Over-Exposure Correction in CT Using Optimization-Based Multiple Cylinder Fitting - Preuhs, Alexander; Berger, Martin; Xia, Yan; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.overex_3
Keywords: GRK-1773

Palmprint Identification for User Verification based on Line Detection and Local Standard Deviation - Prakoso, Bagas Sakamulia; Timotius, Ivanna; Setyawan, Iwan (The 1st International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE 2014), 2014)
Human palmprints contain biometric features that can be used to identify an individual. These features can be used for example in user verification applications. This paper presents a user verification system using palmprint identification. The image of the palm is captured using a web camera. Then the features used for palmprint identification is extracted using line detection and local standard deviation. The proposed system is evaluated by asking 40 subjects to act as users (10 subject as registered users and 30 non-registered users). Our experiments show that the system can achieve accuracy rate of up to 98% with no false acceptance and 2% false rejection rate. The average time required to perform a user verification is 340 ms.
Keywords: palmprint identification; line detection; local standard deviation; user verification.

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset - Wilm, Frauke; Fragoso, Marco; Marzahl, Christian; Qiu, Jingna; Puget, Chloe; Diehl, Laura; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas; Breininger, Katharina; Aubreville, Marc (Scientific Data, 2022)
Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.
CRIS-Team Scopus Importer:2022-10-14

Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry - Wilm, Frauke; Ihling, Christian; Méhes, Gábor; Terracciano, Luigi; Puget, Chloé; Klopfleisch, Robert; Schüffler, Peter; Aubreville, Marc; Maier, Andreas; Mrowiec, Thomas; Breininger, Katharina (Journal of Pathology Informatics, 2023)
The success of immuno-oncology treatments promises long-term cancer remission for an increasing number of patients. The response to checkpoint inhibitor drugs has shown a correlation with the presence of immune cells in the tumor and tumor microenvironment. An in-depth understanding of the spatial localization of immune cells is therefore critical for understanding the tumor's immune landscape and predicting drug response. Computer-aided systems are well suited for efficiently quantifying immune cells in their spatial context. Conventional image analysis approaches are often based on color features and therefore require a high level of manual interaction. More robust image analysis methods based on deep learning are expected to decrease this reliance on human interaction and improve the reproducibility of immune cell scoring. However, these methods require sufficient training data and previous work has reported low robustness of these algorithms when they are tested on out-of-distribution data from different pathology labs or samples from different organs. In this work, we used a new image analysis pipeline to explicitly evaluate the robustness of marker-labeled lymphocyte quantification algorithms depending on the number of training samples before and after being transferred to a new tumor indication. For these experiments, we adapted the RetinaNet architecture for the task of T-lymphocyte detection and employed transfer learning to bridge the domain gap between tumor indications and reduce the annotation costs for unseen domains. On our test set, we achieved human-level performance for almost all tumor indications with an average precision of 0.74 in-domain and 0.72–0.74 cross-domain. From our results, we derive recommendations for model development regarding annotation extent, training sample selection, and label extraction for the development of robust algorithms for immune cell scoring. By extending the task of marker-labeled lymphocyte quantification to a multi-class detection task, the pre-requisite for subsequent analyses, e.g., distinguishing lymphocytes in the tumor stroma from tumor-infiltrating lymphocytes, is met.
CRIS-Team Scopus Importer:2023-07-21
Keywords: Deep learning; Domain adaptation; Immuno-oncology; Immunohistochemistry; Transfer learning; Tumor-infiltrating lymphocytes

Papoulis-Gerchberg Algorithms for Limited Angle Tomography Using Data Consistency Conditions - Huang, Yixing; Taubmann, Oliver; Huang, Xiaolin; Lauritsch, Günter; Maier, Andreas (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography, 2018)
The Papoulis-Gerchberg (P-G) algorithm is widely used for extrapolation of band-limited signals. It is applicable to limited angle tomography as well since typical imaged objects in computed tomography have a limited spatial extent, which means that the Fourier transforms of the objects can be considered band-limited signals. In computed tomography, some other bandlimitation properties have been discovered as well, which are referred to as data consistency conditions. For example, the Fourier transform of a parallel-beam sinogram has an empty double-wedge region. The Chebyshev-Fourier transform of a parallel-beam sinogram only has nonzero values inside a wedge region and these values form a checkerboard pattern, which is Helgason-Ludwig consistency condition. In this paper, we propose two P-G algorithms to restore missing data in limited angle tomography using the above two consistency conditions. Numerical experiments on the Shepp-Logan phantom demonstrate that they can reduce streaks better than the conventional P-G algorithm. <br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.papoul_8
Keywords: limited angle tomography; Papoulis-Gerchberg; artifact reduction; data consistency

Paralinguistics in speech and language - State-of-the-art and the challenge - Schuller, Björn; Steidl, Stefan; Batliner, Anton; Burkhardt, Felix; Devillers, Laurence; Müller, Christian; Narayanan, Shrikanth (Computer Speech and Language, 2013)
Paralinguistic analysis is increasingly turning into a mainstream topic in speech and language processing. This article aims to provide a broad overview of the constantly growing field by defining the field, introducing typical applications, presenting exemplary resources, and sharing a unified view of the chain of processing. It then presents the first broader Paralinguistic Challenge organised at INTERSPEECH 2010 by the authors including a historical overview of the Challenge tasks of recognising age, gender, and affect, a summary of methods used by the participants, and their results. In addition, we present the new benchmark obtained by fusion of participants’ predictions and conclude by discussing ten recent and emerging trends in the analysis of paralinguistics in speech and language.
Keywords: paralinguistics; age; gender; affect; survey; trends; challenge

Parallel Representation Learning for the Classification of Pathological Speech: Studies on Parkinson's Disease and Cleft Lip and Palate - Vasquez Correa, Juan; Arias Vergara, Tomás; Schuster, M.; Orozco Arroyave, Juan Rafael; Nöth, Elmar (Speech Communication, 2020)
Speech signals may contain different paralinguistic aspects such as the presence of pathologies that affect the proper communication capabilities of a speaker. Those speech disorders have different origin depending on the type of the disease. For instance, diseases with morphological origin such as cleft lip and palate that causes hypernasality, or with neurodegenerative origin such as Parkinson's disease that generates hypokinetic dysarthria on the patients. Automatic assessment of pathological speech allows to support the diagnosis and/or the evaluation of the disease severity. Conventional methods are based on the manually applied assessment of single features such as jitter, shimmer, or formant frequencies that may not completely model all of the phenomena that appear due to the disease. This paper introduces a novel strategy based on unsupervised representation learning for automatic detection of pathological speech. The proposed approach is based on the use of recurrent and convolutional autoencoders trained to extract informative features to characterize the presence of pathologies in speech. A novel feature set based on the reconstruction error of the autoencoders is also proposed. The performance of the introduced models is evaluated classifying pathological speech signals recorded from people suffering from Parkinson's disease, and children with cleft lip and palate. All participants from this study were Spanish native speakers. The proposed models are accurate to classify the speech signals of both kinds of diseases, with an accuracy of up to 97% for cleft lip and palate, and up to 84% for the case of Parkinson's disease. We also show that the reconstruction error from the autoencoders in different frequency regions contain information related to specific speech symptoms of both diseases.
CRIS-Team Scopus Importer:2020-08-14
Keywords: Cleft lip and palate; Convolutional autoencoders; Pathological speech; Recurrent autoencoders Parkinson's disease; Unsupervised representation learning

Parallel-Shift Tomosynthesis for Orthopedic Applications - Luckner, Christoph; Sesselmann, Stefan; Mertelmeier, Thomas; Maier, Andreas; Ritschl, Ludwig (Proc. SPIE, 2018)
The upsurge in interest of digital tomosynthesis is mainly caused by breast imaging; however, it finds more and more attention in orthopedic imaging as well. Offering a superior in-plane resolution compared to CT imaging and the additional depth information compared to conventional 2-D X-ray images, tomosynthesis may be an interesting complement to the other two imaging modalities. Additionally, a tomosynthesis scan is likely to be faster and the radiation dose is considerably below that of a CT. Usually, a tomosynthetic acquisition focuses only on one body part as the common acquisition techniques restrict the field-of-view. We propose a method which is able to perform full-body acquisitions with a standard X-ray system by shifting source and detector simultaneously in parallel planes without the need to calibrate the system beforehand. Furthermore, a novel aliasing filter is introduced which addresses the impact of the non-isotropic resolution during the reconstruction. We provide images obtained by filtered as well as unfiltered backprojection and discuss the influence of the scanning angle as well as the reconstruction filter on the reconstructed images. We found from the experiments that our method shows promising results especially for the imaging of anatomical structures which are usually obscured by each other since the depth resolution allows to distinguish between these structures. Additionally, as of the high isotropic in-plane spatial resolution of the tomographic volume, it is easily possible to perform precise measurements which are a crucial task, e. g. during the planning of orthopedic surgeries or the assessment of pathologies like scoliosis or subtle fractures.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.parall
Keywords: Digital Tomosynthesis; Tomography; Aliasing Filter; Reconstruction; Full-body imaging; Orthopedics

Parametric LV Model Fitting to Coronary Arteries - Geimer, Tobias; Höhn, Johannes; Unberath, Mathias; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
X-ray angiography is the gold standard in assessing coronary artery diseases. With research focus shifting towards 3D+t applications, heart models that allow for the extraction of functional parameters from the heart motion receive increasing attention. We present an approach to fit a parametric left ventricular heart model originally developed for tagged MRI to the centerlines of coronary arteries reconstructed from rotational coronary angiography. This 3D model fitting process must accommodate the sparse point set conditional to the underlying angiography data. Using a coarse-to-fine optimization based on simulated annealing and ellipsoid pseudo-distances, we achieve a reprojection error of 0.794 mm for the surface points compared to 0.422 mm of the 3D centerline ground truth. Results are promising and form the basis to extend the model to 3D+t in order to monitor radial and longitudinal contraction as well as left ventricular twist over the cardiac cycle.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.parame_9

Parkinson’s disease progression assessment from speech using a mobile device-based application - Arias Vergara, Tomás; Klumpp, Philipp; Vasquez Correa, Juan; Orozco Arroyave, Juan Rafael; Nöth, Elmar (TSD 2017: Text, Speech, and Dialogue, 2017)
This paper presents preliminary results of individual speaker models for monitoring Parkinson’s disease from speech using a smart-phone. The aim of this study is to evaluate the suitability of mobile devices to perform robust speech analysis. Speech recordings from 68 PD patients were captured from 2012 to 2016 in four recording sessions. The performance of the speaker models is evaluated according to two clinical rating scales: the Unified Parkinson’s Disease Rating Scale (UPDRS) and a modified version of the Frenchay Dysarthria Assessment (m-FDA) scale. According to the results, it is possible to assess the disease progression from speech with Pearson’s correlations of up to r=0.51 . This study suggests that it is worth to continue working on the development of mobile-based tools for the continuous and unobtrusive monitoring of Parkinson’s patients.<br
Keywords: Monitoring; Mobile device; Speaker model; Parkinson’s disease

Partial Image Data Registration using Stochastic Optimization - Han, Jingfeng; Qiao, Min; Hornegger, Joachim; Kuwert, Torsten; Bautz, Werner; Römer, Wolfgang (Frontiers in Simulation, 2005)

Particle filter de-noising of voxel-specific time-activity-curves in personalized 177Lu therapy - Götz, Theresa; Götz, Th I.; Lang, E. W.; Schmidkonz, Christian; Maier, Andreas; Kuwert, Torsten; Ritt, Philipp (Zeitschrift für Medizinische Physik, 2019)
Background: Currently, there is a high interest in 177Lu targeted radionuclide therapies, which could be attributed to favorable results obtained from 177Lu compounds targeting neuro-endocrine and prostate tumors. SPECT based dosimetry could be used for deriving dose values for individual voxels, as is the standard in external-beam radiation-therapy (EBRT). For this a time-activity-curve (TAC) at voxel resolution and also a voxel-wise modeling of radiation energy deposition are necessary. But a voxel-wise determination of TACs is problematic, since several confounding factors exist, such as e.g. poor count-statistics or registration inaccuracies, which add noise to the observed activity states. A particle filter (PF) is a class of methods which applies regularization based on a model of the temporal evolution of activity states. The aim of this study is to introduce the application of PFs for de-noising of per-voxel time-activity curves. Methods: We applied a PF for de-noising the TACs of 26 patients, who underwent 177Lu-DOTATOC or -PSMA therapy. The TACs were obtained from fully-quantitative, serial SPECT(/CT) data, acquired at 4 h, 24 h, 48 h, 72 h p.i. The model used in the PF was a mono-exponential decay and its free parameters were determined based on objective criteria. The time-integrated activities (TIA) resulting from the PF (PFF) were compared to the results of a mono-exponential fit (SF) of individual voxels in several volumes of interest (kidneys, spleen, tumors). Additionally, an organ-averaged TIA was derived from whole-organ VOIs and subsequent curve-fitting. This whole-organ TIA was also compared to the whole-organ TIAs obtained from summation of the voxel-wise TIAs from PFF and SF. Results: The number of particles was set to 1000. Optimal values for noise of observations and noise of the model were 0.25 and 0.5, respectively. The deviation of whole-organ TIAs from conventional organ-based dosimetry and the summation of the voxel-wise TIAs was substantial for SF (kidneys −22.3%, spleen −49.6%, tumor −60.0%), as well as for PFF (kidneys −37.1%, spleen −57.9%, tumor −70.9%). The distribution of voxel-wise half-lives resulting from the PFF method was considerably closer to the organ-averaged value, and the number of implausibly long half-lives (>physical HL) was reduced. Conclusion: The PFF leads to voxel-wise half-lives, which are more plausible than those resulting from SF. However, one has to admit that voxel-wise fitting generally leads to considerable deviations from the organ-averaged TIA as obtained by conventional whole-organ evaluation. Unfortunately, we did not have ground-truth TIA of our patient data and proper ground-truth could even be impossible to obtain. Nevertheless, there are strong indicators that particle filtering can be used for reducing voxel-wise TAC noise.
CRIS-Team Scopus Importer:2019-12-27
Keywords: Dosimetry; Nuclear medicine; Time-activity-curve; Voxelwise

Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-Site Robustness Assessment - Aubreville, Marc; Goncalves, Miguel; Knipfer, Christian; Oetter, Nicolai; Würfl, Tobias; Neumann, Helmut; Stelzle, Florian; Bohr, Christopher; Maier, Andreas (Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018), 2018)
Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the algorithm on images acquired from the vocal fold area of five patients with histologically verified squamous cell carcinoma and presumably healthy control images of the clinically normal contra-lateral vocal cord. We find that the network trained on the oral cavity data reaches an accura

Patch based Specular Reflection Removal for Range Images in Hybrid 3-D Endoscopy - Haase, Sven; Wasza, Jakob; Safak, Mustafa; Kilgus, Thomas; Maier-Hein, Lena; Feußner, Hubertus; Hornegger, Joachim (2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.patchb

Patient-bounded Extrapolation for 3D Region of Interest Reconstruction in C-arm CT - Xia, Yan; Bauer, Sebastian; Maier, Andreas; Berger, Martin; Hornegger, Joachim (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.patien_1
Keywords: GRK-1773

Patient-bounded extrapolation using low-dose priors for volume-of-interest imaging in C-arm CT - Xia, Yan; Bauer, Sebastian; Maier, Andreas; Berger, Martin; Hornegger, Joachim (Medical Physics, 2015)
Purpose: Three-dimensional (3D) volume-of-interest (VOI) imaging with C-arm systems provides anatomical information in a predefined 3D target region at a considerably low x-ray dose. However, VOI imaging involves laterally truncated projections from which conventional reconstruction algorithms generally yield images with severe truncation artifacts. Heuristic based extrapolation methods, e.g., water cylinder extrapolation, typically rely on techniques that complete the truncated data by means of a continuity assumption and thus appear to be ad-hoc. It is our goal to improve the image quality of VOI imaging by exploiting existing patient-specific prior information in the workflow. Methods: A necessary initial step prior to a 3D acquisition is to isocenter the patient with respect to the target to be scanned. To this end, low-dose fluoroscopic x-ray acquisitions are usually applied from anteriorposterior (AP) and medio-lateral (ML) views. Based on this, the patient is isocentered by repositioning the table. In this work, we present a patient-bounded extrapolation method that makes use of these noncollimated fluoroscopic images to improve image quality in 3D VOI reconstruction. The algorithm first extracts the 2D patient contours from the noncollimated AP and ML fluoroscopic images. These 2D contours are then combined to estimate a volumetric model of the patient. Forward-projecting the shape of the model at the eventually acquired C-arm rotation views gives the patient boundary information in the projection domain. In this manner, we are in the position to substantially improve image quality by enforcing the extrapolated line profiles to end at the known patient boundaries, derived from the 3D shape model estimate. Results: The proposed method was evaluated on eight clinical datasets with different degrees of truncation. The proposed algorithm achieved a relative root mean square error (rRMSE) of about 1.0% with respect to the reference reconstruction on nontruncated data, even in the presence of severe truncation, compared to a rRMSE of 8.0% when applying a state-of-the-art heuristic extrapolation technique. Conclusions: The method we proposed in this paper leads to a major improvement in image quality for 3D C-arm based VOI imaging. It involves no additional radiation when using fluoroscopic images that are acquired during the patient isocentering process. The model estimation can be readily integrated into the existing interventional workflow without additional hardware.
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.patien
Keywords: computed tomography; extrapolation; fluoroscopic image; truncation correction; volume of interest imaging

