development Environment for Neural Networks - Genetic Algorithms and Fuzzy Logic - Wilke, Peter; Rehder, Jan; Billing, Gunnar; Nilson, Jörg; Mansfeld, Christian (EUFIT95, 1995)

NeuroGraph - Ein Simulator für künstliche Neuronale Netze auf paralleler Hardware - Wilke, Peter (ASIM93, 1993)

NeuroGraph - Ein Simulator für künstliche Neuronale Netzeauf paralleler Hardware - Wilke, Peter; Scholz, R. (PASA93, 1993)

NeuroGraph V3.4 Bedienhandbuch - Wilke, Peter; et al. (1995)
UnivIS-Import:2016-06-27:Pub.1995.tech.IMMD.IMMD2._neuro_9

NeuroGraph V3.4 User's Manual - Wilke, Peter; et al. (1995)
UnivIS-Import:2016-06-27:Pub.1995.tech.IMMD.IMMD2._neuro_5

NeuroGraph V3.6 Bedienhandbuch - Wilke, Peter; et al. (1996)
UnivIS-Import:2016-06-27:Pub.1996.tech.IMMD.IMMD2._neuro

NeuroGraph V3.6 Users Manual - Wilke, Peter (1996)

Neuronale Netze zur Konturanalyse in CAD-Modellen - Greska, Wolfgang; Wilke, Peter; Geiger, Manfred; Berentz, Jörg (Zeitschrift für Wirtschaftliche Fertigung und Automatisierung, 1994)

NeuroSpeech: An open-source software for Parkinson's speech analysis - Rafael Orozco-Arroyave, Juan; Vasquez Correa, Juan; Francisco Vargas-Bonilla, Jesus; Arora, R.; Dehak, N.; Nidadavolu, P. S.; Christensen, H.; Rudzicz, F.; Yancheva, M.; Chinaei, H.; Vann, A.; Vogler, N.; et al. (Digital Signal Processing, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.neuros
Keywords: Parkinson's disease; Dysarthria; Speech processing; Python; Software

New computer aided device for real time analysis of speech of people with Parkinson's disease - Correa, Juan Camilo Vásquez; Arroyave, Juan Rafael Orozco; Arias-Londoño, Julián David; Bonilla, Jesús Francisco Vargas; Nöth, Elmar (Revista Facultad De Ingenieria-Universidad De Antioquia, 2014)
Parkinson's disease (PD) is a neurodegenerative disorder that affects the coordination of muscles and limbs, including those responsible of the speech production. The lack of control of the limbs and muscles involved in the speech production process can generate intelligibility problems and this situation has a negative impact in the social interaction of the patients. It is already demonstrated that constant speech therapy can improve the communication abilities of the patients; however, the measurement of the recovery progress is done subjectively by speech therapists and neurologists. Due to this, it is required the development of flexible tools able to asses and guide the speech therapy of the patients. In this paper the design and deployment of a new device for the real time assessment of speech signals of people with PD is presented. The processes of design and deployment include the development on three platforms: first, a graphic user interface is developed on Matlab, second the first prototype is implemented on a digital signal processor (DSP) and third, the final device is developed on a mini-computer. The device is equipped with an audio codec, storage capacity and the processing unit. Besides, the system is complemented with a monitor to display the processed information on real time and with a keyboard enabling the interaction of the end-user with the device. Different acoustics and nonlinear dynamics measures which have been used in the state of the art for the assessment of speech of people with PD are implemented on the three mentioned platforms. In accordance with the state of the art, the designed platforms show an increment in the variation of the fundamental period of speech (commonly called pitch) of people with PD. Additionally, the decrease of the vocal space area is validated for the case of patients with PD. These results indicate that the designed device is useful to perform the assessment and monitoring of the speech therapy of people with PD.
Keywords: Jitter; Nonlinear dynamics; Parkinson's disease; Pitch; Portable device; Real time speech assessment; Shimmer; Vocal space area

New cues in low-frequency of speech for automatic detection of Parkinson's disease - Belalcazar-Bolaños, Elkyn; Orozco-Arroyave, Juan Rafael; Vargas-Bonilla, Jesús Francisco; Arias-Londoño, Julián David; Castellanos-Domínguez, C.; Nöth, Elmar (2013)
In this paper, the analysis of low-frequency zone of the speech signals from the five Spanish vowels, by means of the Teager energy operator (TEO) and the modified group delay functions (MGDF) is proposed for the automatic detection of Parkinson's disease. According to our findings, different implementations of the TEO are suitable for tackling the problem of the automatic detection of Parkinson's disease. Additionally, the application of MGDF for improving the resolution of the speech spectrum in the low frequency zone is able for enhancing differences exhibited between the first two formants from speech of people with Parkinson's disease and healthy controls. The best results are obtained for vowel /e/, where accuracy rates of up to 92% are achieved. This is an interesting result specially if it is considered that there are muscles that are involved in the production of the vowel /e/ but not in other vowels. © 2013 Springer-Verlag.
Keywords: Group Delay Functions; Parkinson's disease; Teager Energy Operator

New Spanish speech corpus database for the analysis of people suffering from Parkinson's disease - Orozco-Arroyave, J. R.; Arias-Londono, J. D.; Vargas-Bonilla, J. F.; Gonzalez-Rativa, M. C.; Nöth, Elmar (2014)
In this paper we present a new database with speech recordings of 50 patients with PD and their respective healthy controls, matched by age and gender. All of the participants are Spanish native speakers and the recordings were collected following a protocol that considers both technical requirements and several recommendations given by experts in linguistics, phoniatry and neurology. This corpus includes tasks such as sustained phonations of the vowels, diadochokinetic evaluation, 45 words, 10 sentences, a reading text and a monologue. The paper also includes results of the characterization of the Spanish vowels considering different measures used in other works to characterize different speech impairments.
Keywords: Parkinson's disease; Spanish; database

Noise Adaptice Bilateral Filtering in Computed Tomography - Borsdorf, Anja; Kappler, Steffen; Raupach, Rainer; Noo, Frédéric; Hornegger, Joachim (The 20th UCAIR Symposium, 2008)

Noise Reduction In Low-Dose CT Using a 3D Multiscale Sparse Denoising Autoencoder - Mentl, Katrin; Mailhé, Boris; Ghesu, Florin-Cristian; Schebesch, Frank; Haderlein, Tino; Maier, Andreas; Nadar, Mariappan (2017 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.noiser_1

Non-Contrast Head CT Imaging Using Dual Energy: A Study on Bias Induced by Imperfections in Energy Response and Material Knowledge - Haase, Viktor; Stierstorfer, Karl; Hahn, Katharina; Schoendube, Harald; Maier, Andreas; Noo, Frédéric (Proceedings of the 16th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2021)
Diagnostic of stroke and proton therapy treatment planning require accurate non-contrast head CT imaging. Dual energy CT offers improvements in this field by enabling material decomposition and virtual mono-energetic images. Such a computation in the projection space requires an energy response model for the CT system and a selection of materials for data decomposition. In this work, we use incorrect response models and materials to study the effects of imperfections on the quality of the material decomposition. The experiments are carried out in fanbeam geometry with noise-free computer simulated data of two head phantoms. The results show that small errors in the energy response can quickly lead to small inhomogeneity and to beam hardening errors between close bones. They also show that the difference between soft tissues and water lead to soft tissue leakage in the bone image, but may not have a negative effect on the mono-energetic image.<br
Keywords: Dual Energy Computed Tomography; Material Decomposition; Energy Response Model; Non-Contrast Head CT Imaging; Simulation Study

Non-convex polyhedral volume of interest selection - Fuchs, Raphael; Welker, Volkmar; Hornegger, Joachim (Computerized Medical Imaging and Graphics, 2010)
We introduce a novel approach to specify and edit volumes of interest (VOI for short) interactively. Enhancing the capabilities of standard systems we provide tools to edit the VOI by defining a not necessarily convex polyhedral bounding object. We suggest to use low-level editing interactions for moving, inserting and deleting vertices, edges and faces of the polyhedron. The low-level operations can be used as building blocks for more complex higher order operations fitting the application demands. Flexible initialization allows the user to select within a few clicks convex VOI that in the classical clipping plane model need the specification of a large number of cutting planes. In our model it is similarly simple to select non-convex VOI. Boolean combinations allow to select non-connected VOI of arbitrary complexity. The polyhedral VOI selection technique enables the user to define VOI with complex boundary structure interactively, in an easy to comprehend and predictable manner. © 2009 Elsevier Ltd. All rights reserved.

Non-Destructive Digitization of Soiled Historical Chinese Bamboo Scrolls - Stromer, Daniel; Christlein, Vincent; Huang, Xiaolin; Zippert, Patrick; Helmecke, Eric; Hausotte, Tino; Maier, Andreas (2018 13th IAPR Workshop on Document Analysis Systems (DAS), 2018)
For about 2000 years, no paper was used as a media in China but writings and drawings were captured on bamboo and wooden slips. Several slips were bound together with strips and rolled up to a scroll. The writings and drawings were either brushed or even carved into the wood. Those documents are very precious for culture inheritance and research, but due to aging processes, the discovered pieces are sometimes in a poor condition and also soiled. Because cleaning the slips is not only challenging but also writings could be erased, we developed a method to digitize such historical documents without the need of cleaning. We perform a 3-D X-ray micro-CT scan resulting in a 3-D volume of the complete document. With our approach, we were able to investigate the scroll without any manual labor (e.g. unwrapping or cleaning). We showed that the method also works for heavily soiled scrolls where nothing is readable with the naked eye. This can help conservators to store all writings before they may be erased by the cleaning process. Finally, we present a manual technique to virtually unwrap and post-process the documents resulting in a 2-D image of all bamboo slips.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.nondes
Keywords: Bamboo and wooden slips; 3-D X-ray CT; Historical Chinese Documents; Document Digitization

Nonlinear Diffusion Noise Reduction in CT Using Correlation Analysis - Mayer, Markus Anton; Borsdorf, Anja; Köstler, Harald; Hornegger, Joachim; Rüde, Ulrich (3rd Russian-Bavarian Conference on Biomedical Engineering, 2007)

Nonlinear Diffusion vs. Wavelet Based Noise Reduction in CT Using Correlation Analysis - Mayer, Markus Anton; Borsdorf, Anja; Köstler, Harald; Hornegger, Joachim; Rüde, Ulrich (Vision, Modelling, and Visualisation 2007, 2007)

Nonlinear dynamics and Poincare sections to model gait impairments in different stages of Parkinson's disease - Pérez Toro, Paula Andrea; Vasquez Correa, Juan; Arias Vergara, Tomás; Nöth, Elmar; Orozco-Arroyave, J. R. (Nonlinear Dynamics, 2020)
Parkinson's disease is a progressive neurological disorder that affects the motor system and produces several problems to control muscles and limbs. One of the major manifestations of the disease appears in gait and typically causes disability in the most advanced stages of the disease. Gait assessment is suitable for the diagnosis and monitoring of the neurological state of the patients. Gait is mainly evaluated from signals collected with inertial sensors attached to the limbs, and kinematics features are commonly extracted. Besides the classical kinematic methods, there are nonlinear phenomena in the gait process that cannot be properly modeled with kinematic features. This study proposes the use of several nonlinear dynamics features to assess gait impairments of Parkinson's patients. We consider classical nonlinear features including correlation dimension, largest Lyapunov exponent, Hurst exponent, and others. In addition, we propose a novel nonlinear analysis based on the construction of Gaussian mixture models to find clusters in Poincare sections. The nonlinear dynamics features proposed here are used to discriminate between Parkinson's patients and healthy subjects, and to classify patients in different stages of the disease. To the best of our knowledge, this is the first study that considers nonlinear dynamics analysis including Poincare sections to assess gait impairments of patients with Parkinson's disease.
Keywords: Parkinson's disease; Nonlinear dynamics; Poincare section; Gait assessment; Gaussian mixture models; Inertial sensors

Nonlinear dynamics and Poincaré sections to model gait impairments in different stages of Parkinson’s disease - Pérez-Toro, P. A.; Vasquez Correa, Juan; Arias Vergara, Tomás; Nöth, Elmar; Orozco Arroyave, Juan Rafael (Nonlinear Dynamics, 2020)
Parkinson’s disease is a progressive neurological disorder that affects the motor system and produces several problems to control muscles and limbs. One of the major manifestations of the disease appears in gait and typically causes disability in the most advanced stages of the disease. Gait assessment is suitable for the diagnosis and monitoring of the neurological state of the patients. Gait is mainly evaluated from signals collected with inertial sensors attached to the limbs, and kinematics features are commonly extracted. Besides the classical kinematic methods, there are nonlinear phenomena in the gait process that cannot be properly modeled with kinematic features. This study proposes the use of several nonlinear dynamics features to assess gait impairments of Parkinson’s patients. We consider classical nonlinear features including correlation dimension, largest Lyapunov exponent, Hurst exponent, and others. In addition, we propose a novel nonlinear analysis based on the construction of Gaussian mixture models to find clusters in Poincaré sections. The nonlinear dynamics features proposed here are used to discriminate between Parkinson’s patients and healthy subjects, and to classify patients in different stages of the disease. To the best of our knowledge, this is the first study that considers nonlinear dynamics analysis including Poincaré sections to assess gait impairments of patients with Parkinson’s disease.
CRIS-Team Scopus Importer:2020-06-05
Keywords: Gait assessment; Gaussian mixture models; Inertial sensors; Nonlinear dynamics; Parkinson’s disease; Poincaré section

Non-linear dynamics characterization fromwavelet packet transform for automatic recognition of emotional speech - Vásquez-Correa, J.; Orozco-Arroyave, Juan Rafael; Arias-Londoño, Julián David; Vargas-Bonilla, Jesús Francisco; Nöth, Elmar (Springer Science and Business Media Deutschland GmbH, 2016)
A new set of features based on non-linear dynamics measures obtained from the wavelet packet transform for the automatic recognition of “fear-type” emotions in speech is proposed. The experiments are carried out using three different databases with a Gaussian MixtureModel for classification. The results indicate that the proposed approach is promising for modeling “fear-type” emotions in speech.
Keywords: Non-linear dynamics; Non-linear speech processing; Speech emotion recognition; Wavelet packet transform

Non-Periodic 3-D Motion Estimation and Reconstruction of Coronary Stents - Rohkohl, Christopher; Lauritsch, Günter; Hornegger, Joachim (Proceedings of 11th Fully 3D Meeting and 3rd HPIR Workshop, 2011)

Non-photorealistic rendering for minimally invasive procedures - Raab, Jens; Schäfer, Henry; Brost, Alexander; Stamminger, Marc; Pfister, Marcus (SPIE Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 2013)

Non-rigid Fusion of Morphological and Functional Images Using Anatomical Fix Points and Contours - A New Approach to Overcome the Current Drawbacks of Retrospective Image Fusion - Römer, Wolfgang; Hornegger, Joachim; Han, Jingfeng; Bautz, Werner; Kuwert, Torsten (RSNA2004, 2005)

Non-rigid movements of heart region from many angiographics views - Bouattour, Sahla; Heigl, Benno; Paulus, Dietrich; Hornegger, Joachim (Vision, Modeling and Visualization 03, 2003)

Non-rigid registration guided by landmarks and learning - Eckl, Jutta; Daum, Volker; Hornegger, Joachim; Pohl, Kilian M. (2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012, 2012)
Registration methods frequently rely on prior information in order to generate anatomical meaningful transformations between medical scans. In this paper, we propose a novel intensity based non-rigid registration framework, which is guided by landmarks and a regularizer based on Principle Component Analysis (PCA). Unlike existing methods in this domain, the computational complexity of our approach reduces with the number of landmarks. Furthermore, our PCA is invariant to translations. The additional regularizer is based on the outcome of this PCA. We register a skull CT scan to MR scans aquired by a MR/PET hybrid scanner. This aligned CT scan can then be used to gain an attenuation map for PET reconstruction. As a result we have a Dice coefficient for bone areas at 0.71 and a Dice coefficient for bone and soft issue areas at 0.97. © 2012 IEEE.

Nonrigid registration of joint histograms for intensity standardization in magnetic resonance imaging - Hornegger, Joachim; Jäger, Florian (IEEE Transactions on Medical Imaging, 2009)
A major disadvantage of magnetic resonance imaging (MRI) compared to other imaging modalities like computed tomography is the fact that its intensities are not standardized. Our contribution is a novel method for MRI signal intensity standardization of arbitrary MRI scans, so as to create a pulse sequence dependent standard intensity scale. The proposed method is the first approach that uses the properties of all acquired images jointly (e.g., T1- and T2-weighted images). The image properties are stored in multidimensional joint histograms. In order to normalize the probability density function (pdf) of a newly acquired data set, a nonrigid image registration is performed between a reference and the joint histogram of the acquired images. From this matching a nonparametric transformation is obtained, which describes a mapping between the corresponding intensity spaces and subsequently adapts the image properties of the newly acquired series to a given standard. As the proposed intensity standardization is based on the probability density functions of the data sets only, it is independent of spatial coherence or prior segmentations of the reference and current images. Furthermore, it is not designed for a particular application, body region or acquisition protocol. The evaluation was done using two different settings. First, MRI head images were used, hence the approach can be compared to state-of-the-art methods. Second, whole body MRI scans were used. For this modality no other normalization algorithm is known in literature. The Jeffrey divergence of the pdfs of the whole body scans was reduced by 45%. All used data sets were acquired during clinical routine and thus included pathologies. © 2006 IEEE.