Patient-individualized boundary conditions for CFD simulations using time-resolved 3D angiography - Bögel, Marco; Gehrisch, Sonja; Redel, Thomas; Rohkohl, Christopher; Dörfler, Arnd; Maier, Andreas; Kowarschik, Markus; Hölter, Philip (International Journal of Computer Assisted Radiology and Surgery, 2016)
Hemodynamic simulations are of increasing interest for the assessment of aneurysmal rupture risk and treatment planning. Achievement of accurate simulation results requires the usage of several patient-individual boundary conditions, such as a geometric model of the vasculature but also individualized inflow conditions.We propose the automatic estimation of various parameters for boundary conditions for computational fluid dynamics (CFD) based on a single 3D rotational angiography scan, also showing contrast agent inflow. First the data are reconstructed, and a patient-specific vessel model can be generated in the usual way. For this work, we optimize the inflow waveform based on two parameters, the mean velocity and pulsatility. We use statistical analysis of the measurable velocity distribution in the vessel segment to estimate the mean velocity. An iterative optimization scheme based on CFD and virtual angiography is utilized to estimate the inflow pulsatility. Furthermore, we present methods to automatically determine the heart rate and synchronize the inflow waveform to the patient's heart beat, based on time-intensity curves extracted from the rotational angiogram. This will result in a patient-individualized inflow velocity curve.The proposed methods were evaluated on two clinical datasets. Based on the vascular geometries, synthetic rotational angiography data was generated to allow a quantitative validation of our approach against ground truth data. We observed an average error of approximately [Formula: see text] for the mean velocity, [Formula: see text] for the pulsatility. The heart rate was estimated very precisely with an average error of about [Formula: see text], which corresponds to about 6 ms error for the duration of one cardiac cycle. Furthermore, a qualitative comparison of measured time-intensity curves from the real data and patient-specific simulated ones shows an excellent match.The presented methods have the potential to accurately estimate patient-specific boundary conditions from a single dedicated rotational scan.
EVALuna2:23010

Patient positioning using 3-D surface registration - Adelt, André; Schaller, Christian; Penne, Jochen; Hornegger, Joachim (Proceedings of the 4th Russian-Bavarian Conference on Biomedical Engineering, 2008)

Patient-specific modeling of the heart: Applications to cardiovascular disease management - Ionasec, Razvan; Voigt, Ingmar; Mihalef, Viorel; Grbic, Sasa; Vitanovski, Dime; Wang, Yabin; Zheng, Yefeng; Hornegger, Joachim; Navab, Nassir; Georgescu, Bogdan; Comaniciu, Dorin (Springer-verlag, 2010)
As decisions in cardiology increasingly rely on non-invasive methods, fast and precise image analysis tools have become a crucial component of the clinical workflow. Especially when dealing with complex cardiovascular disorders, such as valvular heart disease, advanced imaging techniques have the potential to significantly improve treatment outcome as well as to reduce procedure risks and related costs. We are developing patient-specific cardiac models, estimated from available multi-modal images, to enable advanced clinical applications for the management of cardiovascular disease. In particular, a novel physiological model of the complete heart, including the chambers and valvular apparatus is introduced, which captures a large spectrum of morphological, dynamic and pathological variations. To estimate the patient-specific model parameters from four-dimensional cardiac images, we have developed a robust learning-based framework. The model-driven approach enables a multitude of advanced clinical applications. Gold standard clinical methods, which manually process 2D images, can be replaced with fast, precise, and comprehensive model-based quantification to enhance cardiac analysis. For emerging percutaneous and minimal invasive valve interventions, cardiac surgeons and interventional cardiologists can substantially benefit from automated patient selection and virtual valve implantation techniques. Furthermore, the complete cardiac model enables for patient-specific hemodynamic simulations and blood flow analysis. Extensive experiments demonstrated the potential of these technologies to improve treatment of cardiovascular disease. © 2010 Springer-Verlag Berlin Heidelberg.
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD5.patien

Patient-Specific Model of Left Heart Anatomy, Dynamics and Hemodynamics from 4D TEE: A First Validation Study - Voigt, Ingmar; Mansi, Tommaso; Mihalef, Viorel; Ionasec, Razvan; Calleja, Anna; Assoumou Mengue, Etienne; Sharma, Puneet; Houle, Helene; Georgescu, Bogdan; Hornegger, Joachim; Comaniciu, Dorin (Springer-verlag, 2011)
Patient-specific models of the heart physiology have become powerful instruments able to improve the diagnosis and treatment of cardiac disease. A systemic representation of the whole organ is required to capture the complex functional and hemodynamical interdependencies among the anatomical structures. We propose a novel framework for personalized modeling of the left-side heart that integrates comprehensive data of the morphology, function and hemodynamics. Patient-specific fluid dynamics are computed over the entire cardiac cycle using embedded boundary and ghost fluid methods, constrained by the dynamics of highly detailed anatomical models. Personalized boundary conditions are determined by estimating cardiac shape and motion from 4D TEE images through robust discriminative learning methods. Qualitative and quantitative validation of the computed blood dynamics is performed against Doppler echocardiography measurements, following an original methodology. Results showed a high agreement between simulation and ground truth and a correlation of r = 0.85 (p < 0.0002675). To the best of our knowledge, this is the first time that computational fluid dynamics are simulated on a systemic and comprehensive patient-specific model of the heart and validated against routinely acquired clinical ground truth. © 2011 Springer-Verlag Berlin Heidelberg.
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD5.patien

PATSY-I: A Corpus on Non-Native English Air Traffic Communication - Kaufhold, Caroline; Martindale, Christine; Horndasch, Axel; Reinhard, Klaus; Nöth, Elmar (Tagungsband der 12. Tagung Phonetik und Phonologie im deutschsprachigen Raum, 2016)
In many global tasks English is used as an international language. As a consequence, non-native speakers of the English language often communicate with other non-native speakers. An example is the Air Traffic Control (ATC) service which directs aircrafts on the ground and through controlled airspace. It is of course essential that there is a perfect understanding between the pilot and the ground-based controller to prevent collisions and to organize air traffic efficiently. Aviation English already accommodates non-native speakers of English by providing guidelines for wording and phraseology. To avoid confusion, for example, letters and numbers are spelled according to the international spelling alphabet provided by the International Civil Aviation Organization (ICAO). However, the ability to speak and understand English still has a high impact on communication success. In this paper, we present a corpus that was recorded in the context of the ATC phraseology training system PATSY, the prototype of which was presented at the Show&Tell session at Interspeech 2015. The corpus consists of basic ATC utterances by speakers of 16 different mother tongues. Furthermore, "Please Call Stella" and part of "The Rainbow Passage" were recorded twice for every speaker with different biases. We plan on using this data to study the entrainment effect, which was observed for conversations by Levitan and Hirschberg. Preliminary results on basic ATC utterances show a moderate correlation between the speakers' self-assessment and the GoP (Goodness of Pronunciation) score.<br
Keywords: English as Lingua Franca (ELF); Computer Assisted Pronunciation Training (CAPT); Accent Entrainment

PATSY - It's all about pronunciation! - Kaufhold, Caroline; Gamidov, Vadim; Kiessling, Andreas; Reinhard, Klaus; Nöth, Elmar (16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015, 2015)
PATSY is an abbreviation for its German name "Piloten/ATC Trainingssystem für den Sprechfunk" which translates to pilot/ air traffic control (ATC) training system for radio communication. The phraseology training system is intended to be a stand-alone, platform-independent, multi-user e-Learning framework for learning and practicing the ATC radio communication wordings and at the same time improving intelligibility of the speaker by pronunciation scoring. A serious gaming approach is aimed at, which allows the user to practice his or her communication skills in almost real-life scenarios. At the Show and Tell session at Interspeech 2015, we would like to present a subsystem of PATSY in which we only concentrate on pronunciation scoring. The speaker is prompted to record a sequence of words of the NATO phonetic alphabet and he is given back a visually enhanced feedback regarding his pronunciation score. Further aspects of PATSY are the verification of correct syntax and the assessment of the speaker's "comfort level" which states how familiar the speaker is with the topic in question.
Keywords: Augmented learning; Computer Assisted Pronunciation Training (CAPT); Pronunciation scoring

Pattern Recognition and Image Processing in C CLASS="cmtt-12">++ - Paulus, Dietrich; Hornegger, Joachim (Vieweg, 1995)

Pattern Recognition in Medical and Health Engineering '10 - Hornegger, Joachim; Höller, Kurt Emmerich; Spiegel, Martin; Niedermeier, Hans-Peter (Kloster Banz, 2010)
UnivIS-Import:2015-05-08:Pub.2010.tech.IMMD.IMMD5.rectif

Pattern Recognition in Medical and Health Engineering - Hornegger, Joachim; Höller, Kurt Emmerich; Ritt, Philipp; Borsdorf, Anja; Niedermeier, Hans-Peter (Union Aktuell, 2008)
UnivIS-Import:2015-07-08:Pub.2008.tech.IMMD.IMMD5.patter_7

Pattern Recognition of Images and Speech in C++ - Paulus, Dietrich; Hornegger, Joachim (Vieweg, 1997)

Patterns, Prototypes, Performance: Classifying Emotional User States - Seppi, Dino; Batliner, Anton; Schuller, Björn; Steidl, Stefan; Vogt, Thurid; Wagner, Johannes; Devillers, Laurence; Vidrascu, Laurence; Amir, Noam; Aharonson, Vered (Proceedings of Interspeech, 2008)
In this paper, we report on classification results for emotional user states (4 classes, German database of children interacting with a pet robot). Starting with 5 emotion labels per word, we obtained chunks with different degrees of prototypicality. Six sites computed acoustic and linguistic features independently from each other. A total of 4232 features were pooled together and grouped into 10 low level descriptor types. For each of these groups separately and for all taken together, classification results using Support Vector Machines are reported for 150 features each with the highest individual Information Gain Ratio, for a scale of prototypicality. With both acoustic and linguistic features, we obtained a relative improvement of up to 27.6%, going from low to higher prototypicality.
Keywords: emotion; prototypes; feature types; automatic classification

PCA Regularized Nonrigid Registration for PET/MRI Attenuation Correction - Daum, Volker; Hahn, Dieter; Hornegger, Joachim; Kuwert, Torsten (Proceedings of the MICCAI Workshop on Probabilistic Models For Medical Image Analysis, 2009)

PEAKS-A Platform for Evaluation and Analysis of all Kinds of Speech Disorders - Maier, Andreas; Haderlein, Tino; Schuster, Maria; Nöth, Elmar (Proc. 41st Annual Meeting of the Society for Biomedical Technologies of the Association for Electrical, Electronic & Information Technologies (BMT 2007), 2007)

PEAKS - A system for the automatic evaluation of voice and speech disorders - Maier, Andreas; Haderlein, Tino; Eysholdt, Ulrich; Rosanowski, Frank; Batliner, Anton; Schuster, Maria; Nöth, Elmar (Speech Communication, 2009)

PEAKS: Ein Client-Server-Internetportal zur Bewertung der Aussprache - Maier, Andreas; Haderlein, Tino; Nöth, Elmar; Schuster, Maria (Telemed 2008 Proceedings, 2008)

Pectoral Muscle Detection in Digital Breast Tomosynthesis and Mammography - Ghesu, Florin-Cristian; Wels, Michael; Jerebko, Anna; Sühling, Michael; Hornegger, Joachim; Kelm, B. Michael (Medical Computer Vision. Large Data in Medical Imaging, 2014)
Screening and diagnosis of breast cancer with Digital Breast Tomosynthesis (DBT) and Mammography are increasingly supported by algorithms for automatic post-processing. The pectoral muscle, which dorsally delineates the breast tissue towards the chest wall, is an important anatomical structure for navigation. Along with the nipple and the skin, the pectoral muscle boundary is often used for reporting the location of breast lesions. It is visible in mediolateral oblique (MLO) views where it is well approximated by a straight line. Here, we propose two machine learning-based algorithms to robustly detect the pectoral muscle in MLO views from DBT and mammography. Embedded into the Marginal Space Learning framework, the algorithms involve the evaluation of multiple candidate boundaries in a hierarchical manner. To this end, we propose a novel method for candidate generation using a Hough-based approach. Experiments were performed on a set of 100 DBT volumes and 95 mammograms from different clinical cases. Our novel combined approach achieves competitive accuracy and robustness. In particular, for the DBT data, we achieve significantly lower deviation angle error and mean distance error than the standard approach. The proposed algorithms run within a few seconds. © 2014 Springer International Publishing Switzerland.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.pector

Pediatric Patient Surface Model Atlas Generation and X-Ray Skin Dose Estimation - Zhong, Xia; Roser, Philipp; Bayer, Siming; Ravikumar, Nishant; Strobel, Norbert; Birkhold, Annette; Horz, Tim; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (BVM, 2019)
Fluoroscopy is used in a wide variety of examinations and procedures to diagnose or treat patients in modern pediatric medicine. Although these image guided interventions have many advantages in treating pediatric patients, understanding the deterministic and long term stochastic effects of ionizing radiation are of particular importance for this patient demographic. Therefore, quantitative estimation and visualization of radiation exposure distribution, and dose accumulation over the course of a procedure, is crucial for intra-procedure dose tracking and long term monitoring for risk assessment. Personalized pediatric models are necessary for precise determination of patient-X-ray interactions. One way to obtain such a model is to collect data from a population of pediatric patients, establish a population based generative pediatric model and use the latter for skin dose estimation. In this paper, we generate a population model for pediatric patient using data acquired by two RGB-D cameras from different views. A generative atlas was established using template registration. We evaluated the registered templates and generative atlas by computing the mean vertex error to the associated point cloud. The evaluation results show that the mean vertex error reduced from 25.2 ± 12.9 mm using an average surface model to 18.5 ± 9.4mm using specifically estimated pediatric surface model using the generated atlas. Similarly, the dose estimation error was halved from 10.6 ± 8.5% using the average surface model to 5.9 ± 9.3% using the personalized surface estimates.<br

Penalized least-square CT reconstruction without and with statistical weights: effect on lesion detection performance with human observers - Haase, Viktor; Griffith, April; Guo, Zijia; Oktay, Mehmet; Hahn, Katharina; Schöndube, Harald; Stierstorfer, Karl; Noo, Frederic (Proceedings of Fully3D'17, 2017)
UnivIS-Import:2019-08-15:Pub.2017.tech.IMMD.IMMD5.penali_5

Perceptual analysis of speech signals from people with Parkinson's disease - Orozco-Arroyave, Juan Rafael; Arias-Londoño, Julián David; Vargas-Bonilla, Jesús Francisco; Nöth, Elmar (2013)
Parkinson's disease (PD) is a neurodegenerative disorder of the nervous central system and it affects the limbs motor control and the communication skills of the patients. The evolution of the disease can get to the point of affecting the intelligibility of the patient's speech. The treatments of the PD are mainly focused on improving limb symptoms and their impact on speech production is still unclear. Considering the impact of the PD in the intelligibility of the patients, this paper explores the discrimination capability of different perceptual features in the task of automatic classification of speech signals from people with Parkinson's disease (PPD) and healthy controls (HC). The experiments presented in this paper are performed considering the five Spanish vowels uttered by 20 PPD and 20 HC. The considered set of features includes linear prediction coefficients (LPC), linear prediction cepstral Coefficients (LPCC), Mel-frequency cepstral coefficients (MFCC), perceptual linear prediction coefficients (PLP) and two versions of the relative spectra coefficients (RASTA). Accordin the results for vowels /e/ and /o/ it is not enough to consider one kind of perceptual features, it is required to perform combination of different coefficients such as PLP, MFCC and RASTA. For the case of the remaining vowels, the best results are obtained considering only one kind of perceptual features, PLP for vowel /a/ and MFCC for vowels /i/ and /u/. © 2013 Springer-Verlag.
Keywords: linear prediction; Parkinson's disease; Perceptual analysis; relative spectra analysis