Non-Rigid Registration to Capture Optic Nerve Head Variability - Paulus, Jan; Bock, Rüdiger; Daum, Volker; Hornegger, Joachim (Bildverarbeitung für die Medizin 2010 - Algorithmen, Systeme, Anwendungen, 2010)

Non-stationary CT image noise spectrum analysis - Balda, Michael; Heismann, Björn; Hornegger, Joachim (Bildverarbeitung für die Medizin, 2010)

No-reference wavelet based retinal image quality assessment - Abdel Hamid, L. S.; El-Rafei, A.; El-Ramly, S.; Michelson, Georg; Hornegger, Joachim (5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VipIMAGE 2015, 2016)
Retinal Image Quality Assessment (RIQA) is an essential preliminarily step in Automatic Retinal Screening Systems (ARSS) to avoid misdiagnosis of retinal disease. In this work, a no-reference wavelet based RIQA algorithm suitable for real-time ARSS is introduced. Retinal images are classified into good and bad quality images using two proposed metrics. These measures are extracted from the wavelet decomposition subbands of the image to reflect sharpness and contrast. Wavelet level and regional analyses are performed to determine the most relevant levels and retinal region to be used in the RIQA. The algorithm is tested on two publicly available datasets with different resolutions. Area under the ROC curve (AUC) of 1 and 0.985 are obtained for the low and high resolution datasets respectively with runtime ranging between 0.1 and 2 seconds depending on image resolution. Comparison with other RIQA algorithms shows superiority of the proposed algorithm in both performance and time.
Keywords: Real-time system; Retinal Image Quality Assessment; Wavelet transform

Novel Evaluation Metrics for Vascular Structure Segmentation - Reimann, Marcel; Fu, Weilin; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

Novel Method for Comparison of Pre-Planned Ablation Lines for Treatment of Atrial Fibrillation Using a Common Reference Model - Koch, Martin; Kleinöder, Andreas; Bourier, Felix; Hornegger, Joachim; Strobel, Norbert (Proceedings of 2013 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2013)

Novel Projection-Based Unsupervised Respiratory Motion Feedback for Free-Breathing Whole-Heart Coronary MR Imaging - Forman, Christoph; Piccini, Davide; Hornegger, Joachim; Zenge, Michael O. (Proceedings of the 21st Annual Meeting of ISMRM, 2013)

Novel Visualization Approach of an Automated Image Based Glaucoma Risk Index for Intuitive Diagnosis - Meier, Jörg; Bock, Rüdiger; Nyul, Laszlo G.; Hornegger, Joachim; Michelson, Georg (Analysis of Biomedical Signals and Images, Proceedings of the Biosignal 2008 International Eurasip Conference, 2008)

Numerical and experimental investigation of the turbulent flow and mixing in a static T-mixer - Peukert, Wolfgang; Schwertfirm, Florian; Manhart, Michael; Gradl, Johannes; Schwarzer, Hans-Christoph (ASME 2006 2nd Joint U.S.-European Fluids Engineering Summer Meeting Collocated With the 14th International Conference on Nuclear Engineering, 2006)
Turbulent mixing plays an important role in chemical engineering, especially when the chemical reaction is fast compared to the mixing time. In this context a detailed knowledge of the flow field, the distribution of turbulent kinetic energy (TKE) and its dissipation rate is important, as these quantities are used for many mixing models. For this reason we conduct two direct numerical simulations (DNS) of a confined impinging jet reactor (CIJR) at Re = 500 and Sc = 1, with and without modelling the feeding pipes. The data is compared with particle image velocimetry (PIV) measurements and it is shown that only the simulation which includes the feeding pipes gives the correct flow field. This flow field is dominated by a stable vortex in the main mixing duct. High intensities of turbulent kinetic energy and dissipation are found in the impingement zone and decrease rapidly towards the exit of the CIJR. In the whole CIJR the turbulence is not in equilibrium. The mixing of the scalar is rapid in the impingement zone, but due to the fast decrease of the turbulent intensities and due to the relaminarisation of the flow the PDFs of the scalar distributions in cross sections of the CIJR take a bimodal form after the impingement zone. Copyright © 2006 by ASME.
Keywords: turbulence; mixing; kinetic; energy; simulation; jet; impinging

Numerical study of the packing density of fine powders by means of the Discrete Element Method - Blümel, Christina; Ribeiro Parteli, Eric Josef; Schmidt, Jochen; Wirth, Karl Ernst; Peukert, Wolfgang; Pöschel, Thorsten (85th Annual Meeting of the International Association of Applied Mathematics and Mechanics – GAMM 2014, 2014)
Numerical simulations by means of the Discrete Element Method (DEM) can provide a helpful tool in the investigation of the packing behavior of powders. In this type of numerical simulations, the Newton’s equations of motion for every single particle in the system are solved by taking into account all forces and torques acting on it, both due to external fields and due to interactions with other particles in the system

Numeric quantification of intelligibility in schoolchildren with isolated and combined cleft palate - Vogt, Beate; Maier, Andreas; Batliner, Anton; Nöth, Elmar; Nkenke, Emeka; Eysholdt, Ulrich; Schuster, Maria (HNO, 2007)
Objective. Even after adequate surgical and nonsurgical treatment, combined or isolated clefts often cause functional disorders, such as speech disorders. Speech disorders vary widely in extent and can take the form, for example, of specific features of articulation, with altered nasal emission and shifted articulation, leading to reduced speech intelligibility. So far it has not been possible to describe the relationship between cleft type and intelligibility except subjective, categorical evaluation. Methods. Intelligibility of the speech as influenced by cleft palate in 58 children aged 9±2 was quantified objectively and numerically by means of automatic speech recognition technology in speech recordings for the PLAKSS test [test for psycholinguistic analysis of childhood speech impairments]. It was found that 33 children had a unilateral cleft lip and palate (CLP), 9, bilateral CLP, 10, isolated cleft palate, and 6, submucosal cleft palate. As a control group, 84 children aged 9±1 years and with physiological articulation were recruited from a mainstream school. Speech intelligibility is expressed as word accuracy, which means the percentage of correctly recognized words in a word sequence. Results. Word accuracy levels in the control group were between 39% and 84% (mean 62%±10%), while the children with clefts achieved values between 0% and 73% (mean 42%±17%). There was no difference in intelligibility between children with isolated cleft palate and those with combined cleft type. Differences were, however, found between each group and the control group (p<0.01). Conclusion. Speech intelligibility of children with isolated cleft palate does not significantly differ from that of children with combined cleft type including the palate, as has been objectively quantified for the first time by objective methods. In the presence of speech disorders, the need for diagnosis and therapy is just as great in children with isolated cleft palate as in children with combined cleft types. © 2007 Springer Medizin Verlag.
Keywords: Automatic speech recognition; Cleft lip and palate; Isolated cleft palate; Objective evaluation; Speech intelligibility

Numerische Quantifizierung der Verständlichkeit von Schulkindern mit isolierter und kombinierter Gaumenspalte - Vogt, Beate; Maier, Andreas; Batliner, Anton; Nöth, Elmar; Nkenke, Emeka; Eysholdt, Ulrich; Schuster, Maria (HNO, 2007)

Objective vs. Subjective Evaluation of Speakers with and without Complete Dentures - Haderlein, Tino; Bocklet, Tobias; Maier, Andreas; Nöth, Elmar; Knipfer, Christian; Stelzle, Florian (Proc. Text, Speech and Dialogue; 12th International Conference, 2009)

Object recognition using Hidden Markov Models - Hornegger, Joachim; Niemann, Heinrich; Paulus, Dietrich; Schlottke, Gero (Pattern Recognition in Practice IV: Multiple Paradigms, Comparative Studies and Hybrid Systems, 1994)

Object Removal in Gradient Domain of Cone-Beam CT Projections - Bier, Bastian; Berger, Martin; Maier, Jennifer; Unberath, Mathias; Hsieh, Scott; Bonaretti, Serena; Fahrig, Rebecca; Levenston, Marc; Gold, Garry; Maier, Andreas (2016 IEEE Nuclear Science Symposuim & Medical Imaging Conference Record, 2016)

Objektive und automatische Ermittlung der Verständlichkeit von Kindern und Jugendlichen mit Lippen-Kiefer-Gaumenspalten - Schuster, Maria; Maier, Andreas; Vogt, Beate; Nöth, Elmar; Nkenke, Emeka; Holst, Alexandra; Eysholdt, Ulrich; Rosanowski, Frank (Aktuelle phoniatrisch-pädaudiologische Aspekte 2006, 2006)

Obstacle Fusion and Scene Interpretation for Autonomous Driving with Occupancy Grids - Seeger, Christoph (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2023)
Autonomous driving is, besides electrification, currently one of the most competitive areas in the automotive industry. A substantially challenging part of autonomous vehicles is the reliable perception of the driving environment. This dissertation is concerned with improving the sensor-based perception and classification of the static driving environment. In particular, it focuses on recognizing road boundaries and obstacles on the road, which is indispensable for collision-free automated driving. Moreover, an exact perception of static road infrastructure is essential for accurate localization in a priorly built highly precise navigation map, which is currently commonly used to extend the environment model beyond the limited range of sensors. The first contribution is concerned with environment sensors with a narrow vertical field of view, which frequently fail to detect obstacles with a small vertical extent from close range. As an inverse beam sensor model infers free space if there is no measurement, those obstacles are deleted from an occupancy grid even though they have been observed in past measurements. The approach presented here explicitly models those errors using multiple hypotheses in an evidential grid mapping framework that neither requires a classification nor a height of obstacles. Furthermore, the grid mapping framework, which usually assumes mutually independent cells, is extended to information from neighboring cells. The evaluation in several freeway scenarios and a challenging scene with a boom barrier shows that the proposed method is superior to evidential grid mapping with an inverse beam sensor model. In the second contribution, a common shortcoming of occupancy grid mapping is approached. Multi-sensor fusion algorithms, such as a Kalman filter, usually perform obstacle association and gating to improve the obstacle position if multiple sensors detected it. However, this strategy is not common in occupancy grid fusion. In this dissertation, an efficient method to associate obstacles across sensor grids is proposed. Imprecise sensors are discounted locally in cells where a more accurate sensor detected the same obstacle and derived free space. The quantitative evaluation with an exact navigation map shows an increased obstacle position accuracy compared to standard evidential occupancy grid mapping. Whereas the first two contributions were concerned with multi-sensor fusion approaches for collision avoidance, the third uses occupancy grids for situation interpretation. In particular, this work proposes to use occupancy maps to classify the driving environment into the categories freeway, country or rural road, parking area, and city street. Identifying the current road type is essential for autonomous driving systems designed for limited environment types. Inspired by the success of deep learning approaches in image classification, end-to-end Convolutional Neural Networks are compared to Support Vector Machines trained on hand-crafted features. Two novel CNN architectures for occupancy-grid-based situation classification, designed for embedded applications with limited computing power are proposed. Furthermore, the occupancy-grid-based classification is fused with camera-image-based classification, and the road type probabilities are recursively estimated over time with a Bayes filter. The evaluation of the approaches on an extensive data set yielded accuracies of up to 99 %.<br
Keywords: CNN; DNN; autonomous driving; bayes; car; classification; convolutional; deep; deep network; dempster-shafer; evidential; fusion; grid; learning; mapping; neural network; occupancy; road classification; road type; robotics; scene interpretation; situation classification; situation interpretation Einrichtungen / Fakultäten / Fachbereiche: Technische Faku