[Perceptual and automatic voice and speech analysis of chronic laryngitis and T1 vocal cord cancer]. - Bartke, Betina; Haderlein, Tino; Döllinger, Michael; Nöth, Elmar; Graf, S.; Eysholdt, Ulrich; Ziethe, A. (HNO, 2013)
BACKGROUND Patients with chronic laryngitis and T1 vocal cord cancer were compared using perceptual and text-based objective voice and speech analyses in order to determine which group is more affected in its ability to communicate and whether a distinction between the two pathologies is possible. PATIENTS AND METHODS In all, 13 patients with histologically proven chronic laryngitis and 13 patients with T1 vocal cord cancer were compared perceptually by five speech therapists on the basis of seven criteria and objectively by a speech recognition system and prosodic analysis. RESULTS Both, the data of the five speech therapists and the results of the automatic analysis revealed no significant differences between the two patient groups. CONCLUSION A distinction between chronic laryngitis and T1 vocal cord carcinoma by mere voice and speech analysis is not possible, because the patient groups do not show significant differences in their voice quality.
Keywords: Automatic pattern recognition; Comparative study; Laryngitis; Perceptual and objective voice and speech analysis; Vocal folds

Percutaneous punctures with MR imaging guidance: Comparison between MR imaging-enhanced fluoroscopic guidance and real-time MR imaging guidance - Meyer, Bernhard; Brost, Alexander; Kraitchman, Dara; Gilson, Wesley D.; Strobel, Norbert; Hornegger, Joachim; Lewin, Jonathan; Wacker, Frank (Radiology, 2013)
Purpose: To evaluate and compare the technical accuracy and feasibility of magnetic resonance (MR) imaging-enhanced fluoroscopic guidance and real-time MR imaging guidance for percutaneous puncture procedures in phantoms and animals. Materials and Methods: The experimental protocol was approved by the institutional animal care and use committee. Punctures were performed in phantoms, aiming for markers (20 each for MR imaging-enhanced fluoroscopic guidance and real-time MR imaging guidance), and pigs, aiming for anatomic landmarks (10 for MR imaging-enhanced fluoroscopic guidance and five for MR imaging guidance). To guide the punctures, T1-weighted three-dimensional (3D) MR images of the phantom or pig were acquired. Additional axial and coronal T2- weighted images were used to visualize the anatomy in the animals. For MR imaging-enhanced fluoroscopic guidance, phantoms and pigs were transferred to the fluoroscopic system after initial MR imaging and C-arm computed tomography (CT) was performed. C-arm CT and MR imaging data sets were coregistered. Prototype navigation software was used to plan a puncture path with use of MR images and to superimpose it on fluoroscopic images. For realtime MR imaging, an interventional MR imaging prototype for interactive real-time section position navigation was used. Punctures were performed within the magnet bore. After completion, 3D MR imaging was performed to evaluate the accuracy of insertions. Puncture durations were compared by using the log-rank test. The Mann-Whitney U test was applied to compare the spatial errors. Results: In phantoms, the mean total error was 8.6 mm ± 2.8 with MR imaging-enhanced fluoroscopic guidance and 4.0 mm ± 1.2 with real-time MR imaging guidance (P < .001). The mean puncture time was 2 minutes 10 seconds ± 44 seconds with MR imaging- enhanced fluoroscopic guidance and 37 seconds ± 14 with real-time MR imaging guidance (P < .001). In the animal study, a tolerable distance (<1 cm) between target and needle tip was observed for both MR imaging-enhanced fluoroscopic guidance and real-time MR imaging guidance. The mean total error was 7.7 mm ± 2.4 with MR imaging-enhanced fluoroscopic guidance and 7.9 mm ± 4.9 with real-time MR imaging guidance (P = .77). The mean puncture time was 5 minutes 43 seconds ± 2 minutes 7 seconds with MR imaging-enhanced fluoroscopic guidance and 5 minutes 14 seconds ± 2 minutes 25 seconds with real-time MR imaging guidance (P = .68). Conclusion: Both MR imaging-enhanced fluoroscopic guidance and real-time MR imaging guidance demonstrated reasonable and similar accuracy in guiding needle placement to selected targets in phantoms and animals. © RSNA, 2012.

Performance analysis of X-Ray phase-contrast interferometers with respect to grating layouts - Haas, Wilhelm; Bartl, Peter; Bayer, Florian; Durst, Jürgen; Grund, T.; Kenntner, J.; Michel, Thilo; Ritter, André; Weber, Thomas; Anton, Gisela; Hornegger, Joachim (2010 IEEE Nuclear Science Symposium, Medical Imaging Conference, NSS/MIC 2010 and 17th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, RTSD 2010, 2010)
The grating-based phase-contrast imaging approach is highly dependent on the quality of the used gratings. While the fabrication of gratings for the soft X-ray range is more or less well controllable, the fabrication for the hard X-ray range (>30 keV) is more challenging as the gratings must have high aspect ratios and thus fine structures. One of the best fabrication technologies for such gratings is LIGA (Lithography, Electroplating and Molding). However, due to such small structures and high aspect ratio it is unavoidable that the gratings become non-perfect and have deformations. Since the fabrication is complex, expensive and time consuming, a simple way is needed to assess the influence of such deformations on the signal and also a simple way to design and test new grating layouts. This work presents a simulation framework for X-ray phase-contrast imaging which allows to model, simulate and assess the quality of arbitrary grating layouts in an easier, cheaper and faster way. Furthermore, it allows the assessment of the quality of new grating layouts as well as of existing gratings. © 2010 IEEE.

Performance Comparison of Two Step Segmentation Algorithms using Different Step Activities - Leutheuser, Heike; Dölfel, Sina; Schuldhaus, Dominik; Reinfelder, Samuel; Eskofier, Björn (Wearable and Implantable Body Sensor Networks (BSN), 2014 11th International Conference on, 2014)
Insufficient physical activity is the 4th leading risk factor for mortality. The physical activity of a person is reflected in the walking behavior. Different methods for the calculation of the accurate step number exists and most of them are evaluated using different walking speeds measured on a treadmill or using a small sample size of overground walking. In this paper, we introduce the BaSA (Basic Step Activities) dataset consisting of four different step activities (walking, jogging, ascending, and descending stairs) that were performed under natural conditions. We further compare two step segmentation algorithms (a simple peak detection algorithm vs. subsequence Dynamic Time Warping (sDTW)). We calculated a multivariate Analysis of Variance (ANOVA) with repeated measures followed by multiple dependent t-tests with Bonferroni correction to test for significant differences in the two algorithms. sDTW performed equally good compared to the peak detection algorithm, but was not considerably better. In further analysis, continuous, real walking signals with transitions from one step activity to the other step activity should be considered to investigate the adaptability of these two step segmentation algorithms. © 2014 IEEE.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.perfor_2

Performance of a Module and Defect Detection Algorithm for Aerial Infrared Images as a Function of the Flying Altitude - Dalsaß, Manuel; Deitsch, Sergiu; Luchscheider, Philipp; Gallwitz, Florian; Brabec, Christoph (32nd European Photovoltaic Solar Energy Conference and Exhibition, 2016)
n recent years, remote-controlled unmanned air vehicles (UAV) have been used increasingly to attain aerial infrared (aIR) recordings of large photovoltaic (PV) power plants with which thermally suspicious PV modules can be identified and localized. But so far, aIR recordings are still analyzed mostly manually – a process which can be extremely time-consuming and error-prone. In this context, the performance of a module and defect detection algorithm for aIR images is evaluated as a function of the flying altitude. For this purpose, aIR recordings of a PV power plant are generated from six flying altitudes from 5 m to 30 m above ground level by means of a drone measurement system and used as data input for the image processing tool. The results indicate optimal flying altitudes for both module and defect detection. However, the most suitable flying altitude may be a compromise with further conditions. The findings of this work will advance future automatic monitoring and early detection systems.<br
Keywords: Monitoring; Aerial Thermography; PV Module Defects; Automatic Detection

Performance of Continuous Hydrogen Production from Perhydro Benzyltoluene by Catalytic Distillation and Heat Integration Concepts with a Fuel Cell - Rüde, Timo; Lu, Yanye; Anschuetz, Leon; Blasius, Marco; Wolf, Moritz; Preuster, Patrick; Wasserscheid, Peter; Geißelbrecht, Michael (Energy Technology, 2023)
The benzyltoluene-based liquid organic hydrogen carrier (LOHC) system enables the safe transport and loss-free storage of hydrogen. At least 26% of the lower heating value of the released hydrogen, however, has to be invested in form of heat to release the stored hydrogen. The low operation temperatures of catalytic distillation (CD) can facilitate waste heat integration to reduce external heat demand. Herein, the continuous hydrogen release from perhydro benzyltoluene via CD is demonstrated. It is revealed in the experimental results that this mode of operation leads to a high hydrogen release rate and very efficient noble metal catalyst usage at exceptionally mild conditions. The hydrogen-based productivity of platinum of 0.35 g(H2) g(Pt)(-1) min(-1) (0.7 kW(LHV_H2) g(Pt)(-1)) at a dehydrogenation temperature of only 267 degrees C is found to be nearly four times higher than for the conventional continuous liquid-phase dehydrogenation at the same temperature. Furthermore, simulation results of the CD process are described. The feasibility of a fully heat-integrated process for electricity generation from the released hydrogen via CD using waste heat from the fuel cell for the CD reboiler is demonstrated. The technical potential of coupling the H12-BT dehydrogenation by CD with high-temperature fuel cell operation is highlighted by the simulation.
CRIS-Team WoS Importer:2023-02-24

Periodic Load Balancing on the N-Cycle: Analytical and Experimental Evaluation - Riess, Christian; Wanka, Rolf (Proc. 13th European Conference in Parallel Processing (Euro-Par), 2007)

Personalized Learning-based Segmentation of Thoracic Aorta and Main Branches for Diagnosis and Treatment Planning - Vitanovski, Dime; Ralovich, Kristof; Ionasec, Razvan; Zheng, Yefeng; Suehling, Michael; Krawtschuk, Waldemar; Hornegger, Joachim; Comaniciu, Dorin (Cardiac and Vascular Imaging, 2012)
Coarctation of the aorta (CoA), is an obstruction of the aortic arch present in 5-8% of congenital heart diseases. For children older than a year, CoA is increasingly treated by aortic stenting instead of surgical repair. In pediatric cardiology, CMR is accepted as the standard non-invasive imaging modality to assess the aortic arch in it's entire spatial context [1]. Interpreting such 3D datasets are required to assess the underlying anatomy during both diagnosis and therapy planning phases. However this process is time consuming and varies with operator skills. Within this study we propose for the first time in our knowledge a method of automatic segmentation of the lumen of thoracic aorta and main branches. The personalized model of the aorta and the supra-aortic arteries, automatically estimated from 3D CMR data, will provide better understanding of the complexity of pathology and assist the cardiologist to choose the best treatment and timing of repair. A hierarchical framework based on robust machine-learning algorithms is proposed to estimate the personalized model parameters. Experiments throughout 212 3D CMR volumes demonstrate model estimation error of 3.24 mm and average computation time of 8 sec. combined with clinical evaluation on 32 patients. © 2012 IEEE.
UnivIS-Import:2017-03-24:Pub.2012.tech.IMMD.IMMD5.person

Personalized Mitral Valve Closure Computation and Uncertainty Analysis from 3D Echocardiography - Voigt, Ingmar; Neumann, Dominik; et al. (Medical Image Analysis, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.person

Personalized Modeling and Assessment of the Aortic-Mitral Coupling from 4D TEE and CT - Ionasec, Razvan; Voigt, Ingmar; Georgescu, Bogdan; Wang, Yabin; Houle, Helene; Hornegger, Joachim; Navab, Nassir; Comaniciu, Dorin (Springer-verlag, 2009)
The anatomy, function and hemodynamics of the aortic and mitral valves are known to be strongly interconnected. An integrated quantitative and visual assessment of the aortic-mitral coupling may have an impact on patient evaluation, planning and guidance of minimal invasive procedures. In this paper, we propose a novel model-driven method for functional and morphological characterization of the entire aortic-mitral apparatus. A holistic physiological model is hierarchically defined to represent the anatomy and motion of the two left heart valves. Robust learning-based algorithms are applied to estimate the patient-specific spatial-temporal parameters from four-dimensional TEE and CT data. The piecewise affine location of the valves is initially determined over the whole cardiac cycle using an incremental search performed in marginal spaces. Consequently, efficient spectrum detection in the trajectory space is applied to estimate the cyclic motion of the articulated model. Finally, the full personalized surface model of the aortic-mitral coupling is constructed using statistical shape models and local spatial-temporal refinement. Experiments performed on 65 4D TEE and 69 4D CT sequences demonstrated an average accuracy of 1.45mm and speed of 60 seconds for the proposed approach. Initial clinical validation on model-based and expert measurement showed the precision to be in the range of the inter-user variability. To the best of our knowledge this is the first time a complete model of the aortic-mitral coupling estimated from TEE and CT data is proposed. © 2009 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.person_6

Personalized pulmonary trunk modeling for intervention planning and valve assessment estimated from CT data - Vitanovski, Dime; Ionasec, Razvan; Georgescu, Bogdan; Huber, Martin; Taylor, Andrew; Hornegger, Joachim; Comaniciu, Dorin (Springer-verlag, 2009)
Pulmonary valve disease affects a significant portion of the global population and often occurs in conjunction with other heart dysfunctions. Emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. As minimal invasive procedures become common practice, imaging and non-invasive assessment techniques turn into key clinical tools. In this paper, we propose a novel approach for intervention planning as well as morphological and functional quantification of the pulmonary trunk and valve. An abstraction of the anatomic structures is represented through a four-dimensional, physiological model able to capture large pathological variation. A hierarchical estimation, based on robust learning methods, is applied to identify the patient-specific model parameters from volumetric CT scans. The algorithm involves detection of piecewise affine parameters, fast centre-line computation and local surface delineation. The estimated personalized model enables for efficient and precise quantification of function and morphology. This ability may have impact on the assessment and surgical interventions of the pulmonary valve and trunk. Experiments performed on 50 cardiac computer tomography sequences demonstrated the average speed of 202 seconds and accuracy of 2.2mm for the proposed approach. An initial clinical validation yielded a significant correlation between model-based and expert measurements. To the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from CT data. © 2009 Springer-Verlag.