OCPAD - Occluded Checkerboard Pattern Detector - Fürsattel, Peter; Deitsch, Sergiu; Placht, Simon; Balda, Michael; Maier, Andreas; Riess, Christian (IEEE Winter Conference on Applications of Computer Vision, 2016)
Many camera calibration techniques require the detection of a pattern with known geometry, e.g., a checkerboard. Typically, the pattern must be fully contained in the field of view. This brings several limitations, one of which is that lens distortion can not reliably be estimated in outer image regions. This paper presents the occluded checkerboard pattern detector (OCPAD) to find checkerboards, even in a) low-resolution images, b) images with high lens distortion and if c) the pattern is partly occluded or not completely within the field of view. We exploit that checkerboards can easily be represented by a graph. We use graph matching to find the largest partial checkerboard in the image. Our detector complements a state-of-the-art calibration algorithm. Quantitatively, detection rates are considerably improved over the state-of-the-art. Additionally, estimation of lens distortion is greatly improved at outer image regions. Here, the reprojection error is improved by up to 50%.<br

OCT Angiography: Measurement of Retinal Macular Microvasculature with Spectralis II OCT Angiography - Reliability and Reproducibility - Hosari, Sami; Hohberger, Bettina; Theelke, Luisa; Sari, Hasan; Lucio, Marianna; Mardin, Christian Y. (Ophthalmologica, 2019)
Aim: The aim of the present study was to investigate the reliability of macular microvasculature measurements in normal subjects by Heidelberg Spectralis II optical coherence tomography angiography (OCT-A) in combination with a newly made software. Subjects and Methods: This prospective study included 23 eyes of 23 persons from the Erlangen Glaucoma Registry (ISSN 2191-5008, CS-2011; NTC00494923). The subjects underwent a complete clinical, standardized ophthalmologic examination to rule out any eye disease. En face OCT-A imaging was done using Heidelberg Spectralis II OCT (Heidelberg, Germany). Images were recorded with a 15 × 15° angle and a lateral resolution of 5.7 μm/pixel, resulting in a retinal section of 2.9 × 2.9 mm. The Erlangen-Angio-Tool (EA-Tool) OCT-A application performed multiple segmentations, allowing analysis of the vessel density in 12 segments. The software was coded in MATLAB. Macular data on the superficial vascular plexus (SVP), intermediate capillary plexus (ICP), and deep capillary plexus (DCP) were exported into the application and analyzed separately. The EA-Tool calculated the percentage of "white area" in the "total area" of the region of interest, called vessel density. Foveolar avascular zones (FAZs) of the SVP, ICP, and DCP were calculated manually. To investigate the reproducibility of the new software, individual scans (SVP, ICP, and DCP) were analyzed twice with the EA-Tool and intraclass coefficients (ICCs) of the vessel density values were calculated. Statistical analysis was performed with SPSS version 21.0. Results: The mean vessel density of the SVP ranged between 30.4 and 33.5, that of the ICP between 20.9 and 24.7, and that of the DCP between 23.5 and 27.6. Bland-Altman plots showed a good reliability of two consecutive scans of each sector (S1-S12) in the SVP, ICP, and DCP. Testing reproducibility, no statistically significantly different sectorial coefficients of variation of the SVP, ICP, and DCP were observed (p > 0.05). The mean FAZ area of the SVP was 0.43 ± 0.16 mm2, that of the ICP 0.28 ± 0.1 mm2, and that of the DCP 0.44 ± 0.12 mm2. Conclusions: Spectralis OCT II, in combination with the semiautomated vessel density software EA-Tool, showed good or even excellent ICCs in 75% of all segments of the SVP, ICP, and DCP. The ICCs for the FAZ area in the SVP, ICP, and DCP were excellent.
CRIS-Team Scopus Importer:2019-09-24

OCT-OCTA Segmentation: a Novel Framework and an Application to Segment Bruch's Membrane in the Presence of Drusen - Schottenhamml, Julia; Moult, Eric M.; Novais, Eduardo A.; Kraus, Martin; Lee, Byungkun; Choi, Woojhon; Ploner, Stefan; Husvogt, Lennart; Lu, Chen D.; Yiu, Patrick; Rosenfeld, Philip; Duker, Jay S.; et al. (Investigative Ophthalmology & Visual Science, 2017)
Purpose : We present a novel framework for segmenting optical coherence tomography (OCT) and OCT angiography (OCTA) that jointly uses structural and angiographic information. We term this new paradigm “OCT-OCTA segmentation,” and demonstrate its utility by segmenting Bruch’s membrane (BM) in the presence of drusen. Methods : We developed an automatic OCT-OCTA graph-cut algorithm for BM segmentation. Our algorithm’s performance was quantitatively validated by comparing it with manual segmentation in 7 eyes (6 patients; 73.8±5.7 y/o) with drusen. The algorithm was also qualitatively assessed in healthy eyes (n=13), eyes with diabetic retinopathy (n=21), early/intermediate age-related macular degeneration (AMD) (n=14), exudative AMD (n=5), geographic atrophy (GA) (n=6), and polypoidal choroidal vasculopathy (n=7). Results : The absolute pixel-wise error between the manual and automatic segmentations had the following values: mean: 4.5±0.89um; 1st Quartile: 1.9±1.35um; 2nd Quartile: 3.9±1.90um; and 3rd Quartile: 6.3±2.67. This corresponds to a mean absolute error smaller than the optical axial resolution of our OCT system ( 8-9um). In all other tested eyes, qualitative visual inspection showed BM contours that were deemed suitably accurate for use in forming en face OCT(A) projections. The algorithm’s poorest results occurred in GA patients with large areas of atrophy. Conclusions : By leveraging both structural and angiographic information we showed that OCT-OCTA segmentation is likely to be a widely useful framework for segmenting ocular structur
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.octoct_8
Keywords: oct; octa; oct angiography; layer segmentation; drusen; diabetic retinopathy

OCT-OCTA Segmentation - Schottenhamml, Julia; Moult, Eric M.; Novais, Eduardo A.; Kraus, Martin; Lee, Byungkun; Choi, WooJhon; Ploner, Stefan; Husvogt, Lennart; Lu, Chen D.; Yiu, Patrick; Rosenfeld, Philip; Duker, Jay S.; et al. (Bildverarbeitung für die Medizin 2018, 2018)
In this work, a novel paradigm for segmenting optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) is presented [1]. Since it uses OCT and OCTA information jointly it is called “OCT-OCTA segmentation” and its usefulness is demonstrated by segmenting the Bruch’s Membrane (BM) in the presence of drusen. Therefore a fully automatic graph-cut algorithm was developed and evaluated by comparing the automatic segmentation results with manual segmentation in 7 eyes (6 patients; 73.8 ± 5.7 y/o) with nascent geographic atrophy and/or drusen associated geographic atrophy.<br
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.octoct