Personalized pulmonary trunk modeling for intervention planning and valve assessment estimated from CT data - Vitanovski, Dime; Ionasec, Razvan Ioan; Georgescu, Bogdan; Huber, Martin; Taylor, Andrew Mayall; Hornegger, Joachim; Comaniciu, Dorin (Proceedings of 12th Internation Conference on Medical Image Computing and Computer-Assisted Intervention, 2009)
Pulmonary valve disease affects a significant portion of the global population and often occurs in conjunction with other heart dysfunctions. Emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. As minimal invasive procedures become common practice, imaging and non-invasive assessment techniques turn into key clinical tools. In this paper, we propose a novel approach for intervention planning as well as morphological and functional quantification of the pulmonary trunk and valve. An abstraction of the anatomic structures is represented through a four-dimensional, physiological model able to capture large pathological variation. A hierarchical estimation, based on robust learning methods, is applied to identify the patient-specific model parameters from volumetric CT scans. The algorithm involves detection of piecewise affine parameters, fast centre-line computation and local surface delineation. The estimated personalized model enables for efficient and precise quantification of function and morphology. This ability may have impact on the assessment and surgical interventions of the pulmonary valve and trunk. Experiments performed on 50 cardiac computer tomography sequences demonstrated the average speed of 202 seconds and accuracy of 2.2mm for the proposed approach. An initial clinical validation yielded a significant correlation between model-based and expert measurements. To the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from CT data.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.person_33

Phase-Sensitive Region-of-Interest Computed Tomography - Felsner, Lina; Berger, Martin; Käppler, Sebastian; Bopp, Johannes; Ludwig, Veronika; Weber, Thomas; Pelzer, Georg; Michel, Thilo; Maier, Andreas; Anton, Gisela; Riess, Christian (21st International Conference on Medical Image Computing and Computer Assisted Intervention, 2018)

Phase-unwrapping of differential phase-contrast data using attenuation information - Haas, Wilhelm; Bech, Martin; Bartl, Peter; Bayer, Florian; Ritter, André; Weber, T.; Pelzer, Georg; Willner, M.; Achterhold, K.; Durst, Jürgen; Michel, Thilo; Prümmer, Marcus; et al. (Medical Imaging 2011: Image Processing, 2011)
Phase-contrast imaging approaches suffer from a severe problem which is known in Magnetic Resonance Imaging (MRI) and Synthetic Aperture Radar (SAR) as phase-wrapping. This work focuses on an unwrapping solution for the grating based phase-contrast interferometer with X-rays. The approach delivers three types of information about the x-rayed object - the absorption, differential phase-contrast and dark-field information whereas the observed differential phase values are physically limited to the interval (-π, π]; values higher or lower than the interval borders are mapped (wrapped) back into it. In contrast to existing phase-unwrapping algorithms for MRI and SAR the presented algorithm uses the absorption image as additional information to identify and correct phase-wrapped values. The idea of the unwrapping algorithm is based on the observation that at locations with phase-wrapped values the contrast in the absorption image is high and the behavior of the gradient is similar to the real (unwrapped) phase values. This can be expressed as a cost function which has to be minimized by an integer optimizer. Applied on simulated and real datasets showed that 95.6% of phase-wraps were correctly unwrapped. Based on the results we conclude that it is possible to use the absorption information in order to identify and correct phase-wrapped values. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Phonation and articulation analyses in laryngeal pathologies, cleft lip and palate, and parkinson’s disease - Jimenez-Monsalve, Juan Camilo; Vasquez Correa, Juan; Orozco Arroyave, Juan Rafael; Nöth, Elmar (Springer Verlag, 2017)

Phonation and articulation analysis of Spanish vowels for automatic detection of Parkinson's disease - Orozco-Arroyave, Juan Rafael; Arias-Londoño, Julián David; Belalcazar-Bolaños, Elkyn; Vargas-Bonilla, Jesus-Francisco; Haderlein, Tino; Nöth, Elmar (Proc. Text, Speech and Dialogue; 17th International Conference, TSD 2014, 2014)
UnivIS-Import:2018-09-06:Pub.2014.tech.IMMD.IMMD5.phonat_9
Keywords: speech

Phonation and articulation analysis of Spanish vowels for automatic detection of Parkinson's disease - Orozco-Arroyave, Juan Rafael; Belalcazar-Bolaños, Elkyn; Arias-Londoño, Julián David; Vargas-Bonilla, Jesús Francisco; Haderlein, Tino; Nöth, Elmar (Springer Verlag, 2014)
Parkinson's disease (PD) is a chronic neurodegenerative disorder of the nervous central system and it can affect the communication skills of the patients. There is an interest in the research community to develop computer aided tools for the analysis of the speech of people with PD for detection and monitoring. In this paper, three new acoustic measures for the simultaneous analysis of the phonation and articulation of patients with PD are presented. These new measures along with other classical articulation and perturbation features are objectively evaluated with a discriminant criterion. According to the results, the speech of people with PD can be detected with an accuracy of 81% when phonation and articulation features are combined. © 2014 Springer International Publishing.
Keywords: acoustics; articulation; nonlinear dynamics; Parkinson's disease; phonation

Phone-attribute posteriors to evaluate the speech of cochlear implant users - Arias Vergara, Tomás; Orozco-Arroyave, J. R.; Cernak, M.; Gollwitzer, S.; Schuster, M.; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019)
People with pre- and postlingual onset of deafness, i.e, age of occurrence of hearing loss, often present speech production problems even after hearing rehabilitation by cochlear implantation. In this paper, the speech of 20 prelinguals (aged between 18 to 71 years old), 20 postlinguals (aged between 33 to 78 years old) and 20 healthy control (aged between 31 to 62 years old) German native speakers are analyzed considering phone-attribute features extracted with pre-trained Deep Neural Networks. Speech signals are analyzed with reference to the manner of articulation of consonants according to 5 groups: nasals, sibilants, fricatives, voiced-stops, and voiceless-stops. According to the results, it is possible to detect alterations in the consonant production of CI users when compared with healthy speakers. A comprehensive evaluation of speech changes of CI users will help in the rehabilitation after deafening.
CRIS-Team Scopus Importer:2019-11-19
Keywords: Acoustic analysis; Cochlear implant; Hearing loss; Onset of deafness

Phoneme-to-Grapheme Mapping for Spoken Inquiries to the Semantic Web - Horndasch, Axel; Nöth, Elmar; Batliner, Anton; Warnke, Volker (Proceedings of the Ninth International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP), 2006)
Automatic methods for grapheme-to-phoneme (G2P) and phoneme-to-grapheme (P2G) conversion have become very popular in recent years. Their performance has improved considerably, while at the same time these developments required less input from expert lexicographers. Continuing in this tradition we will present in this paper a data-driven, language-independent approach called MASSIVE (the name was chosen because with the approach Mapping Arbitrary Symbol Sets Is Very Easy) with which it is possible to create efficient online modules for automatic symbol mapping. Our framework is solely based on statistical methods for training and run-time and has been optimized for P2G conversion in the context of spoken inquiries to the Semantic Web, an issue researched in the SmartWeb project (https://www.dfki.de/web/forschung/projekte-publikationen/projekte/projekt/smartweb/). MASSIVE systems can be trained using a pronunciation lexicon, the output of a phone recognizer or any other suitable set of corresponding symbol strings. Successful tests have been performed on German and English data sets.<br
Keywords: Grapheme-to-phoneme; phoneme-to-grapheme; sound-to-letter; letter-to-sound; MASSIVE

Phonet: A tool based on gated recurrent neural networks to extract phonological posteriors from speech - Vasquez Correa, Juan; Klumpp, Philipp; Orozco Arroyave, Juan Rafael; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019)
There are a lot of features that can be extracted from speech signals for different applications such as automatic speech recognition or speaker verification. However, for pathological speech processing there is a need to extract features about the presence of the disease or the state of the patients that are comprehensible for clinical experts. Phonological posteriors are a group of features that can be interpretable by the clinicians and at the same time carry suitable information about the patient's speech. This paper presents a tool to extract phonological posteriors directly from speech signals. The proposed method consists of a bank of parallel bidirectional recurrent neural networks to estimate the posterior probabilities of the occurrence of different phonological classes. The proposed models are able to detect the phonological classes with accuracies over 90%. In addition, the trained models are available to be used by the research community interested in the topic.
Created from Fastlane, Scopus look-up
Keywords: Gated recurrent units; Pathological speech; Phonological posteriors; Recurrent neural network

Phonological posteriors and GRU recurrent units to assess speech impairments of patients with Parkinson’s disease - Vasquez Correa, Juan; Garcia, Nicanor; Orozco-Arroyave, Juan Rafael; Cernak, Milos; Nöth, Elmar (Text, Speech, and Dialogue, 2018)
Parkinson’s disease is a neurodegenerative disorder characterized by a variety of motor symptoms, including several impairments in the speech production process. Recent studies show that deep learning models are highly accurate to assess the speech deficits of the patients; however most of the architectures consider static features computed from a complete utterance. Such an approach is not suitable to model the dynamics of the speech signal when the patients pronounce different sounds. Phonological features can be used to characterize the voice quality of the speech, which is highly impaired in patients suffering from Parkinson’s disease. This study proposes a deep architecture based on recurrent neural networks with gated recurrent units combined with phonological posteriors to assess the speech deficits of Parkinson’s patients. The aim is to model the time-dependence of consecutive phonological posteriors, which follow the sound patterns of English phonological model. The results show that the proposed approach is more accurate than a baseline based on standard acoustic features to assess the speech deficits of the patient

Photometric Estimation of 3-D Surface Motion Fields for Respiration Management - Bauer, Sebastian; Wasza, Jakob; Hornegger, Joachim (Bildverarbeitung für die Medizin 2012, 2012)

Photonics in medicine -From diagnostics to training - Schmidt, Michael; Zimmermann, Maik; Hohmann, Martin; Paulus, Jan (2011)

Photorealistic 3-D Surface Reconstructions Using TOF Cameras - Penne, Jochen; Höller, Kurt Emmerich; Wilhelm, Dirk; Feußner, Hubertus; Hornegger, Joachim (3rd Russian-Bavarian Conference on Biomedical Engineering, 2007)

Photoreceptor Lengthening During Dark Adaptation Detected With Ultra-High Resolution Optical Coherence Tomography - Lu, Chen D.; Choi, Woo Jhon; Wang, Zhao; Kraus, Martin; Hornegger, Joachim; Duker, Jay S.; Fujimoto, James G. (Investigative Ophthalmology & Visual Science, 2015)

Physical Constraints for Beam Hardening Reduction using Polynomial Models - Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Aichert, André; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.physic

Physics-based and Statistical Features for Image Forensics - Physikalische und statistische Merkmale in der Bildforensik - Riess, Christian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2013)
Keywords: Image forensics; physics-based image forensics; statistical image forensics; copy-move forgery detection; JPEG forensics; illumination analysis; non-uniform illumination estimation; illumination direction

Physics-based illuminant color estimation as an image semantics clue - Riess, Christian; Angelopoulou, Elli (Proceedings of the International Conference on Image Processing, 2009)

Piezoelectric-transducer-based miniature catheter for ultrahigh-speed endoscopic optical coherence tomography - Tsai, Tsung Han; Potsaid, Benjamin; Kraus, Martin; Zhou, Chao; Tao, Yuankai K.; Hornegger, Joachim; Fujimoto, James G. (Biomedical Optics Express, 2011)
We developed a piezoelectric-transducer- (PZT) based miniature catheter with an outer diameter of 3.5 mm for ultrahigh-speed endoscopic optical coherence tomography (OCT). A miniaturized PZT bender actuates a fiber and the beam is scanned through a GRIN lens and micro-prism to provide high-speed, side-viewing capability. The probe optics can be pulled back over a long distance to acquire three-dimensional (3D) data sets covering a large area. Imaging is performed with 11 μm axial resolution in air (8 μm in tissue) and 20 μm transverse resolution, at 960 frames per second with a Fourier domain mode-locked laser operating at 480 kHz axial scan rate. Using a high-speed data acquisition system, endoscopic OCT imaging of the rabbit esophagus and colon in vivo and human colon specimens ex vivo is demonstrated. © 2011 Optical Society of America.

Piezoelectric transducer based miniature catheter for ultrahigh speed endoscopic optical coherence tomography - Tsai, Tsung-Han; Potsaid, Benjamin M.; Kraus, Martin; Liu, Jonathan J.; Zhou, Chao; Hornegger, Joachim; Fujimoto, James G. (Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XV, 2011)
We developed a piezoelectric transducer (PZT) based miniature catheter with an outer diameter of 3 mm for ultrahigh speed endoscopic optical coherence tomography (OCT) using Fourier domain modelocked (FDML) laser at a 480 kHz axial scan rate. The miniaturized PZT bender actuates a fiber to provide high scanning speed. The side-viewing probe can be pulled back for a long distance to acquire three-dimensional (3D) dataset covering a large area on the specimen. Operating with a high speed data acquisition (DAQ) system, OCT imaging with 6.5 mm imaging range, 10 μm axial resolution, 20 μm lateral resolution, and frame rate of 480 frames per second (fps) is demonstrated. © 2011 Copyright SPIE - The International Society for Optical Engineering.

Planungstool für die Cryo Ballon Technik zur Pulmonalvenenisolation - Bourier, Felix; Vukajlovic, Dejan; Brost, Alexander; Hornegger, Joachim; Strobel, Norbert; Kurzidim, Klaus (78. Jahrestagung der Deutschen Gesellschaft für Kardiologie, 2012)

Polychromatic Maximum Likelihood Reconstruction for Talbot-Lau X-ray Tomography - Schiffers, Florian; Käppler, Sebastian; Pelzer, Georg; Wolf, Andreas; Maier, Andreas; Anton, Gisela; Riess, Christian (International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2017)

Portability of TV-Regularized Reconstruction Parameters to Varying Data Sets - Amrehn, Mario; Maier, Andreas; Dennerlein, Frank; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015, 2015)
In C-arm computed tomography there are certain constraints due to the data acquisition process which can cause limited raw data. The reconstructed image’s quality may significantly decrease depending on these constraints. To compensate for severely under-sampled projection data during reconstruction, special algorithms have to be utilized, more robust to such ill-posed problems. In the past few years it has been shown that reconstruction algorithms based on the theory of compressed sensing are able to handle incomplete data sets quite well. In this paper, the iterative iTV reconstruction method by Ludwig Ritschl et al. is analyzed regarding it’s elimination capabilities of image artifacts caused by incomplete raw data with respect to the settings of it’s various parameters. The evaluation of iTV and the data dependency of iterative reconstruction’s parameters is conducted in two stages. First, projection data with severe angular under-sampling is acquired using an analytical phantom. Proper reconstruction parameters are selected by analyzing the reconstruction results from a set of proposed parameters. In a second step multiple phantom data sets are acquired with limited angle geometry and a small number of projections. The iTV reconstructions of these data sets are compared to short-scan FDK and SART reconstruction results, highlighting the distinct data dependence of the iTV reconstruction parameters.
UnivIS-Import:2018-09-06:Pub.2015.tech.IMMD.IMMD5.portab_4
Keywords: health; medicine; image processing; reconstruction; segmentation; pattern recognition; machine learning

Post-procedural evaluation of catheter contact force characteristics - Koch, Martin; Brost, Alexander; Kiraly, Attila P.; Strobel, Norbert; Hornegger, Joachim (Medical Imaging 2012: Computer-Aided Diagnosis, 2012)
Minimally invasive catheter ablation of electric foci, performed in electrophysiology labs, is an attractive treatment option for atrial fibrillation (AF)-in particular if drug therapy is no longer effective or tolerated. There are different strategies to eliminate the electric foci inducing the arrhythmia. Independent of the particular strategy, it is essential to place transmural lesions. The impact of catheter contact force on the generated lesion quality has been investigated recently, and first results are promising. There are different approaches to measure catheter-tissue contact. Besides traditional haptic feedback, there are new technologies either relying on catheter tip-to-tissue contact force or on local impedance measurements at the tip of the catheter. In this paper, we present a novel tool for post-procedural ablation point evaluation and visualization of contact force characteristics. Our method is based on localizing ablation points set during AF ablation procedures. The 3-D point positions are stored together with lesion specific catheter contact force (CF) values recorded during the ablation. The force records are mapped to the spatial 3-D positions, where the energy has been applied. The tracked positions of the ablation points can be further used to generate a 3-D mesh model of the left atrium (LA). Since our approach facilitates visualization of different force characteristics for post-procedural evaluation and verification, it has the potential to improve outcome by highlighting areas where lesion quality may be less than desired. © 2012 SPIE.

Postural Instability Inference in Parkinson's Disease Patients Using Wearable Sensors - Pasluosta, Cristian Federico; Barth, Jens; Gaßner, Heiko; Winkler, Jürgen; Klucken, Jochen; Eskofier, Björn (88th Congress of the Deutschen Gesellschaft für Neurologie mit Fortbildungsakademie (DGN), 2015)

Precipitation of nanoparticles: A coupled LIF / PIV, DNS and PBE approach for prediction of the particle size distribution (PSD) - Peukert, Wolfgang; Gradl, Johannes; Schwertfirm, Florian; Schwarzer, Hans-Christoph; Manhart, Michael (2005)
Continuous precipitation is a promising process to generate nanoparticles with tailored properties. One challenge is to predict the PSD by considering all fluid dynamical, physiochemical and thermodynamical process parameters. A CFD-based approach using DNS in combination with Lagrangian Particle Tracking is applied coupled with micromixing and population balance models. The simulated flow field is validated by optical measurement methods (PIV and LIF). Whereas the mean particle size in dependence of the Re-number (over 6 orders of magnitude) can be predicted by a 1D approach, prediction of the complete PSD needs information from the fully resolved 3D fluid flow field.
Keywords: precipitation; continuous; mixing; prediction; simulation

Precipitation of nanoparticles in a T-mixer: Coupling the particle population dynamics with hydrodynamics through direct numerical simulation - Peukert, Wolfgang; Gradl, Johannes; Schwarzer, Hans-Christoph; Schwertfirm, Florian; Manhart, Michael (Chemical Engineering and Processing, 2006)
Mixing is a key parameter to tailor the particle size distribution (PSD) in nanoparticle precipitation. In this study we present two model approaches to simulate the impact of mixing on the PSD, using barium sulfate as exemplary material. In the first model, we combine a Lagrangian micromixing model with the population balance equation. This approach was found successful in predicting the influence of mixing on mean particle sizes but fails to predict the shape and width of the PSD. This is attributed to the neglect of spatial and temporal fluctuations in that model. Therefore, an improved CFD-based approach using direct numerical simulation (DNS) in combination with Lagrangian particle tracking strategy is applied. We found that the full DNS-approach, coupled to the population balance and a derived micromixing model, is capable of predicting the full PSD in nanoparticle precipitation. © 2006 Elsevier B.V. All rights reserved.
Keywords: Direct numeric simulation; Micromixing; Nanoparticle precipitation; Population balance; Supersaturation

Precise Lumen Segmentation in Coronary Computed Tomography Angiography - Lugauer, Felix; Zheng, Yefeng; Hornegger, Joachim; Kelm, B. Michael (Medical Computer Vision: Algorithms for Big Data, 2014)
Coronary computed tomography angiography (CCTA) allows for non-invasive identification and grading of stenoses by evaluating the degree of narrowing of the blood-filled vessel lumen. Recently, methods have been proposed that simulate coronary blood flow using computational fluid dynamics (CFD) to compute the fractional flow reserve non-invasively. Both grading and CFD rely on a precise segmentation of the vessel lumen from CCTA.We propose a novel, model-guided segmentation approach based on a Markov random field formulation with convex priors which assures the preservation of the tubular structure of the coronary lumen. Allowing for various robust smoothness terms, the approach yields very accurate lumen segmentations even in the presence of calcified and non-calcified plaques. Evaluations on the public Rotterdam segmentation challenge demonstrate the robustness and accuracy of our method: on standardized tests with multi-vendor CCTA from 30 symptomatic patients, we achieve superior accuracies as compared to both state-of-the-art methods and medical experts.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.precis
Keywords: CCTA; Lumen segmentation; Markov random field; Tubular surface

Precision Learning: Reconstruction Filter Kernel Discretization - Syben-Leisner, Christopher; Stimpel, Bernhard; Breininger, Katharina; Würfl, Tobias; Fahrig, Rebecca; Dörfler, Arnd; Maier, Andreas (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.precis_0

Precision Learning: Towards Use of Known Operators in Neural Networks - Maier, Andreas; Schebesch, Frank; Syben-Leisner, Christopher; Würfl, Tobias; Steidl, Stefan; Choi, Jang-Hwan; Fahrig, Rebecca (2018 24rd International Conference on Pattern Recognition (ICPR), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.precis_5

Prediction of MRI Hardware Failures based on Image Features using Ensemble Learning - Kuhnert, Nadine; Pflueger, Lea; Maier, Andreas (Bildverarbeitung für die Medizin 2020, 2020)
In order to ensure trouble-free operation, prediction of hardware failures is essential. This applies especially to medical systems. Our goal is to determine hardware which needs to be exchanged before failing. In this work, we focus on predicting failures of head/neck coils using image-related measurements. Thus, we aim to solve a classification problem with two classes, normal and broken coil. To solve this problem, we use data of two different levels. One level refers to one-dimensional features per individual coil channel on which we found a fully connected neural network to perform best. Furthermore, we use matrix features representing the state of an entire coil to train another neural network on. This ensemble of two networks and combining them using a Random Forest improves the prediction results even further. Thus, combining insights of both trained models allows us to determine the coil's condition with an F-score of 94.14% and an accuracy of 99.09%.

Prediction of MRI Hardware Failures based on Image Features using Time Series Classification - Kuhnert, Nadine; Pflueger, Lea; Maier, Andreas (Bildverarbeitung für die Medizin, 2020)
Already before systems malfunction one has to know if hardware components will fail in near future in order to counteract in time. Thus, unplanned downtime is ought to be avoided. In medical imaging, maximizing the system's uptime is crucial for patients' health and healthcare provider's daily business. We aim to predict failures of Head/Neck coils used in Magnetic Resonance Imaging (MRI) by training a statistical model on sequential data collected over time. As image features depend on the coil's condition, their deviations from the normal range already hint to future failure. Thus, we used image features and their variation over time to predict coil damage. After comparison of different time series classification methods we found Long Short Term Memorys (LSTMs) to achieve the highest F-score of 86.43% and to tell with 98.33% accuracy if hardware should be replaced.
Keywords: Time Series Classification; LSTM; Classification; Time Series Prediction

Prediction of Respiration-Induced Internal 3-D Deformation Fields From Dense External 3-D Surface Motion - Taubmann, Oliver; Wasza, Jakob; Forman, Christoph; Fischer, Peter; Wetzl, Jens; Maier, Andreas; Hornegger, Joachim (Proceedings of the 28th International Congress and Exhibition, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.predic_9
Keywords: Respiratory Motion; Motion Modeling; Motion Prediction; External Surrogate

Prediction of the Benefit of Motion-Compensated Reconstruction for Whole-Heart Coronary MRI - Wetzl, Jens; Forman, Christoph; Maier, Andreas; Hornegger, Joachim; Zenge, Michael O. (Proceedings of the 23rd Annual Meeting of the ISMRM (ISMRM 2015), 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.predic

Predictive simulation of nanoparticle precipitation based on the population balance equation - Peukert, Wolfgang; Schwarzer, Hans-Christoph; Schwertfirm, Florian; Manhart, Michael; Schmid, Hans-Joachim (Chemical Engineering Science, 2006)
Nanoparticle precipitation is an interesting process to generate particles with tailored properties. In this study we investigate the impact of various process steps such as solid formation, mixing and agglomeration on the resulting particle size distribution (PSD) as representative property using barium sulfate as exemplary material. Besides the experimental investigation, process simulations were carried out by solving the full 1D population balance equation coupled to a model describing the micromixing kinetics based on a finite-element Galerkin h-p-method. This combination of population balance and micromixing model was applied successfully to predict the influence of mixing on mean sizes (good quantitative agreement between experimental data and simulation results are obtained) and gain insights into nanoparticle precipitation: The interfacial energy was identified to be a critical parameter in predicting the particle size, poor mixing results in larger particles and the impact of agglomeration was found to increase with supersaturation due to larger particle numbers. Shear-induced agglomeration was found to be controllable through the residence time in turbulent regions and the intensity of turbulence, necessary for intense mixing but undesired due to agglomeration. By this approach, however, the distribution width is underestimated which is attributed to the large spectrum of mixing histories of fluid elements on their way through the mixer. Therefore, an improved computational fluid dynamics-based approach using direct numerical simulation with a Lagrangian particle tracking strategy is applied in combination with the coupled population balance-micromixing approach. We found that the full DNS-approach, coupled to the population balance and micromixing model is capable of predicting not only the mean sizes but the full PSD in nanoparticle precipitation. © 2005 Elsevier Ltd. All rights reserved.
Keywords: Computational fluid dynamics; Direct numerical simulation; Micromixing; Nanoparticle; Population balance; Precipitation

Preface - Jelinek, Fred; Nöth, Elmar (Springer Verlag, 1999)

Preliminary Study Investigating Brain Shift Compensation using 3D CBCT Cerebral Vascular Images - Bayer, Siming; Schaffert, Roman; Ravikumar, Nishant; Maier, Andreas; Fahrig, Rebecca; Ostermeier, Martin; Tong, Xiaoguang; Wang, Hu (Bildverarbeitung für die Medizin 2018, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.prelim_3

Principal component analysis of the spectrogram of the speech signal: Interpretation and application to dysarthric speech - Kacha, Abdellah; Grenez, Francis; Orozco Arroyave, Juan Rafael; Schoentgen, Jean (Computer Speech and Language, 2020)
The article concerns the interpretation of the principal components of the spectrogram of the speech signal and its application to the description of dysarthric speech. Each principal component is a linear combination of the frame spectra. We show that the first principal component of the spectrogram is closely related to the long-term average spectrum (LTAS) and the second principal component is the difference of two weighted sums of frame spectra reporting open and close vowel frame spectra respectively. We investigate articulation deficits in dysarthric speakers via cues obtained from principal components of the spectrogram of connected speech because long-term average spectra have been claimed to inform about speaker settings of the vocal tract.
CRIS-Team Scopus Importer:2019-08-09
Keywords: Dysarthric speech; Long-term average spectrum; Principal component analysis; Spectrogram

Prior-based automatic segmentation of the carotid artery lumen in TOF MRA (PASCAL) - Hutter, Jana; Hofmann, Hannes; Grimm, Robert; Greiser, Andreas; Saake, Marc; Hornegger, Joachim; Dörfler, Arnd; Schmitt, Peter (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012)
In current clinical practice, examinations of the carotid artery bifurcation are commonly carried out with Computed Tomography Angiography (CTA) or contrast-enhanced Magnetic Resonance Angiography (ceMRA). Quantitative information about vessel morphology, extracted from segmentations, is promising for diagnosis of vessel pathologies. However, both above-mentioned techniques require the administration of contrast media. In contrary, non-ce MRA methods such as Time-of-Flight (TOF) provide fully non-invasive imaging without any exogenous contrast agent. The diagnostic value of TOF MRA, however, for assessment of the carotid bifurcation area can be hampered due to its susceptibility to irregular blood flow patterns. Conventional methods for lumen segmentation are very sensitive to such signal voids and produce inaccurate results. In this work, a novel, fully automatic 3D segmentation algorithm is proposed which uses prior knowledge about irregular flow patterns. The presented technique has been successfully tested on eleven volunteer datasets as well as in a patient case, offering the comparison to CTA images. The sensitivity could be increased by 29.2% to 85.6% compared to standard level set methods. The root mean squared error in diameter measurements was reduced from 4.85mm to 1.44 mm.
CRIS-Team Scopus Importer:2022-08-05

Prior-Free Respiratory Motion Estimation in Rotational Angiography - Unberath, Mathias; Taubmann, Oliver; Aichert, André; Achenbach, Stephan; Maier, Andreas (IEEE Transactions on Medical Imaging, 2018)
Rotational coronary angiography using C-arm angiography systems enables intra-procedural 3-D imaging that is considered beneficial for diagnostic assessment and interventional guidance. Despite previous efforts, rotational angiography was not yet successfully established in clinical practice for coronary artery procedures due to challenges associated with substantial intra-scan respiratory and cardiac motion. While gating handles cardiac motion during reconstruction, respiratory motion requires compensation. State-of-the-art algorithms rely on 3-D / 2-D registration that requires an uncompensated reconstruction of sufficient quality. To overcome this limitation, we investigate two prior-free respiratory motion estimation methods based on the optimization of: 1) epipolar consistency conditions (ECCs) and 2) a task-based auto-focus measure (AFM). The methods assess redundancies in projection images or impose favorable properties of 3-D space, respectively, and are used to estimate the respiratory motion of the coronary arteries within rotational angiograms. We evaluate our algorithms on the publicly available CAVAREV benchmark and on clinical data. We quantify reductions in error due to respiratory motion compensation using a dedicated reconstruction domain metric. Moreover, we study the improvements in image quality when using an analytic and a novel temporal total variation regularized algebraic reconstruction algorithm. We observed substantial improvement in all figures of merit compared with the uncompensated case. Improvements in image quality presented as a reduction of double edges, blurring, and noise. Benefits of the proposed corrections were notable even in cases suffering little corruption from respiratory motion, translating to an improvement in the vessel sharpness of (6.08 ± 4.46)% and (14.7 ± 8.80)% when the ECC-based and the AFM-based compensation were applied. On the CAVAREV data, our motion compensation approach exhibits an improvement of (27.6 ± 7.5)% and (97.0 ± 17.7)% when the ECC and AFM were used, respectively. At the time of writing, our method based on AFM is leading the CAVAREV scoreboard. Both motion estimation strategies are purely image-based and accurately estimate the displacements of the coronary arteries due to respiration. While current evidence suggests the superior performance of AFM, future work will further investigate the use of ECC in the context of angiography as they solely rely on geometric calibration and projection-domain images.
EVALuna2:35976

Privacy Implications of Room Climate Data - Morgner, Philipp; Müller, Christian; Ring, Matthias; Eskofier, Björn; Riess, Christian; Armknecht, Frederik; Benenson, Zinaida (Proceedings of the 22nd European Symposium on Research in Computer Security (ESORICS 2017), 2017)

Private emotions versus social interaction: a data-driven approach towards analysing emotion in speech - Batliner, Anton; Steidl, Stefan; Hacker, Christian; Nöth, Elmar (User Modeling and User-Adapted Interaction, 2008)
The ‘traditional’ first two dimensions in emotion research are VALENCE and AROUSAL. Normally, they are obtained by using elicited, acted data. In this paper, we use realistic, spontaneous speech data from our ‘AIBO’ corpus (human-robot communication, children interacting with Sony’s AIBO robot). The recordings were done in a Wizard-of-Oz scenario: the children believed that AIBO obeys their commands; in fact, AIBO followed a fixed script and often disobeyed. Five labellers annotated each word as belonging to one of eleven emotion-related states; seven of these states which occurred frequently enough are dealt with in this paper. The confusion matrices of these labels were used in a Non-Metrical Multi-dimensional Scaling to display two dimensions; the first we interpret as VALENCE, the second, however, not as AROUSAL but as INTERACTION, i.e., addressing oneself (angry, joyful) or the communication partner (motherese, reprimanding). We show that it depends on the specifity of the scenario and on the subjects’ conceptualizations whether this new dimension can be observed, and discuss impacts on the practice of labelling and processing emotional data. Two-dimensional solutions based on acoustic and linguistic features that were used for automatic classification of these emotional states are interpreted along the same lines.
Keywords: emotion; speech; dimensions; categories; annotation; data-driven; non- metrical multi-dimensional scaling

Private Emotions vs. Social Interaction - towards New Dimensions in Research on Emotion - Batliner, Anton; Steidl, Stefan; Hacker, Christian; Nöth, Elmar; Niemann, Heinrich (Adapting the Interaction Style to Affective Factors, 2005)
The ‘traditional’ first two dimensions in emotion research are AROUSAL and VALENCE. Normally, they are obtained by using elicited, acted data. In this paper, we use realistic, spontaneous speech data from our ‘AIBO’ corpus (human-robot communication, children interacting with Sony’s AIBO robot). TherecordingsweredoneinaWizard-of-Ozscenario: the children believed that AIBO obeys their commands; in fact, AIBO followed a fixed script and often disobeyed. The emotional annotations of five labellers, transformed into a confusion matrix, were used in a non-metrical multi-dimensional scaling to display two dimensions, the first being VALENCE, the second, however, not AROUSAL but INTERACTION, i.e., addressing oneself (angry, joyful) or the communication partner (motherese, reprimanding). We show that it depends on the specifity of the scenario and on the subjects’ conceptualizations whether this new dimension can be observed, and discuss impacts on the practice of labelling and processing emotional data.
Keywords: emotion; categories; dimensions; annotation; non-metrical multi-dimensional scaling

Probabilistic Methods for 3-D Object Recognition - Hornegger, Joachim; Niemann, Heinrich; Shimshoni, Ilan (3D Image Analysis and Synthesis '96, 1996)

Probabilistic modeling and recognition of 3-D objects - Hornegger, Joachim; Niemann, Heinrich (International Journal of Computer Vision, 2000)
This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical tool for stochastic modeling. The algorithmic part introduces methods for automatic model generation, localization, and recognition of objects. 2-D images are used for learning the statistical appearance of 3-D objects; both the depth information and the matching between image and model features are missing for model generation. The implied incomplete data estimation problem is solved by the Expectation Maximization algorithm. This leads to a novel class of algorithms for automatic model generation from projections. The estimation of pose parameters corresponds to a non-linear maximum likelihood estimation problem which is solved by a global optimization procedure. Classification is done by the Bayesian decision rule. This work includes the experimental evaluation of the various facets of the presented approach. An empirical evaluation of learning algorithms and the comparison of different pose estimation algorithms show the feasibility of the proposed probabilistic framework.