Odin’s Eye – A Close Look at Gamification of Labelling of Ophthalmic Diseases - Fan, Fuxin; Qiu, Jingna; Wang, JiaWei; Valianos, Stelica; Farooq, Muhammad Muqtasid; Fu, Weilin; Kordon, Florian Johannes; Maier, Andreas (Joint Conference of the GMDS & CEN-IBS 2020, 2020)
Introduction State of the art deep learning [1] requires vast amounts of accurately labeled training data to enable high classification performance [2]. In order to obtain sufficient amounts of data, data donation is a feasible approach [3]. Yet, the data is only usable, if correct annotations are present. One way to create such annotations is crowd-sourcing via gamification. In this paper, we present an integrated training and annotation approach that allows large scale annotation of ophthalmic diseases. Methods We created a game called “Odin’s Eye” in order to make image classification an exciting and rewarding experience. The game has three modes that are used to slowly lead the player to the complex field of ophthalmic diseases. In order to do so, we used image data from the ODIR 2019 Challenge (https://odir2019.grand-challenge.org/dataset/). The dataset contains more than 7000 images showing healthy eye data and different pathologies. In “Normal Mode”, the player is shown one fundus image and is asked to select whether the shown image is “normal” or shows Cataract, Macular Degeneration, Glaucoma, Retinopathy, or Myopia. After a sufficient number of correct answers, the player can access the unlabeled mode, in which the user is able to give annotation to unlabeled image. The annotations for the unlabeled images will be shown in the “Unlabeled Result”. The “Difficult Mode” is inspired by puzzle games such as Candy Crush or Zookeeper. Here, the player is displayed various fundus images and is asked to align them such that three images showing the same pathology form either a row or a column. Once such a triplet is found, it is eliminated and new images enter the game canvas from the top. In order to make the game more challenging, a curtain enters the field of view from the top that gradually increases the pressure on the player as well as increases game difficulty via decreasing the image brightness. After each successful elimination, the curtain is raised a little and the time limit is increased. Rewarding strategies like reward animation and image shuffling are applied to achieve better user experience. The idea of using triplets is appealing as images of unknown pathology can be mixed in the game area. The player will implicitly classify such images when he tries to align them with two more images of the same class. A leader board shows high scores in order to encourage players to compare their scores online and to compete in creating a large number of annotations. Results A prototype was implemented in Unity [4]. In order to test the game idea, only five prototypical images were chosen for each pathology at present. The difficulty of the game can easily be increased by increasing types of diseases. First experiences with test players confirmed that in particular the “Difficult Mode” has a very rewarding game experience. Prototypes are available as Android APK and WebGL (https://www.medicaldatadonors.org/index.php/odins-eye/). An in-game video was created to demonstrate the gameplay (https://youtu.be/UehnND9gkvY). Conclusion A believe that we created a challenging yet rewarding game experience. In future versions, we will make use of additional images from the ODIR 2019 dataset in order to create slowly increasing difficulty in the game to convince even expert players to keep playing Odin’s Eye. References [1] Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik, 29(2), 86-101. [2] Bertram, C. A., Aubreville, M., Marzahl, C., Maier, A., & Klopfleisch, R. (2019). A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor. Scientific data, 6(1), 1-9. [3] Servadei, L., Schmidt, R., Eidelloth, C., & Maier, A. (2017, October). Medical Monkeys: A Crowdsourcing Approach to Medical Big Data. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 87-97). Springer, Cham. [4] Murray, J. W. (2014). C# game programming cookbook for Unity 3D. AK Peters/CRC Press
Keywords: Gamification; Medical Image Annotation; Deep Learning

"Of All Things the Measure is Man" - Classification of Emotions and Inter-Labeler Consistency - Steidl, Stefan; Levit, Michael; Batliner, Anton; Nöth, Elmar; Niemann, Heinrich (Proceedings of ICASSP 2005 - International Conference on Acoustics, Speech, and Signal Processing, 2005)
In traditional classification problems, the reference needed for training a classifier is given and considered to be absolutely correct. However, this does not apply to all tasks. In emotion recognition in non-acted speech, for instance, one often does not know which emotion was really intended by the speaker. Hence, the data is annotated by a group of human labelers who do not agree on one common class in most cases. Often, similar classes are confused systematically. We propose a new entropy-based method to evaluate classification results taking into account these systematic confusions. We can show that a classifier which achieves a recognition rate of "only" about 60% on a four-class-problem performs as well as our five human labelers on average.

Offline Writer Identification Using Convolutional Neural Network Activation Features - Christlein, Vincent; Bernecker, David; Maier, Andreas; Angelopoulou, Elli (Pattern Recognition, 2015)
Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR 2013 benchmark database and the CVL dataset. While we perform comparably to the state of the art on CVL, our proposed method yields about 0.21 absolute improvement in terms of mAP on the challenging bilingual ICDAR dataset.
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.IMMD5.offlin_86

On Feature Tracking in X-Ray Images - Klüppel, Moritz; Wang, Jian; Bernecker, David; Fischer, Peter; Hornegger, Joachim (Bildverarbeitung für die Medizin 2014, 2014)
Feature point tracking and detection of X-ray images is challenging due to overlapping anatomical structures of different depths, which lead to low-contrast images. Tracking of motion in X-ray sequences can support many clinical applications like motion compensation or two- or three-dimensional registration algorithms. This paper is the first to evaluate the performance of several feature tracking and detection algorithms on artificial and real X-ray image sequences, which involve rigid motion as well as external disturbances. A stand-alone application has been developed to provide an overall test bench for all algorithms, realized by OpenCV implementations. Experiments show that the Karhunen Loeve Transform-based Tracker is the most consistent and effective tracking algorithm. Considering external disturbances, template matching provides the most sufficient results. Furthermore, the influence of feature point detection methods on tracking results is shown.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.onfeat

On Filtration for High-Energy Phase-Contrast X-Ray Imaging - Riess, Christian; Mohamed, Ashraf; Hinshaw, Waldo; Fahrig, Rebecca (Medical Imaging 2015: Physics of Medical Imaging, 2015)

Online improvement of the reliability of PRF based temperature maps displayed during laser-induced thermotherapy of liver lesions - Rothgang, Eva; Kickhefel, Antje; Roland, Jörg; Rosenberg, Christian; Hornegger, Joachim; Lorenz, Christine H. (Magnetic Resonance Materials in Physics Biology and Medicine, 2009)

On Modeling Glottal Source Information for Phonation Assessment in Parkinson's Disease - Vasquez Correa, Juan; Fritsch, Julian; Orozco Arroyave, Juan Rafael; Nöth, Elmar; Magimai-Doss, Mathew (INTERSPEECH 2021, 2021)
CRIS-Team WoS Importer:2022-09-30

On rotation invariance in copy-move forgery detection - Christlein, Vincent; Riess, Christian; Angelopoulou, Elli (2010 IEEE International Workshop on Information Forensics and Security, 2010)

On Sweat Analysis for Quantitative Estimation of Dehydration during Physical Exercise - Ring, Matthias; Lohmüller, Clemens; Rauh, Manfred; Eskofier, Björn (Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015)
Quantitative estimation of water loss during physical exercise is of importance because dehydration can impair both muscular strength and aerobic endurance. A physiological indicator for deficit of total body water (TBW) might be the concentration of electrolytes in sweat. It has been shown that concentrations differ after physical exercise depending on whether water loss was replaced by fluid intake or not. However, to the best of our knowledge, this fact has not been examined for its potential to quantitatively estimate TBW loss. Therefore, we conducted a study in which sweat samples were collected continuously during two hours of physical exercise without fluid intake. A statistical analysis of these sweat samples revealed significant correlations between chloride concentration in sweat and TBW loss (r = 0.41, p < 0.01), and between sweat osmolality and TBW loss (r = 0.43, p < 0.01). A quantitative estimation of TBW loss resulted in a mean absolute error of 0.49 l per estimation. Although the precision has to be improved for practical applications, the present results suggest that TBW loss estimation could be realizable using sweat samples.

On the Accuracy of a Video-Based Drill-Guidance Solution for Orthopedic and Trauma Surgery: Preliminary Results - Magaraggia, Jessica; Kleinszig, Gerhard; Wei, Wei; Weiten, Markus; Graumann, Rainer; Angelopoulou, Elli; Hornegger, Joachim (SPIE Medical Imaging 2014, 2014)
Over the last years, several methods have been proposed to guide the physician during reduction and fixation of bone fractures. Available solutions often use bulky instrumentation inside the operating room (OR). The latter ones usually consist of a stereo camera, placed outside the operative field, and optical markers directly attached to both the patient and the surgical instrumentation, held by the surgeon. Recently proposed techniques try to reduce the required additional instrumentation as well as the radiation exposure to both patient and physician. In this paper, we present the adaptation and the first implementation of our recently proposed video camera-based solution for screw fixation guidance. Based on the simulations conducted in our previous work, we mounted a small camera on a drill in order to recover its tip position and axis orientation w.r.t our custom-made drill sleeve with attached markers. Since drill-position accuracy is critical, we thoroughly evaluated the accuracy of our implementation. We used an optical tracking system for ground truth data collection. For this purpose, we built a custom plate reference system and attached reflective markers to both the instrument and the plate. Free drilling was then performed 19 times. The position of the drill axis was continuously recovered using both our video camera solution and the tracking system for comparison. The recorded data covered targeting, perforation of the surface bone by the drill bit and bone drilling. The orientation of the instrument axis and the position of the instrument tip were recovered with an accuracy of 1:60 ± 1:22° and 2:03 ± 1:36 mm respectively. © 2014 SPIE.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.onthea_7
Keywords: Video-guided method; Real-time guidance; Orthopedic and Trauma Surgery; Drill Sleeve

On the Automatic Classification of Reading Disorders - Maier, Andreas; Schafflhuber, Caroline; Bocklet, Tobias; Hönig, Florian Thomas; Kratz, Oliver; Horndasch, Stefanie; Nöth, Elmar; Moll, Gunther (Pattern Recognition in Information Systems, 2009)

On the Characteristics of Helical 3-D X-ray Dark-field Imaging - Felsner, Lina; Hu, Shiyang; Ludwig, Veronika; Anton, Gisela; Maier, Andreas; Rieß, Christian (Bildverarbeitung für die Medizin (BVM 2019), 2019)

On the energetics of conformational switching of molecules at and close to room temperature - Ditze, Stefanie; Stark, Michael Werner; Buchner, Florian; Aichert, André; Jux, Norbert; Luckas, Nicola; Görling, Andreas; Hieringer, Wolfgang; Hornegger, Joachim; Steinrück, Hans-Peter; Marbach, Hubertus (Journal of the American Chemical Society, 2014)
We observe and induce conformational switching of individual molecules via scanning tunneling microscopy (STM) at and close to room temperature. 2H-5,10,15,20-Tetrakis-(3,5-di-tert-butyl)-phenylporphyrin adsorbed on Cu(111) forms a peculiar supramolecular ordered phase in which the molecules arrange in alternating rows, with two distinct appearances in STM which are assigned to concave and convex intramolecular conformations. Around room temperature, frequent bidirectional conformational switching of individual molecules from concave to convex and vice versa is observed. From the temperature dependence, detailed insights into the energy barriers and entropic contributions of the switching processes are deduced. At 200 K, controlled STM tip-induced unidirectional switching is possible, yielding an information storage density of 4.9 × 10 bit/inch. With this contribution we demonstrate that controlled switching of individual molecules at comparably high temperatures is possible and that entropic effects can be a decisive factor in potential molecular devices at these temperatures. © 2014 American Chemical Society.