Probabilistic Modeling in Computer Vision - Hornegger, Joachim; Paulus, Dietrich; Niemann, Heinrich (Computer Vision and Applications, 2000)

Probabilistic Semantic Analysis In Restricted Domains - Haas, Jürgen; Hornegger, Joachim; Niemann, Heinrich (Proceedings of the International Workshop on Speech and Computer, 1998)

Probabilistic semantic analysis of speech - Haas, Jürgen; Hornegger, Joachim; Huber, Richard; Niemann, Heinrich (Mustererkennung 1997, 1997)

Probabilistic Sparse Matching for Robust 3D/3D Fusion in Minimally Invasive Surgery - Neumann, Dominik; Grbic, Sasa; John, Matthias; Navab, Nassir; Hornegger, Joachim; Ionasec, Razvan (IEEE Transactions on Medical Imaging, 2015)
Classical surgery is being overtaken by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm computed tomography (CT) and C-arm fluoroscopy are routinely used in clinical practice for intraoperative guidance. However, due to constraints regarding acquisition time and device configuration, intraoperative modalities have limited soft tissue image quality and reliable assessment of the cardiac anatomy typically requires contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a probabilistic sparse matching approach to fuse high-quality preoperative CT images and nongated, noncontrast intraoperative C-arm CT images by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the preoperative CT and mapped to the intraoperative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments on 95 clinical datasets demonstrate that our model-based fusion approach has an average execution time of 1.56 s, while the accuracy of 5.48 mm between the anchor anatomy in both images lies within expert user confidence intervals. In direct comparison with image-to-image registration based on an open-source state-of-the-art medical imaging library and a recently proposed quasi-global, knowledge-driven multi-modal fusion approach for thoracic-abdominal images, our model-based method exhibits superior performance in terms of registration accuracy and robustness with respect to both target anatomy and anchor anatomy alignment errors.
UnivIS-Import:2015-04-02:Pub.2015.tech.IMMD.IMMD5.probab
Keywords: Anatomical overlay; computed tomography (CT); model-based cardiac image registration; Procedure guidance

Probability map based localization of optic disk - Budai, Attila; Laurik, Lenke; Hornegger, Joachim; Somfai, Gábor M.; Michelson, Georg (2012 19th International Conference on Systems, Signals and Image Processing, IWSSIP 2012, 2012)
Fundus imaging is one of themost commonly used modalities for examining retinal structures. The images are mostly analyzed by physicians. These diagnoses are subjective with a high rate of inter-observer variability, therefore an automated or a computer aided diagnosis is needed to speed up the monotonous screening process and to provide objective measurements. In this article we present an automatic algorithm for localization of the optic disk center in fundus images. The application field of the algorithm is preprocessing for further algorithms such as segmentation of the optic disk boundary, vessel tracking etc. The algorithm is evaluated using the DRIVE database and our own public high resolution image database. The tests show a localization error less than 0.2 optic disk diameter (ODD) in case of the high resolution database, and less than 0.35 ODD in case of the lower resolution DRIVE database. © 2012 Institute of Telecommunica.

Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds. - Goncalves, Miguel; Aubreville, Marc; Mueller, Sarina K.; Sievert, Matti; Maier, Andreas; Iro, Heinrich; Bohr, Christopher (Acta Otorhinolaryngologica Italica, 2019)
Probe-based confocal laser endomicroscopy (CLE) is an innovative technique for real-time, non-invasive analysis of the surface epithelium. While being successfully used for diagnosis by experts, this method has not yet been established in clinical routine, partly due to the lack of standards and criteria for classifying various lesions. Our aim was to determine the diagnostic value and inter-rater reliability of CLE in detecting malignant lesions of the vocal cords. 58 video sequences were extracted from the probe-based CLE (GastroFlex probe with a Cellvizio® laser system) examinations of 3 patients with squamous cell carcinomas and 4 patients with benign alterations of the vocal folds. Two ENT surgeons, who were blinded to the histological result, were asked to identify the sequences representing a carcinoma. We showed an accuracy, sensitivity, specificity, PPV and NPV of 91.38-96.55%, 100%, 87.8-95.2%, 77.27-89.47% and 100%, respectively, with an inter-rater reliability of k = 0.89 ("almost perfect agreement"). Probe-based CLE is a promising method for diagnosis and assessment of vocal fold lesions in vivo. Our results suggest that, with adequate training, the diagnostic value of this technique can be improved and potentially provide important information during oncological surgery.

Programmierhandbuch für NeuroGraph Version 3.0 - Wilke, Peter (1994)

Programmiersprachen mit Mengenkonzept für unkonventionelle Hardware - Wilke, Peter; Schneider, Hans Jürgen; Völk, Andreas (1985)
UnivIS-Import:2016-06-23:Pub.1985.tech.IMMD.IMMD2._progr

Projection and Reconstruction-Based Noise Filtering Methods in Cone Beam CT - Lorch, Benedikt; Berger, Martin; Hornegger, Joachim; Maier, Andreas (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.projec

Projection-Based Denoising Method for Photon-Counting Energy-Resolving Detectors - Lu, Yanye; Manhart, Michael; Taubmann, Oliver; Zobel, Tobias; Yang, Qiao; Choi, Jang-Hwan; Wu, Meng; Dörfler, Arnd; Fahrig, Rebecca; Ren, Qiushi; Hornegger, Joachim; Maier, Andreas (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.projec_7

Projection-based Material Decomposition by Machine Learning using Image-based Features for Computed Tomography - Lu, Yanye; Geret, Jan; Unberath, Mathias; Manhart, Michael; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.projec_5

Projection image-to-image translation in hybrid x-ray/MR imaging - Stimpel, Bernhard; Syben-Leisner, Christopher; Würfl, Tobias; Breininger, Katharina; Lommen, Jonathan; Dörfler, Arnd; Maier, Andreas (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
The potential benefit of hybrid X-ray and MR imaging in the interventional environment is large due to the combination of fast imaging with high contrast variety. However, a vast amount of existing image enhancement methods requires the image information of both modalities to be present in the same domain. To unlock this potential, we present a solution to image-to-image translation from MR projections to corresponding x-ray projection images. The approach is based on a state-of-the-art image generator network that is modified to fit the specific application. Furthermore, we propose the inclusion of a gradient map in the loss function to allow the network to emphasize high-frequency details in image generation. Our approach is capable of creating x-ray projection images with natural appearance. Additionally, our extensions show clear improvement compared to the baseline method.
CRIS-Team Scopus Importer:2019-07-12

Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging - Stimpel, Bernhard; Syben-Leisner, Christopher; Würfl, Tobias; Breininger, Katharina; Hölter, Philip; Dörfler, Arnd; Maier, Andreas (Scientific Reports, 2019)
Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in interventional medical imaging applications due to the broad variety of contrast of MRI combined with fast imaging of X-ray-based modalities. To fully utilize the potential of the vast amount of existing image enhancement techniques, the corresponding information from both modalities must be present in the same domain. For image-guided interventional procedures, X-ray fluoroscopy has proven to be the modality of choice. Synthesizing one modality from another in this case is an ill-posed problem due to ambiguous signal and overlapping structures in projective geometry. To take on these challenges, we present a learning-based solution to MR to X-ray projection-to-projection translation. We propose an image generator network that focuses on high representation capacity in higher resolution layers to allow for accurate synthesis of fine details in the projection images. Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging. The proposed extensions prove valuable in generating X-ray projection images with natural appearance. Our approach achieves a deviation from the ground truth of only 6% and structural similarity measure of 0.913 ± 0.005. In particular the high frequency weighting assists in generating projection images with sharp appearance and reduces erroneously synthesized fine details.<br

Projective Invariants for Geometric Calibration in Flat-Panel Computed Tomography - Aichert, André; Bier, Bastian; Rist, Leonhard; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.projec_8

Pronunciation Feature Extraction - Hacker, Christian; Cincarek, Tobias; Gruhn, Rainer; Steidl, Stefan; Nöth, Elmar; Niemann, Heinrich (Pattern Recognition, 27th DAGM Symposium, 2005)

Prosodic Classification of Offtalk: First Experiments - Batliner, Anton; Zeissler, Viktor; Nöth, Elmar; Niemann, Heinrich (Text, Speech and Dialogue, Proceedings of the Fifth International Conference on Text, Speech, Dialogue - TSD 2002, 2002)

Prosodic Dialog Control in EVAR. In Niemann, de Mori, and Hanrieder - Niemann, Heinrich; Eckert, Wieland; Kießling, Andreas; Kompe, Ralf; Kuhn, Thomas; Nöth, Elmar; Mast, Marion; Rieck, Stefan; Schukat-Talamazzini, Günter; Batliner, Anton (Progress and Prospects of Speech Research and Technology: Proc. of the CRIM/FORWISS Workshop (München, Sept. 1994), 1994)

Prosodic Events Recognition in Evaluation of Speech-Synthesis System Performance - Mihelic, France; Vesnicer, B.; Zibert, J.; Nöth, Elmar (Springer-verlag, 2008)
We present an objective-evaluation method of the prosody modeling in an HMM-based Slovene speech-synthesis system. Method is based on the results of the automatic recognition of syntactic-prosodic boundary positions and accented words in the synthetic speech. We have shown that the recognition results represent a close match with the prosodic notations, labeled by the human expert on the natural-speech counterpart that was used to train the speech-synthesis system. The recognition rate of the prosodic events is proposed as an objective evaluation measure for the quality of the prosodic modeling in the speech-synthesis system. The results of the proposed evaluation method are also in accordance with previous subjective-listening assesment evaluations, where high scores for the naturalness for such a type of speech synthesis were observed.
Keywords: Speech synthesis; system evaluation; prosody

Prosodic models and speech synthesis: towards the common ground - Möbius, Bernd; Möhler, Gregor; Schweitzer, Antje; Batliner, Anton; Nöth, Elmar (Proc. of Prosody 2000, 2000)

Prosodic Parsing of Spontaneous Speech - Nöth, Elmar; Kompe, Ralf; Kießling, Andreas; Niemann, Heinrich; Batliner, Anton; Schachtl, Stefanie; Ruland, Tobias; Block, Hans Ulrich (Proc. of the Workshop on Multi-lingual Spontaneous Speech Recognition in Real Environments, 1996)

Prosodic scoring of word hypotheses graphs - Kompe, Ralf; Kießling, Andreas; Niemann, Heinrich; Nöth, Elmar; Schukat-Talamazzini, Günter; Zottman, Andreas; Batliner, Anton (Proc. European Conf. on Speech Communication and Technology, 1995)

Prosody Takes Over: A Prosodically Guided Dialog System - Kompe, Ralf; Kießling, Andreas; Kuhn, Thomas; Mast, Marion; Niemann, Heinrich; Nöth, Elmar; Ott, Karin; Batliner, Anton (EUROSPEECH, 1993)

Prosody takes over: Towards a prosodically guided dialog system - Nöth, Elmar; Kießling, Andreas; Niemann, Heinrich; Ott, Karin; Batliner, Anton; Mast, Marion; Kuhn, Thomas; Kompe, Ralf (Speech Communication, 1994)
The domain of the speech recognition and dialog system EVAR is train time table inquiry. We observed that in real human-human dialogs when the officer transmits the information, the customer very often interrupts. Many of these interruptions are just repetitions of the time of day given by the officer. The functional role of these interruptions is often determined by prosodic cues only. An important result of experiments where naive persons used the EVAR system is that it is hard to follow the train connection given via speech synthesis. In this case it is even more important than in human-human dialogs that the user has the opportunity to interact during the answer phase. Therefore we extended the dialog module to allow the user to repeat the time of day and we added a prosody module guiding the continuation of the dialog by analyzing the intonation contour of this utterance.
Keywords: DIALOG; SENTENCE MODALITY; PROSODY; SPEECH UNDERSTANDING

Prospective optical motion correction for 3D time-of-flight angiography - Kopeinigg, Daniel; Aksoy, Murat; Forman, Christoph; Straka, Matus; Seaman, Danielle; Rosenberg, Jarrett; Fleischmann, Dominik; Hornegger, Joachim; Bammer, Roland (Magnetic Resonance in Medicine, 2012)

Prozessbegleitende Qualitätssicherung in der Elektronikproduktion - Wilke, Peter; Feldmann, Klaus; Götz, Knuth (Tagungsband Symposium Anwendung von Fuzzy Technologien und Neuronalen Netzen, 1996)
UnivIS-Import:2017-01-09:Pub.1996.tech.IMMD.IMMD2._proze

Pulmonary Vein Isolation guided by fluoroscopybased 3D-Navigation on a biplane angiography system - Bourier, Felix; Schneider, Hans-Jürgen; Heißenhuber, Frank; Ganslmeier, Patrycja; Brost, Alexander; Koch, Martin; Hornegger, Joachim; Kleinöder, Andreas; Kiraly, Attila P.; Barbot, Julien; Strobel, Norbert; Kurzidim, Klaus (Venice Arrhythmias 2011, 2011)

Pulmonary Vein Isolation Supported by Magnetic Resonance Imaging derived 3d-overlay of the Left Atrium with Motion Compensation in Biplane Fluoroscopy - Bourier, Felix; Vukajlovic, Dejan; Brost, Alexander; Hornegger, Joachim; Strobel, Norbert; Kurzidim, Klaus (33rd Annual Scientific Meeting, 2012)

Pulmonary vein isolation supported by MRI-derived 3D-augmented biplane fluoroscopy: A feasibility study and a quantitative analysis of the accuracy of the technique - Bourier, Felix; Vukajlovic, Dejan; Brost, Alexander; Hornegger, Joachim; Strobel, Norbert; Kurzidim, Klaus (Journal of Cardiovascular Electrophysiology, 2013)
MRI-Derived 3D-Augmented Biplane Fluoroscopy. Background: Despite the advancement of technology in electroanatomic mapping systems (EAMS), fluoroscopy remains a necessary, basic imaging modality for electrophysiology procedures. We present a feasibility study of new software that enables 3D-augmented fluoroscopy in biplane catheterization laboratories for planning and guidance of pulmonary vein isolation (PVI). The computer-assisted overlay registration accuracy was assessed in a clinical setting using an automatic calculation of overlay projection geometry that was derived from hardware sensors in C-arms, detectors, and patient table. Methods: Consecutive patients (n = 89) underwent left atrium (LA) magnetic resonance imaging MRI scan prior to PVI. Ideal ablation lines encircling the ipsilateral pulmonary veins (PVs) at antral level were drawn onto the segmented LA surface. The 3D-model was superimposed onto biplane fluoroscopy and matched with angiographies of LA and PVs. Three-dimensional-overlay projection geometry was automatically calculated from C-arm, detectors, and table sensors. Accuracy of technique was assessed as alignment of MRI-derived 3D overlay and angiographic LA/PV anatomy. Integrity of registered overlay was quantified using landmark measurements. Results: Alignment offsets were 1.3 ± 1.5 mm in left PV, 1.2 ± 1.5 mm in right PV, and 1.1 ± 1.4 mm in LA roof region. Bravais-Pearson correlation of the landmark measurements was r = 0.978 (s < 0.01), mean offset between landmark distance measurements was 1.4 ± 0.78 mm. Average time needed for overlay registration was 9.5 ± 3.5 seconds. Conclusions: MRI-derived 3D-augmented fluoroscopy demonstrated a high level of accuracy when compared with LA/PV angiography. The new system could be especially useful to guide procedures not supported by EAMS, such as cryotechnique PVI. © 2012 Wiley Periodicals, Inc.

Pushing the limits for medical image reconstruction on recent standard multicore processors - Eitzinger, Jan; Hager, Georg; Hofmann, Hannes; Hornegger, Joachim; Wellein, Gerhard (International Journal of High Performance Computing Applications, 2013)
Volume reconstruction by backprojection is the computational bottleneck in many interventional clinical computed tomography (CT) applications. Today vendors in this field replace special purpose hardware accelerators with standard hardware such as multicore chips and GPGPUs. Medical imaging algorithms are on the verge of employing high-performance computing (HPC) technology, and are therefore an interesting new candidate for optimization. This paper presents low-level optimizations for the backprojection algorithm, guided by a thorough performance analysis on four generations of Intel multicore processors (Harpertown, Westmere, Westmere EX, and Sandy Bridge). We choose the RabbitCT benchmark, a standardized testcase well supported in industry, to ensure transparent and comparable results. Our aim is to provide not only the fastest possible implementation but also compare with performance models and hardware counter data in order to fully understand the results. We separate the influence of algorithmic optimizations, parallelization, SIMD vectorization, and microarchitectural issues and pinpoint problems with current SIMD instruction set extensions on standard CPUs (SSE, AVX). The use of assembly language is mandatory for best performance. Finally, we compare our results to the best GPGPU implementations available for this open competition benchmark. © The Author(s) 2012.