On-the-fly detection of images with gastritis aspects in magnetically guided capsule endoscopy - Mewes, Philip; Neumann, Dominik; Juloski, Aleksandar Lj; Angelopoulou, Elli; Hornegger, Joachim (Medical Imaging 2011: Computer-Aided Diagnosis, 2011)
Capsule Endoscopy (CE) was introduced in 2000 and has since become an established diagnostic procedure for the small bowel, colon and esophagus. For the CE examination the patient swallows the capsule, which then travels through the gastrointestinal tract under the influence of the peristaltic movements. CE is not indicated for stomach examination, as the capsule movements can not be controlled from the outside and the entire surface of the stomach can not be reliably covered. Magnetically-guided capsule endoscopy (MGCE) was introduced in 2010. For the MGCE procedure the stomach is filled with water and the capsule is navigated from the outside using an external magnetic field. During the examination the operator can control the motion of the capsule in order to obtain a sufficient number of stomach-surface images with diagnostic value. The quality of the examination depends on the skill of the operator and his ability to detect aspects of interest in real time. We present a novel computer-assisted diagnostic-procedure (CADP) algorithm for indicating gastritis pathologies in the stomach during the examination. Our algorithm is based on pre-processing methods and feature vectors that are suitably chosen for the challenges of the MGCE imaging (suspended particles, bubbles, lighting). An image is classified using an ada-boost trained classifier. For the classifier training, a number of possible features were investigated. Statistical evaluation was conducted to identify relevant features with discriminative potential. The proposed algorithm was tested on 12 video sequences stemming from 6 volunteers. A mean detection rate of 91.17% was achieved during leave-one out cross-validation. © 2011 SPIE.

On-the-fly-Reconstruction in Exact Cone-Beam CT using the Cell Broadband Engine Architecture - Scherl, Holger; Hoppe, Stefan; Dennerlein, Frank; Lauritsch, Günter; Eckert, Wieland; Kowarschik, Markus; Hornegger, Joachim (Proceedings Fully3D Meeting and HPIR Workshop, 2007)

On the Impact of Children's Emotional Speech on Acoustic and Language Models - Steidl, Stefan; Batliner, Anton; Seppi, Dino; Schuller, Björn (EURASIP Journal on Audio, Speech, and Music Processing, 2010)
The automatic recognition of children's speech is well known to be a challenge, and so is the influence of affect that is believed to downgrade performance of a speech recogniser. In this contribution, we investigate the combination of both phenomena. Extensive test runs are carried out for 1 k vocabulary continuous speech recognition on spontaneous motherese, emphatic, and angry children's speech as opposed to neutral speech. The experiments address the question how specific emotions influence word accuracy. In a first scenario, "emotional" speech recognisers are compared to a speech recogniser trained on neutral speech only. For this comparison, equal amounts of training data are used for each emotion-related state. In a second scenario, a "neutral" speech recogniser trained on large amounts of neutral speech is adapted by adding only some emotionally coloured data in the training process. The results show that emphatic and angry speech is recognised best—even better than neutral speech—and that the performance can be improved further by adaptation of the acoustic and linguistic models. In order to show the variability of emotional speech, we visualise the distribution of the four emotion-related states in the MFCC space by applying a Sammon transformation.

On the Influence of Acquisition Angle and Slice Thickness on the in-plane Spatial Resolution of Calcifications in Digital Breast Tomosynthesis - Luckner, Christoph; Schebesch, Frank; Syben-Leisner, Christopher; Mertelmeier, Thomas; Maier, Andreas; Ritschl, Ludwig (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.onthei_5

On the Use of Prosody in Automatic Dialogue Understanding - Nöth, Elmar; Batliner, Anton; Warnke, Volker; Haas, Jürgen; Boros, Manuela; Buckow, Jan-Constantin; Huber, Richard; Gallwitz, Florian; Nutt, Matthias; Niemann, Heinrich (Proc. ESCA Workshop on Dialogue and Prosody, 1999)

On the Value of the Non-negativity Constraint in CT - Haase, Viktor; Hahn, Katharina; Schoendube, Harald; Stierstorfer, Karl; Maier, Andreas; Noo, Frédéric (2019 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC), 2019)
Model based iterative reconstruction (MBIR) has attracted a lot of attention in X-ray computed tomography (CT). A strength of MBIR over classical filtered backprojection is its ability to apply constraints over the voxel values, which can be critical to improve image quality. Given that the linear attenuation coefficient of X-rays is non-negative, applying a non-negativity constraint appears very natural. Indeed, most MBIR-related publications in CT invoke it. However, there is little to no information in the literature on the intrinsic value of the non-negativity constraint. In this work, we shed light on this question in the context of two challenging imaging scenarios: (i) heavy truncation, (ii) photon starvation due to a metal implant. Real CT data sets are used, and the effect of the constraint is examined in terms of image similarity and closeness to a preferred ground truth. Additionally, convergence properties are examined. The reconstruction is performed using a provably converging algorithm applied with a large number of iterations to nearly reach convergence, and is also performed using ordered subsets to obtain a result in a manner that is more practical for clinical routine applications. Our results show that the non-negativity constraint can be both beneficial and detrimental depending on the imaging scenario. However, the observed differences tend to be much smaller than the overall level of inaccuracy in the image. We also find that the non-negativity constraint can prevent divergence when using ordered subsets, but this gain does not translate into a satisfactory reconstruction. Altogether, we conclude that strong value for the non-negativity constraint is difficult to demonstrate. This constraint could thus be discarded in favor of other constraints or utilization of algorithms that cannot handle it.<br

OpenCL: A viable solution for high-performance medical image reconstruction? - Siegl, Christian; Hofmann, Hannes; Keck, Benjamin; Prümmer, Marcus; Hornegger, Joachim (Medical Imaging 2011: Physics of Medical Imaging, 2011)
Reconstruction of 3-D volumetric data from C-arm CT projections is a computationally demanding task. For interventional image reconstruction, hardware optimization is mandatory. Manufacturers of medical equipment use a variety of high-performance computing (HPC) platforms, like FPGAs, graphics cards, or multi-core CPUs. A problem of this diversity is that many different frameworks and (vendor-specific) programming languages are used. Furthermore, it is costly to switch the platform, since the code has to be re-written, verified, and optimized. OpenCL, a relatively new industry standard for HPC, promises to enable portable code. Its key idea is to abstract hardware in a way that allows an efficient mapping onto real CPUs, GPUs, and other hardware. The code is compiled for the actual target by the device driver. In this work we investigated the suitability of OpenCL as a tool to write portable code that runs efficiently across different hardware. The problems chosen are back- and forward-projection, the most time-consuming parts of (iterative) reconstruction. We present results on three platforms, a multi-core CPU system and two GPUs, and compare them against manually optimized native implementations. We found that OpenCL allows to share a common framework in one language across platforms. However, considering differences in the underlying architecture, a hardware-oblivious implementation cannot be expected to deliver maximal performance. By optimizing the OpenCL code for the specific hardware we reached over 90% of native performance for both problems, back- and forward-projection, on all platforms. ̈ 2011 SPIE.

Opening Windows - Increasing Window Size in Motion-Compensated ECG-gated Cardiac Vasculature Reconstruction - Schwemmer, Chris; Rohkohl, Christopher; Lauritsch, Günter; Müller, Kerstin; Hornegger, Joachim (Proceedings of the 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013)

Open-source 4D Statistical Shape Model of the Heart for X-ray Projection Imaging - Unberath, Mathias; Maier, Andreas; Fleischmann, Dominik; Hornegger, Joachim; Fahrig, Rebecca (Proceedings of the ISBI, 2015)
We present an open-source 4D (3D+t) statistical shape model of the heart developed as numerical phantom for cone-beam CT simulation. The training set consists of surface meshes from 20 ten-phase-CT angiography data sets extracted using automatic registration-based segmentation. Incorporating 90% of the training set variation, the model exhibits a generalization ability of 5.00 ± 0.93 mm and specificity of 7.30 ± 0.97 mm. The model is implemented in CONRAD, an open-source simulation and reconstruction framework. We provide all algorithms and high resolution projection data on the project's homepage. To the best of our knowledge, this is the first open-source 4D statistical shape model of the heart.
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.openso
Keywords: cone-beam; CT; numerical phantom; open-source; Simulation; XCAT

Open source simulation of fixational eye drift motion in oct scans: Towards better comparability and accuracy in retrospective OCT motion correction - Nau, Merlin A.; Nau, Merlin; Moult, Eric M.; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2020)
Point-wise scanning modalities like Optical Coherence Tomography (OCT) or Scanning Laser Ophthalmoscopy suffer from distortions due to the perpetual motion of the eye. While various motion correction approaches have been proposed, the absence of ground truth displacements or images entails a lack of accurate and comparable evaluations. The purpose of this paper is to close this gap by initiating an open source framework for the simulation of realistic eye motion and corresponding artificial distortion of scans, thereby for the first time enabling the community to a) create datasets with accessible ground truth and b) compare the correction of identical motion patterns in data acquired with different scanners or scan patterns. This paper extends previous work on simulation of fixational eye drift via a self-avoiding random walk in a potential to a continuous domain in time and space, allowing the derivation of smooth displacement fields. The model is demonstrated by presenting an examplary motion path, whose properties resemble reported properties of recordings in current literature on fixational eye motion. Furthermore, the artificial distortion of scans is demonstrated by showing a correspondingly distorted image of a virtual raster scan modeled according to the properties of an existing OCT scanner. All experiments can be reproduced and adapted to arbitrary scanner- and raster scan pattern-properties in the publicly available framework. Beyond that, the open source code provides a starting point for the community to integrate extensions like saccadic or axial eye motion.
CRIS-Team Scopus Importer:2020-04-21

Optical coherence tomography angiography of optic nerve head and parafovea in multiple sclerosis - Wang, Xiaogang; Jia, Yali; Spain, Rebecca; Potsaid, Benjamin; Liu, Jonathan J.; Baumann, Bernhard; Hornegger, Joachim; Fujimoto, James G.; Wu, Qiang; Huang, David (British Journal of Ophthalmology, 2014)
Conclusions Based on OCT angiography, the FI measurement is feasible, highly repeatable and reproducible, and it is suitable for clinical measurement of ONH and parafoveal perfusion. The ONH FI may be useful in detecting damage from ON and quantifying its severity.

Optical Tracking of Respiratory Motion Using a Time of Flight Camera and its Applicability in Emission Tomography - Schuhmann, Peter; Penne, Jochen; Schaller, Christian; Zeintl, Johannes; Hornegger, Joachim; Kuwert, Torsten (NuklearMedizin 2008, 2008)

Optimal Channel Selection for Respiratory Self-Gating Signals - Grimm, Robert; Bauer, Simon; Kiefer, Berthold; Hornegger, Joachim; Block, Kai Tobias (Proceedings of International Society for Magnetic Resonance in Medicine, 2013)

Optimal Linear Feature Transformations for Semi-Continuous Hidden Markov Models - Schukat-Talamazzini, Günter; Hornegger, Joachim; Niemann, Heinrich (Proc. Int. Conf. on Acoustics, Speech and Signal Processing, 1995)

Optimierte Segmentierung der Papille in HRT-Retinaaufnahmen - Arold, Oliver; Bock, Rüdiger; Meier, Jörg; Michelson, Georg; Hornegger, Joachim (Bildverarbeitung für die Medizin 2008 - Algorithmen, Systeme, Anwendungen, 2008)

Optimisation of Image Reconstruction for Phase-Contrast X-Ray Talbot-Lau Imaging with regard to Mechanical Robustness - Seifert, Maria; Käppler, Sebastian; Hauke, Christian; Horn, Florian; Pelzer, Georg; Rieger, Jens; Michel, Thilo; Riess, Christian; Anton, Gisela (Physics in Medicine and Biology, 2016)
Keywords: phase-contrast; reference phase; image reconstruction; PCA; x-ray imaging; robustness; TalbotLau

Optimization-based Extrapolation for Truncation Correction - Maier, Andreas; Scholz, Bernhard; Dennerlein, Frank (Proceedings of the second international conference on image formation in x-ray computed tomography, 2012)

Optimization of Flow-Shop Problems using Evolutionary Algorithms - A Case Study - Wilke, Peter; Zobel, Matthias (Proc. European Conference for Intelligent Technologies (EUFIT'97), 1997)

Optimization Problems in Statistical Object Recognition - Hornegger, Joachim; Niemann, Heinrich (Proceedings on the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 1997)

Optimized viewing angles for cardiac electrophysiology ablation procedures - Koch, Martin; Hoffmann, Matthias; Pfister, Marcus; Hornegger, Joachim; Strobel, Norbert (International Journal of Computer Assisted Radiology and Surgery, 2014)
Purpose Catheter ablation is a common treatment option for atrial fibrillation (AF). Interventional C-arm X-ray systems are used for guiding AF procedures, employing standard view positions. Since the projection angles are not adapted to the individual patient anatomy, standard projections do not necessarily offer the best views of important anatomical structures. Using a pre-procedural 3D data set acquired with MRI or CT, suitable ablation sites (lines) can be identified in advance so an ablation plan can be superimposed on fluoroscopic images to guide the procedure. Methods A method was developed to estimate optimized projection views for biplane X-ray C-arm systems based on planning data for AF ablation procedures. The estimated viewing angles were compared to standard angulations using an objective quality metric, the length of the planned ablation line as seen under X-ray. This method was tested using 35 clinical datasets annotated with planned ablation lines for ipsilateral pulmonary vein isolation. Results The optimized views computed using the new method yielded 28 % less foreshortening of pre-planned ablation lines on average. In one case, anatomy-based view calculation lead to a 69 % reduction in foreshortening. Conclusion The commonly used standard views provide reasonable a priori choices, and some improvement is possible by switching among common angulations depending on the treatment region. Further gains are possible by using anatomy-optimized biplane C-arm angulations. © 2014 CARS.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.optimi_1
Keywords: Electrophysiology; Fluoroscopic projection; Pulmonary vein isolation; Treatment planning

Oral-diadochokinesis rates for Spanish, German and Czech: reference values for normotypical adults Diadococinesia oral para el español, el alemán y el checo: valores de referencia para adultos normotípicos - Camargo-Mendoza, Maryluz; Nöth, Elmar; Mekyska, Jiri; Orozco-Arroyave, Juan Rafael (Revista de Investigación en Logopedia, 2023)
Oral diadochokinesis (oral-DDK) is one of the common maximum performance tasks used in clinical practice to evaluate the oral motor mechanism. Although there are reference values for some languages, there are no recent publications of the adult population speaking Spanish, German or Czech. The aim of the study is to describe the reference values of oral-DDK in adult speakers of Spanish, German and Czech, 2) to determine the influence of gender on the values of oral-DDK, 3) to determine the influence of age on the values of oral-DDK and, 4) to compare the inter-language values of the oral-DDK. Recordings taken from three previous studies of 189 healthy adults were considered (between 26 to 86 years), 94 females and 95 males, while producing the sequential motion rate (SMR) /pa-ta-ka/. Count-by-time method was used using acoustic analysis software. Oral-DDK averages were established for each language and the effect of gender and age was determined. The variability of the measure among languages was also established. Reference data were obtained for each language. Age did not show any significant effect on oral-DDK despite its decline as age advanced; there was no gender effect on the oral-DDK, and the comparison of the oral-DDK among languages showed statistically significant differences. This study determined the reference data of the oral-DDK for Spanish, German and Czech and found significant differences among the three languages and no significant effects of age or gender on the measure studied.
CRIS-Team Scopus Importer:2023-11-03
Keywords: adults; Spanish; Czech; German; normative data; Oral-diadochokinesis

Oral squamous cell carcinoma of the tongue: Prospective and objective speech evaluation of patients undergoing surgical therapy - Riemann, Max; Knipfer, Christian; Rohde, Maximilian; Adler, Werner; Schuster, Maria; Nöth, Elmar; Oetter, Nicolai; Shams, Nima; Neukam, Friedrich Wilhelm; Stelzle, Florian (Head and Neck-Journal For the Sciences and Specialties of the Head and Neck, 2014)
Prospective speech intelligibility assessments lack objectivity in patients undergoing surgery for oral squamous cell carcinoma (OSCC) of the tongue.Speech intelligibility was measured based on word recognition by means of an automatic and objective speech recognition system preoperatively, and 14 to 20 days, and 3 months, 6 months, and 12 months postoperatively. The study comprised 25 patients with OSCC of the tongue and a healthy control group (n = 40).Patients yielded significant speech impairments compared to the healthy control group both before surgery and after 12 months (p <= .002). The speech intelligibility of the patients decreased significantly 14 to 20 days after surgery (p < .001) but realigned to preoperative values after 12 months (p = .159). Preservation of the tip of the tongue resulted in significantly higher word recognition after 12 months (p = .007; ?word recognition = 16.29).Having OSSC of the tongue results in a significant impairment of speech intelligibility. The preservation of the tip of the tongue seems to be a central factor concerning the recovery of speech. © 2015 Wiley Periodicals, Inc. Head Neck, 2015.
EVALuna2:5479

ORCA-CLEAN: A Deep Denoising Toolkit for Killer Whale Communication - Bergler, Christian; Schmitt, Manuel; Maier, Andreas; Smeele, Simeon; Barth, Volker; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020, 2020)
Keywords: Killer Whale; Denoising; Call Type; Deep Learning; Orca