Putting 'p' in RabbitCT - Fast CT Reconstruction Using a Standardized Benchmark - Hofmann, Hannes; Keck, Benjamin; Rohkohl, Christopher; Hornegger, Joachim (Mitteilungen - Gesellschaft für Informatik e.V., Parallel-Algorithmen und Rechnerstrukturen, 2009)

QMOS - A Robust Visualization Method for Speaker Dependencies with Different Microphones - Maier, Andreas; Schuster, Maria; Eysholdt, Ulrich; Haderlein, Tino; Cincarek, Tobias; Steidl, Stefan; Batliner, Anton; Wenhardt, Stefan; Nöth, Elmar (Journal of Pattern Recognition Research, 2009)

Quality-Guided Denoising for Low-Cost Fundus Imaging - Köhler, Thomas; Hornegger, Joachim; Mayer, Markus Anton; Michelson, Georg (Bildverarbeitung für die Medizin 2012, 2012)
The restoration of noisy images is an essential pre-processing step in many medical applications to ensure sufficient quality for diagnoses. In this paper we present a new quality-guided approach for denoising of eye fundus images that suffer from high noise levels. The denoising is based on image sequences and an adaptive frame averaging approach. The novelty of the method is that it takes an objective image quality criteria to assess the different frames and tries to maximize the quality of the resulting image. It can be implemented in an incremental manner which allows real-time denoising. We evaluated our approach on real image sequences captured by a low-cost fundus camera and obtained competitive results to a state-of-the-art method in terms of signal-to-noise ratio whereas our method performs denoising about four times faster.

Quantification of segmentation and F0 errors and their effect on emotion recognition - Steidl, Stefan; Batliner, Anton; Nöth, Elmar; Hornegger, Joachim (Springer-verlag, 2008)
Prosodic features modelling pitch, energy, and duration play a major role in speech emotion recognition. Our word level features, especially duration and pitch features, rely on correct word segmentation and F0 extraction. For the FAU Aibo Emotion Corpus, the automatic segmentation of a forced alignment of the spoken word sequence and the automatically extracted F0 values have been manually corrected. Frequencies of different types of segmentation and F0errors are given and their influence on emotion recognition using different groups of prosodic features is evaluated. The classification results show that the impact of these errors on emotion recognition is small. © 2008 Springer-Verlag Berlin Heidelberg.
UnivIS-Import:2015-04-16:Pub.2008.tech.IMMD.IMMD5.quanti_5

Quantification of thyroid volume using 3-D ultrasound imaging - Kollorz, Eva Nicole Karin; Hahn, Dieter; Linke, Rainer; Goecke, Tamme W.; Hornegger, Joachim; Kuwert, Torsten (IEEE Transactions on Medical Imaging, 2008)
Ultrasound (US) is among the most popular diagnostic techniques today. It is non-invasive, fast, comparably cheap, and does not require ionizing radiation. US is commonly used to examine the size, and structure of the thyroid gland. In clinical routine, thyroid imaging is usually performed by means of 2-D US. Conventional approaches for measuring the volume of the thyroid gland or its nodules may therefore be inaccurate due to the lack of 3-D information. This work reports a semi-automatic segmentation approach for the classification, and analysis of the thyroid gland based on 3-D US data. The images are scanned in 3-D, pre-processed, and segmented. Several pre-processing methods, and an extension of a commonly used geodesic active contour level set formulation are discussed in detail. The results obtained by this approach are compared to manual interactive segmentations by a medical expert in five representative patients. Our work proposes a novel framework for the volumetric quantification of thyroid gland lobes, which may also be expanded to other parenchymatous organs. © 2006 IEEE.

Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment - Ghesu, Florin-Cristian; Georgescu, Bogdan; Gibson, Eli; Gündel, Sebastian; Kalra, Mannudeep; Singh, Ramandeep; Digumarthy, Subba; Grbic, Sasa; Comaniciu, Dorin (Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 2019)
The interpretation of chest radiographs is an essential task for the detection of thoracic diseases and abnormalities. However, it is a challenging problem with high inter-rater variability and inherent ambiguity due to inconclusive evidence in the data, limited data quality or subjective definitions of disease appearance. Current deep learning solutions for chest radiograph abnormality classification are typically limited to providing probabilistic predictions, relying on the capacity of learning models to adapt to the high degree of label noise and become robust to the enumerated causal factors. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose an automatic system that learns not only the probabilistic estimate on the presence of an abnormality, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that explicitly learning the classification uncertainty as an orthogonal measure to the predicted output, is essential to account for the inherent variability characteristic of this data. Experiments were conducted on two datasets of chest radiographs of over 85,000 patients. Sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC, e.g., by 8% to 0.91 with an expected rejection rate of under 25%. Eliminating training samples using uncertainty-driven bootstrapping, enables a significant increase in robustness and accuracy. In addition, we present a multi-reader study showing that the predictive uncertainty is indicative of reader errors.<br

Quantifying the Effects of Acquisition Parameters in Cardiac SPECT Imaging and Comparison with Visual Observers - Zeintl, Johannes; Hornegger, Joachim; et al. (Nuclear Science Symposium, Medical Imaging Conference 2006, 2006)

Quantitative 3D-OCT motion correction with tilt and illumination correction, robust similarity measure and regularization - Kraus, Martin; Liu, Jonathan J.; Schottenhamml, Julia; Chen, Chieh-Li; Budai, Attila; Branchini, Lauren; Ko, Tony; Ishikawa, Hiroshi; Wollstein, Gadi; Schuman, Joel; Duker, Jay S.; Fujimoto, James G.; et al. (Biomedical Optics Express, 2014)
Variability in illumination, signal quality, tilt and the amount of motion pose challenges for post-processing based 3D-OCT motion correction algorithms. We present an advanced 3D-OCT motion correction algorithm using image registration and orthogonal raster scan patterns aimed at addressing these challenges. An intensity similarity measure using the pseudo Huber norm and a regularization scheme based on a pseudo L-0.5 norm are introduced. A two-stage registration approach was developed. In the first stage, only axial motion and axial tilt are coarsely corrected. This result is then used as the starting point for a second stage full optimization. In preprocessing, a bias field estimation based approach to correct illumination differences in the input volumes is employed. Quantitative evaluation was performed using a large set of data acquired from 73 healthy and glaucomatous eyes using SD-OCT systems. OCT volumes of both the optic nerve head and the macula region acquired with three independent orthogonal volume pairs for each location were used to assess reproducibility. The advanced motion correction algorithm using the techniques presented in this paper was compared to a basic algorithm corresponding to an earlier version and to performing no motion correction. Errors in segmentation-based measures such as layer positions, retinal and nerve fiber thickness, as well as the blood vessel pattern were evaluated. The quantitative results consistently show that reproducibility is improved considerably by using the advanced algorithm, which also significantly outperforms the basic algorithm. The mean of the mean absolute retinal thickness difference over all data was 9.9 um without motion correction, 7.1 um using the basic algorithm and 5.0 um using the advanced algorithm. Similarly, the blood vessel likelihood map error is reduced to 69% of the uncorrected error for the basic and to 47% of the uncorrected error for the advanced algorithm. These results demonstrate that our advanced motion correction algorithm has the potential to improve the reliability of quantitative measurements derived from 3D-OCT data substantially. (C) 2014 Optical Society of America

Quantitative accuracy of clinical 99mTc SPECT/CT using ordered-subset expectation maximization with 3-dimensional resolution recovery, attenuation, and scatter correction - Zeintl, Johannes; Vija, Alexander Hans; Yahil, Amos; Hornegger, Joachim; Kuwert, Torsten (Journal of Nuclear Medicine, 2010)
We present a calibration method of a clinical SPECT/CT device for quantitative Tc SPECT. We use a commercially available reconstruction package including ordered-subset expectation maximization (OSEM) with depth-dependent 3-dimensional resolution recovery (OSEM-3D), CT-based attenuation correction, and scatter correction. We validated the method in phantom studies and applied it to images from patients injected with Tc-diphosponate. Methods: The following 3 steps were performedtoderive absolute quantitative values from SPECT reconstructed images. In step 1, we used simulations to characterize the SPECT/CT system and derive emission recovery values for various imaging parameter settings. We simulated spheres of varying diameters and focused on the dependencies of activity estimation errors on structure size and position, pixel size, count density, and reconstruction parameters. In step 2, we cross-calibrated our clinical SPECT/CT system with the well counter using a large cylinder phantom. This step provided the mapping from image counts to kBq/mL. And in step 3, correction factors from steps 1 and 2 were applied to reconstructed images. We used a cylinder phantom with variable-sized spheres for verification of the method. For in vivo validation, SPECT/CT datasets from 16 patients undergoing Tc- diphosponate SPECT/CT examinations of the pelvis including the bladder were acquired. The radioactivity concentration in the patients' urine served as the gold standard. Mean quantitative accuracy and SEs were calculated. Results: In the phantom experiments, the mean accuracy in quantifying radioactivity concentration in absolute terms was within 3.6% (SE, 8.0%), with a 95% confidence interval between 219.4% and 112.2%. In the patient studies, the mean accuracy was within 1.1% (SE, 8.4%), with a 95% confidence interval between 215.4% and 117.5%. Conclusion: Current commercially available SPECT/CT technology using OSEM-3D reconstruction, scatter correction, and CT-based attenuation correction allows quantification of Tc radioactivity concentration in absolute terms within 3.6% in phantoms and 1.1% in patients with a focus on the bladder. This opens up the opportunity of SPECT quantitation entering the routine clinical arena. Still, the imprecision caused by unavoidable measurement errors is a dominant factor for absolute quantitation in a clinical setup. COPYRIGHT © 2010 by the Society of Nuclear Medicine, Inc.

Quantitative accuracy of slow-rotating dynamic SPECT - Zeintl, Johannes; Vija, A. H.; Yahil, A.; Hornegger, Joachim; Kuwert, T. (2009 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2009, 2009)
We investigated biases in time-activity measurements relevant for quantitative dynamic SPECT/CT imaging when slow-rotating dual-headed gamma camera systems in combination with OSEM-3D (Flash3D) with scatter and attenuation correction are used. The goal was to assess the potential and also the limitations of clinical dual-headed SPECT/CT systems for the quantification of dynamic processes with focus on a renal time-activity function. We used simulations of a SPECT/CT system to estimate absolute quantitation errors in time-activity measurements. We systematically assessed dependencies of these errors on signal to noise ratio and sampling frequencies using a MAG-3 renal time-activity profile. In addition, a physical phantom was developed to measure dynamic processes on a clinical SPECT/CT system. The phantom consisted of a cylindrical chamber placed in a large cylinder phantom and connected to a programmable peristaltic pump. SPECT/CT acquisitions were performed by varying the rotation times of the SPECT system. Absolute activity concentrations were calculated by cross calibrating the imaging system with a well counter and using correction factors derived from simulations. Results from simulations show no significant differences in emission recovery coefficients within the range of 7.5 to 120 seconds per rotation. Phantom experiments using corrections from cross calibration and simulation show average estimation errors of -0.9% and -4.5% for 10 seconds and 60 seconds per rotation, respectively. Conclusion: We showed that quantitation of a renal dynamic process in phantoms using multiple time-contiguous SPECT acquisitions with 3D iterative reconstruction is possible with an accuracy of 4.5%. ©2009 IEEE.

Quantitative analysis framework for SPECT•CT Tc-99m bone scintigraphy - Cachovan, Michal; Vija, A. H.; Hornegger, Joachim; Kuwert, Torsten (2011 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2011, 2012)
Clinical SPECT routine uses tedious manual image evaluation using simplistic region of interest statistical analysis without taking the quantification aspect into account. We introduce a software framework for quantitative analysis of 3D/4D SPECT•CT Tc-99m bone scintigraphy data that incorporates automatic analysis and osseous tissue healthiness classification based on a patient database annotated by an experienced physician. The usage workflow of our framework starts with the input of SPECT and CT data registered on each other. Using CT information we segment osseous tissue also in the co-registered SPECT dataset. Patient weight, injected dose and system calibration factor are read out of the input datasets in order to estimate tracer activity concentration (AC) in kBq/ml and standardized uptake value (SUV). Support vector machine method is used to classify tissue based on the reference patient database that contains quantitative data for normal and abnormal tissue. Evaluation of the framework was done on patient data acquired 3 hours post injection on a SPECT/CT Symbia T6 system using protocol for quantitative estimation of tracer activity. The acquired projection data was reconstructed using Ordered Subset Expectation Maximization with 3D resolution recovery (OSEM-3D) with attenuation and scatter correction. Data analysis showed a significant correlation between AC and bone density measured in Hounsfield Units (HU) for both male and female patients. The proposed methods demonstrate the ability to automatically quantify and distinguish abnormal tissue from healthy one and to increase physicians' diagnostic confidence. © 2011 IEEE.

Quantitative bias for SPECT imaging in kidneys due to respiratory motion - a simulation study - Sanders, James Chester; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas; Ritt, Philipp (Kongressausgabe 56. Jahrestagung der Deutschen Gesellschaft für Nuklearmedizin, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.quanti_2

Quantitative Bias from Respiratory Motion and Finite System Resolution for SPECT Imaging of Kidneys A Simulation Study - Sanders, James Chester; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas; Ritt, Philipp (Journal of Nuclear Medicine (Supplement 1), 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.quanti_6

Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye - Jia, Yali; Bailey, Steven T.; Hwang, Thomas S.; Mcclintic, Scott M.; Gao, Simon S.; Pennesi, Mark E.; Flaxel, Christina J.; Lauer, Andreas K.; Wilson, David J.; Hornegger, Joachim; Fujimoto, James G.; Huang, David (Proceedings of the National Academy of Sciences of the United States of America, 2015)
Retinal vascular diseases are important causes of vision loss. A detailed evaluation of the vascular abnormalities facilitates diagnosis and treatment in these diseases. Optical coherence tomography (OCT) angiography using the highly efficient split-spectrum amplitude decorrelation angiography algorithm offers an alternative to conventional dye-based retinal angiography. OCT angiography has several advantages, including 3D visualization of retinal and choroidal circulations (including the choriocapillaris) and avoidance of dye injection-related complications. Results from six illustrative cases are reported. In diabetic retinopathy, OCT angiography can detect neovascularization and quantify ischemia. In age-related macular degeneration, choroidal neovascularization can be observed without the obscuration of details caused by dye leakage in conventional angiography. Choriocapillaris dysfunction can be detected in the nonneovascular form of the disease, furthering our understanding of pathogenesis. In choroideremia, OCT's ability to show choroidal and retinal vascular dysfunction separately may be valuable in predicting progression and assessing treatment response. OCT angiography shows promise as a noninvasive alternative to dye-based angiography for highly detailed, in vivo, 3D, quantitative evaluation of retinal vascular abnormalities.
Keywords: optical coherence tomography angiography; ophthalmic imaging; ocular circulation

Quantitative SPECT/CT Imaging of Lu-177 with In Vivo Validation in Patients Undergoing Peptide Receptor Radionuclide Therapy - Sanders (III), James; Kuwert, Torsten; Hornegger, Joachim; Ritt, Philipp (Molecular Imaging and Biology, 2015)
UnivIS-Import:2015-04-14:Pub.2014.tech.IMMD.IMMD5.quanti
Keywords: Quantitative SPECT; [Lu-177]DOTATATE; Peptide receptor radionuclide therapy; Dosimetry

QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data - Schirrmacher, Franziska; Köhler, Thomas; Husvogt, Lennart; Fujimoto, James G.; Hornegger, Joachim; Maier, Andreas (Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017, Proceedings, Part II, 2017)
Optical coherence tomography (OCT) enables high-resolution and non-invasive 3D imaging of the human retina but is inherently impaired by speckle noise. This paper introduces a spatio-temporal denoising algorithm for OCT data on a B-scan level using a novel quantile sparse image (QuaSI) prior. To remove speckle noise while preserving image structures of diagnostic relevance, we implement our QuaSI prior via median filter regularization coupled with a Huber data fidelity model in a variational approach. For efficient energy minimization, we develop an alternating direction method of multipliers (ADMM) scheme using a linearization of median filtering. Our spatio-temporal method can handle both, denoising of single B-scans and temporally consecutive B-scans, to gain volumetric OCT data with enhanced signal-to-noise ratio. Our algorithm based on 4 B-scans only achieved comparable performance to averaging 13 B-scans and outperformed other current denoising methods.
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.quasiq_2