ORCA-PARTY: An Automatic Killer Whale Sound Type Separation Toolkit Using Deep Learning - Bergler, Christian; Schmitt, Manuel; Maier, Andreas; Cheng, Rachael Xi; Barth, Volker; Nöth, Elmar (ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022)
Data-driven and machine-based analysis of massive bioacoustic data collections, in particular acoustic regions containing a substantial number of vocalizations events, is essential and extremely valuable to identify recurring vocal paradigms. However, these acoustic sections are usually characterized by a strong incidence of overlapping vocalization events, a major problem severely affecting subsequent human-/machine- based analysis and interpretation. Robust machine-driven signal separation of species-specific call types is extremely challenging due to missing ground truth data, speaker/source- relevant information, limited knowledge about inter- and intra-call type variations, next to diverse recording conditions. The current study is the first introducing a fully- automated deep signal separation approach for overlapping orca vocalizations, addressing all of the previously mentioned challenges, together with one of the largest bioacoustic data archives recorded on killer whales (Orcinus Orca). Incorporating ORCA-PARTY as additional data enhancement step for downstream call type classification demonstrated to be extremely valuable. Besides the proof of cross-domain applicability and consistently promising results on non-overlapping signals, significant improvements were achieved when processing acoustic orca segments comprising a multitude of vocal activities. Apart from auspicious visual inspections, a final numerical evaluation on an unseen dataset proved that about 30 % more known sound patterns could be identified.<br
Keywords: Killer Whale; Deep Learning; Call Type

ORCA-SLANG: An Automatic Multi-Stage Semi-Supervised Deep Learning Framework for Large-Scale Killer Whale Call Type Identification - Bergler, Christian; Schmitt, Manuel; Maier, Andreas; Symonds, Helena; Spong, Paul; Ness, Steven; Tzanetakis, George; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2021, 2021)
Keywords: Killer Whale; Deep Learning; Call Type

ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning - Bergler, Christian; Schröter, Hendrik; Cheng, Rachael Xi; Barth, Volker; Weber, Michael; Nöth, Elmar; Hofer, Heribert; Maier, Andreas (Scientific Reports, 2019)
Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species.
CRIS-Team Scopus Importer:2019-08-06

ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated deep learning-based segmentation - Hauer, Christopher; Nöth, Elmar; Barnhill, Alexander; Maier, Andreas; Guthunz, Julius; Hofer, Heribert; Cheng, Rachael Xi; Barth, Volker; Bergler, Christian (Scientific Reports, 2023)
Acoustic identification of vocalizing individuals opens up new and deeper insights into animal communications, such as individual-/group-specific dialects, turn-taking events, and dialogs. However, establishing an association between an individual animal and its emitted signal is usually non-trivial, especially for animals underwater. Consequently, a collection of marine species-, array-, and position-specific ground truth localization data is extremely challenging, which strongly limits possibilities to evaluate localization methods beforehand or at all. This study presents ORCA-SPY, a fully-automated sound source simulation, classification and localization framework for passive killer whale (Orcinus orca) acoustic monitoring that is embedded into PAMGuard, a widely used bioacoustic software toolkit. ORCA-SPY enables array- and position-specific multichannel audio stream generation to simulate real-world ground truth killer whale localization data and provides a hybrid sound source identification approach integrating ANIMAL-SPOT, a state-of-the-art deep learning-based orca detection network, followed by downstream Time-Difference-Of-Arrival localization. ORCA-SPY was evaluated on simulated multichannel underwater audio streams including various killer whale vocalization events within a large-scale experimental setup benefiting from previous real-world fieldwork experience. Across all 58,320 embedded vocalizing killer whale events, subject to various hydrophone array geometries, call types, distances, and noise conditions responsible for a signal-to-noise ratio varying from - 14.2 dB to 3 dB, a detection rate of 94.0 % was achieved with an average localization error of 7.01 ∘ . ORCA-SPY was field-tested on Lake Stechlin in Brandenburg Germany under laboratory conditions with a focus on localization. During the field test, 3889 localization events were observed with an average error of 29.19 ∘ and a median error of 17.54 ∘ . ORCA-SPY was deployed successfully during the DeepAL fieldwork 2022 expedition (DLFW22) in Northern British Columbia, with a mean average error of 20.01 ∘ and a median error of 11.01 ∘ across 503 localization events. ORCA-SPY is an open-source and publicly available software framework, which can be adapted to various recording conditions as well as animal species.
CRIS-Team Scopus Importer:2023-07-21

ORCA-SPY: Killer Whale Sound Source Simulation and Detection, Classification and Localization in PAMGuard Utilizing Integrated Deep Learning Based Segmentation - Hauer, Christopher; Nöth, Elmar; Barnhill, Alexander; Maier, Andreas; Guthunz, Julius; Hofer, Heribert; Cheng, Rachael Xi; Barth, Volker; Bergler, Christian (Scientific Reports, 2023)
Acoustic identification of vocalizing individuals opens up new and deeper insights into animal communications, such as individual-/group-specific dialects, turn-taking events, and dialogs. However, establishing an association between an individual animal and its emitted signal is usually non-trivial, especially for animals underwater. Consequently, a collection of marine species-, array-, and position-specific ground truth localization data is extremely challenging, which strongly limits possibilities to evaluate localization methods beforehand or at all. This study presents ORCA-SPY, a fully-automated sound source simulation, classification and localization framework for passive killer whale (Orcinus orca) acoustic monitoring that is embedded into PAMGuard, a widely used bioacoustic software toolkit. ORCA-SPY enables array- and position-specific multichannel audio stream generation to simulate real-world ground truth killer whale localization data and provides a hybrid sound source identification approach integrating ANIMAL-SPOT, a state-of-the-art deep learning-based orca detection network, followed by downstream Time-Difference-Of-Arrival (TDOA) localization. ORCA-SPY was evaluated on simulated multichannel underwater audio streams including various killer whale vocalization events within a large-scale experimental setup benefiting from previous real-world fieldwork experience. Across all 58,320 embedded vocalizing killer whale events, subject to various hydrophone array geometries, call types, distances, and noise conditions responsible for a signal-to-noise ratio varying from -14.2dB to 3dB, a detection rate of 94.0 % was achieved with an average localization error of 7.01°. ORCA-SPY was field-tested on the lake Stechlin under laboratory conditions with a focus on localization. During the field test, 3889 localization events were observed with an average error of 29.19° and a median error of 17.54°. ORCA-SPY was deployed successfully at the DeepAL fieldwork 2022 expedition (DLFW22) in Northern British Columbia, with a mean average error of 20.01° and a median error of 11.01° across 503 localization events. ORCA-SPY is an open-source and publicly available software framework, which can be adapted to various recording conditions as well as animal species.

ORCA-WHISPER: An Automatic Killer Whale Sound Type Generation Toolkit Using Deep Learning - Bergler, Christian; Barnhill, Alexander; Perrin, Dominik; Schmitt, Manuel; Maier, Andreas; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2022, 2022)
Keywords: Killer Whale; Deep Learning; Call Type; Generative Adversarial Networks

Orientation of endoscopic images: Rectification by gravity - Höller, Kurt Emmerich; Schneider, Armin; Jahn, Jasper; Gutierrez, Javier; Wittenberg, Thomas; Meining, Alexander; Von Delius, Stefan; Hornegger, Joachim; Feussner, Hubertus (Biomedizinische Technik, 2010)
A known problem in endoscopic surgery (especially with flexible video endoscopes) is the absence of a stable horizon in endoscopic images displayed on a monitor. With our "ENDOrientation" approach, image rectification, even in non-rigid endoscopic surgery (particularly NOTES), can be realized with a tiny MEMS tri-axial inertial sensor placed on the tip of an endoscope. This sensor measures the impact of gravity on each of the three orthogonal accelerometer axes in real time. After an initial calibration and temporal filtering of these three data steams, the rotation angle of an endoscope can be estimated directly. The achievable sampling rate of the inertial sensor is above the usual endoscopic video frame rate of 25 Hz; the rotation accuracy is approximately one degree. The image rectification can be performed in real time by digitally rotating the endoscopic video signal. Improvements and benefits have been evaluated in animal studies: coordination and movement of different instruments was rated to be much more intuitive with a stable horizon on endoscopic images. The recorded time stamps and position tracks clearly support this observation. © 2010 by Walter de Gruyter.

Outlier Detection for Multi-Sensor Super-Resolution in Hybrid 3-D Endoscopy - Köhler, Thomas; Haase, Sven; Bauer, Sebastian; Wasza, Jakob; Kilgus, Thomas; Maier-Hein, Lena; Feußner, Hubertus; Hornegger, Joachim (Bildverarbeitung für die Medizin 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.outlie_5

Overexposure Correction by Mixed One-bit Compressive Sensing for C-Arm CT - Huang, Xiaolin; Xia, Yan; Huang, Yixing; Hornegger, Joachim; Maier, Andreas (Bildverarbeitung für die Medizin: Algorithmen-Systeme-Anwendungen, 2017)
This paper proposes a novel method to deal with overexposure for C-arm CT reconstruction. The proposed method is based on recent progress of one bit compressive sensing (1bit-CS), which is to recover sparse signals from sign measurements. Overexposure could be regarded as a kind of sign information, thus the application of 1bit-CS to overexposure correction in CT reconstruction is expected. This method is evaluated on a phantom and its promising performance implies potential application on clinical data.
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.overex
Keywords: Overexposure; Mixed One-bit; Compressive Sensing