Quiet diffusion-weighted head scanning: Initial clinical evaluation in ischemic stroke patients at 1.5T - Rösch, Julie; Ott, Martin; Heismann, Bjoern; Dörfler, Arnd; Engelhorn, Tobias; Sembritzki, Klaus; Grodzki, David M. (Journal of Magnetic Resonance Imaging, 2016)
PURPOSE: To compare the quality and diagnostic value of routine single-shot, echo-planar imaging, diffusion-weighted imaging (ss-EPI-DWI) to those of quiet readout segmented EPI-DWI (q-DWI) in magnetic resonance imaging (MRI) of acute stroke. MATERIALS AND METHODS: Twenty-six patients with acute stroke underwent a 1.5T MRI including diffusion-weighted ss-EPI and q-DWI. The two sequences were protocolled to have identical spatial resolution and spatial coverage. q-DWI was tested with (regular q-DWI) and without (fast q-DWI) averaging in 13 patients each. The acoustic noise generated by each sequence was measured. Quantitative and qualitative assessments regarding signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), lesion conspicuity, level of artifacts, overall image quality as well as diagnostic content were performed. RESULTS: SNR and CNR values of the q-DWI scans were considerably higher than those of ss-EPI DWI (P ≤ 0.0078). No statistical difference was found for lesion conspicuity (P ≥ 0.125). Statistical differences were found for level of artifacts (P ≥ 0.0078) and overall image quality (P ≥ 0.002). Both were evaluated better in the ss-EPI DWI than in the regular and fast q-DWI. Apart from one fast q-DWI patient, radiologists voted the images to have the same diagnostic content, with upper 90% confidence limits of 0.238 for regular q-DWI and 0.429 for fast q-DWI. CONCLUSION: If the acoustic burden is critical to the patient, q-DWI is an equivalent quiet alternative to ss-EPI DWI for use in stroke patients. J. Magn. Reson. Imaging 2016;44:1238-1243.
EVALuna2:33143

Radial Basis Function Interpolation for Rapid Interactive Segmentation of 3-D Medical Images - Mirshahzadeh, Negar; Kurzendorfer, Tanja; Fischer, Peter; Pohl, Thomas; Brost, Alexander; Steidl, Stefan; Maier, Andreas (MIUA 2017: Medical Image Understanding and Analysis, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.radial
Keywords: 3-D interpolation; HRBF; Segmentation; Smart brush

Random Forest Based Left Ventricle Segmentation in LGE-MRI - Kurzendorfer, Tanja; Forman, Christoph; Brost, Alexander; Maier, Andreas (Functional Imaging and Modelling of the Heart, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.random

Range Imaging for Motion Compensation in C-Arm Cone-Beam CT of Knees under Weight-Bearing Conditions - Bier, Bastian; Ravikumar, Nishant; Unberath, Mathias; Levenstone, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Journal of Imaging, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.rangei

Rapid freehand MR-guided percutaneous needle interventions: An image-based approach to improve workflow and feasibility - Rothgang, Eva; Gilson, Wesley D.; Wacker, Frank; Hornegger, Joachim; Lorenz, Christine H.; Weiss, Clifford R. (Journal of Magnetic Resonance Imaging, 2013)
Purpose: To develop and evaluate software-based methods for improving the workflow of magnetic resonance (MR)-guided percutaneous interventions. Materials and Methods: A set of methods was developed that allows the user to: 1) plan an entire procedure, 2) directly apply this plan to skin entry site localization without further imaging, and 3) place a needle under real-time MR guidance with automatic alignment of three orthogonal slices along a planned trajectory with preference to the principal patient axes. To validate targeting accuracy and time, phantom experiments (96 targets) and in vivo paraspinal and kidney needle punctures in two pigs (55 targets) were performed. The influence of trajectory obliquity, level of experience, and organ motion on targeting accuracy and time was analyzed. Results: Mean targeting error was 1.8 ± 0.9 mm (in vitro) and 2.9 ± 1.0 mm (in vivo) in all directions. No statistically significant differences in targeting accuracy between single- and double-oblique trajectories, novice and expert users, or paraspinal and kidney punctures were observed. The average time (in vivo) from trajectory planning to verification of accurate needle placement was 6 minutes. Conclusion: The developed methods allow for accurate needle placement along complex trajectories and are anticipated to reduce table time for MR-guided percutaneous needle interventions. © 2013 Wiley Periodicals, Inc.

Rapid Interactive and Intuitive Segmentation of 3D Medical Images Using Radial Basis Function Interpolation - Kurzendorfer, Tanja; Fischer, Peter; Mirshahzadeh, Negar; Pohl, Thomas; Brost, Alexander; Steidl, Stefan; Maier, Andreas (Journal of Imaging, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.rapidi

Ray contribution masks for structure adaptive sinogram filtering - Balda, Michael; Hornegger, Joachim; Heismann, Björn (IEEE Transactions on Medical Imaging, 2012)
The patient dose in computed tomography (CT) imaging is linked to measurement noise. Various noise-reduction techniques have been developed that adapt structure preserving filters like anisotropic diffusion or bilateral filters to CT noise properties. We introduce a structure adaptive sinogram (SAS) filter that incorporates the specific properties of the CT measurement process. It uses a point-based forward projector to generate a local structure representation called ray contribution mask (RCM). The similarities between neighboring RCMs are used in an enhanced variant of the bilateral filtering concept, where the photometric similarity is replaced with the structural similarity. We evaluate the performance in four different scenarios: The robustness against reconstruction artifacts is demonstrated by a scan of a high-resolution-phantom. Without changing the modulation transfer function (MTF) nor introducing artifacts, the SAS filter reduces the noise level by 13.6%. The image sharpness and noise reduction capabilities are visually assessed on in vivo patient scans and quantitatively evaluated on a simulated phantom. Unlike a standard bilateral filter, the SAS filter preserves edge information and high-frequency components of organ textures well. It shows a homogeneous noise reduction behavior throughout the whole frequency range. The last scenario uses a simulated edge phantom to estimate the filter MTF for various contrasts: the noise reduction for the simple edge phantom exceeds 80%. For low contrasts at 55 Hounsfield units (HU), the mid-frequency range is slightly attenuated, at higher contrasts of approximately 100 HU and above, the MTF is fully preserved. © 2012 IEEE.

Real-Time-Capable GPU-Framework for Depth-Aware Rigid 2D/3D Registration - Utzschneider, Matthias; Wang, Jian; Schaffert, Roman; Borsdorf, Anja; Maier, Andreas (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.realti

Real-time circumferential mapping catheter tracking for motion compensation in atrial fibrillation ablation procedures - Brost, Alexander; Bourier, Felix; Wimmer, Andreas; Koch, Martin; Kiraly, Atilla; Liao, Rui; Kurzidim, Klaus; Hornegger, Joachim; Strobel, Norbert (Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 2012)
Atrial fibrillation (AFib) has been identified as a major cause of stroke. Radiofrequency catheter ablation has become an increasingly important treatment option, especially when drug therapy fails. Navigation under X-ray can be enhanced by using augmented fluoroscopy. It renders overlay images from pre-operative 3-D data sets which are then fused with X-ray images to provide more details about the underlying soft-tissue anatomy. Unfortunately, these fluoroscopic overlay images are compromised by respiratory and cardiac motion. Various methods to deal with motion have been proposed. To meet clinical demands, they have to be fast. Methods providing a processing frame rate of 3 frames-per-second (fps) are considered suitable for interventional electrophysiology catheter procedures if an acquisition frame rate of 2 fps is used. Unfortunately, when working at a processing rate of 3 fps, the delay until the actual motion compensated image can be displayed is about 300 ms. More recent algorithms can achieve frame rates of up to 20 fps, which reduces the lag to 50 ms. By using a novel approach involving a 3-D catheter model, catheter segmentation and a distance transform, we can speed up motion compensation to 25 fps which results in a display delay of only 40 ms on a standard workstation for medical applications. Our method uses a constrained 2-D/3-D registration to perform catheter tracking, and it obtained a 2-D tracking error of 0.61 mm. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices - Gradl, Stefan; Kugler, Patrick; Lohmüller, Clemens; Eskofier, Björn (Engineering in Medicine and Biology Society, EMBC, 2012, 2012)
We developed an application for Android™-based mobile devices that allows real-time electrocardiogram (ECG) monitoring and automated arrhythmia detection by analyzing ECG parameters. ECG data provided by pre-recorded files or acquired live by accessing a Shimmer™ sensor node via Bluetooth™ can be processed and evaluated. The application is based on the Pan-Tompkins algorithm for QRS-detection and contains further algorithm blocks to detect abnormal heartbeats. The algorithm was validated using the MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia databases. More than 99% of all QRS complexes were detected correctly by the algorithm. Overall sensitivity for abnormal beat detection was 89.5% with a specificity of 80.6%. The application is available for download and may be used for real-time ECG-monitoring on mobile devices. © 2012 IEE

Real-time motion compensated patient positioning and non-rigid deformation estimation using 4-D shape priors - Wasza, Jakob; Bauer, Sebastian; Hornegger, Joachim (Lecture Notes in Computer Science, 2012)
Over the last years, range imaging (RI) techniques have been proposed for patient positioning and respiration analysis in motion compensation. Yet, current RI based approaches for patient positioning employ rigid-body transformations, thus neglecting free-form deformations induced by respiratory motion. Furthermore, RI based respiration analysis relies on non-rigid registration techniques with run-times of several seconds. In this paper we propose a real-time framework based on RI to perform respiratory motion compensated positioning and non-rigid surface deformation estimation in a joint manner. The core of our method are pre-procedurally obtained 4-D shape priors that drive the intra-procedural alignment of the patient to the reference state, simultaneously yielding a rigid-body table transformation and a free-form deformation accounting for respiratory motion. We show that our method outperforms conventional alignment strategies by a factor of 3.0 and 2.3 in the rotation and translation accuracy, respectively. Using a GPU based implementation, we achieve run-times of 40 ms.

Real-time Motion Compensated Patient Positioning and Non-rigid Deformation Estimation using 4-D Shape Priors - Wasza, Jakob; Bauer, Sebastian; Hornegger, Joachim (MICCAI 2012, Part II, LNCS 7511, 2012)
Over the last years, range imaging (RI) techniques have been proposed for patient positioning and respiration analysis in motion compensation. Yet, current RI based approaches for patient positioning employ rigid-body transformations, thus neglecting free-form deformations induced by respiratory motion. Furthermore, RI based respiration analysis relies on non-rigid registration techniques with run-times of several seconds. In this paper we propose a real-time framework based on RI to perform respiratory motion compensated positioning and non-rigid surface deformation estimation in a joint manner. The core of our method are pre-procedurally obtained 4-D shape priors that drive the intra-procedural alignment of the patient to the reference state, simultaneously yielding a rigid-body table transformation and a free-form deformation accounting for respiratory motion. We show that our method outperforms conventional alignment strategies by a factor of 3.0 and 2.3 in the rotation and translation accuracy, respectively. Using a GPU based implementation, we achieve run-times of 40 ms.
UnivIS-Import:2015-04-16:Pub.2012.tech.IMMD.IMMD5.realti_8

Real-Time Optical Motion Correction for Diffusion Tensor Imaging - Aksoy, Murat; Forman, Christoph; Straka, Matus; Skare, Stefan; Holdsworth, Samantha; Hornegger, Joachim; Bammer, Roland (Magnetic Resonance in Medicine, 2011)
Head motion is a fundamental problem in brain MRI. The problem is further compounded in diffusion tensor imaging because of long acquisition times, and the sensitivity of the tensor computation to even small misregistration. To combat motion artifacts in diffusion tensor imaging, a novel real-time prospective motion correction method was introduced using an in-bore monovision system. The system consists of a camera mounted on the head coil and a self-encoded checkerboard marker that is attached to the patient's forehead. Our experiments showed that optical prospective motion correction is more effective at removing motion artifacts compared to image-based retrospective motion correction. Statistical analysis revealed a significant improvement in similarity between diffusion data acquired at different time points when prospective correction was used compared to retrospective correction (P < 0.001). © 2011 Wiley-Liss, Inc.
UnivIS-Import:2015-04-14:Pub.2011.tech.IMMD.IMMD5.realti
Keywords: motion; DTI; diffusion; real-time motion correction; optical motion correction; prospective motion correction

Real-time preprocessing for dense 3-D range imaging on the GPU: Defect interpolation, bilateral temporal averaging and guided filtering - Wasza, Jakob; Bauer, Sebastian; Hornegger, Joachim (2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011, 2011)
Recent advances in range imaging (RI) have enabled dense 3-D scene acquisition in real-time. However, due to physical limitations and the underlying range sampling principles, range data are subject to noise and may contain invalid measurements. Hence, data preprocessing is a prerequisite for practical applications but poses a challenge with respect to real-time constraints. In this paper, we propose a generic and modality-independent pipeline for efficient RI data preprocessing on the graphics processing unit (GPU). The contributions of this work are efficient GPU implementations of normalized convolution for the restoration of invalid measurements, bilateral temporal averaging for dynamic scenes, and guided filtering for edge-preserving denoising. Furthermore, we show that the transformation from range measurements to 3-D world coordinates can be computed efficiently on the GPU. The pipeline has been evaluated on real data from a Time-of-Flight sensor and Microsoft's Kinect. In a run-time performance study, we show that for VGA-resolution data, our preprocessing pipeline runs at 100 fps on an off-the-shelf consumer GPU. © 2011 IEEE.

Real-Time Range Imaging in Health Care: A Survey - Bauer, Sebastian; Seitel, Alexander; Hofmann, Hannes; Blum, Tobias; Wasza, Jakob; Balda, Michael; Meinzer, Hans-Peter; Navab, Nassir; Hornegger, Joachim; Maier-Hein, Lena (Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications, 2013)
The recent availability of dynamic, dense, and low-cost range imaging has gained widespread interest in health care. It opens up new opportunities and has an increasing impact on both research and commercial activities. This chapter presents a state-of-the-art survey on the integration of modern range imaging sensors into medical applications. The scope is to identify promising applications and methods, and to provide an overview of recent developments in this rapidly evolving domain. The survey covers a broad range of topics, including guidance in computer-assisted interventions, operation room monitoring and workflow analysis, touch-less interaction and on-patient visualization, as well as prevention and support in elderly care and rehabilitation. We put emphasis on dynamic and interactive tasks where real-time and dense 3-D imaging forms the key aspect. While considering different range imaging modalities that fulfill these requirements, we particularly investigate the impact of Time-of-Flight imaging in this domain. Eventually, we discuss practical demands and limitations, and open research issues and challenges that are of fundamental importance for the progression of the field. © Springer-Verlag 2013.
UnivIS-Import:2015-04-20:Pub.2013.tech.IMMD.IMMD5.realti_6

Real-time Recognition of the Affective User State with Physiological Signals - Hönig, Florian Thomas; Batliner, Anton; Nöth, Elmar (The Second International Conference on Affective Computing and Intelligent Interaction, Proceedings of the Doctoral Consortium, 2007)

Real-Time Respiratory Motion Analysis Using 4-D Shape Priors - Wasza, Jakob; Fischer, Peter; Leutheuser, Heike; Oefner, Tobias; Bert, Christoph; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Biomedical Engineering, 2016)
Respiratory motion analysis based on range imaging (RI) has emerged as a popular means of generating respiration surrogates to guide motion management strategies in computer-assisted interventions. However, existing approaches employ heuristics, require substantial manual interaction, or yield highly redundant information. In this paper, we propose a framework that uses preprocedurally obtained 4-D shape priors from patient-specific breathing patterns to drive intraprocedural RI-based real-time respiratory motion analysis. As the first contribution, we present a shape motion model enabling an unsupervised decomposition of respiration induced high-dimensional body surface displacement fields into a low-dimensional representation encoding thoracic and abdominal breathing. Second, we propose a method designed for GPU architectures to quickly and robustly align our models to high-coverage multiview RI body surface data. With our fully automatic method, we obtain respiration surrogates yielding a Pearson correlation coefficient (PCC) of 0.98 with conventional surrogates based on manually selected regions on RI body surface data. Compared to impedance pneumography as a respiration signal that measures the change of lung volume, we obtain a PCC of 0.96. Using off-the-shelf hardware, our framework enables high temporal resolution respiration analysis at 50 Hz.
Keywords: Graphics processing unit (GPU); radiation therapy; range imaging (RI); respiratory motion

Real-time Respiratory Motion Analysis Using GPU-accelerated Range Imaging - Wasza, Jakob (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Respiratory motion analysis and management are crucial issues for a plurality of medical applications. Of particular scientific concern are methods that allow to analyze the patient’s breathing in a non-invasive and real-time manner that does not involve ionizing radiation exposure. For this purpose, range imaging technologies, that allow to dynamically acquire three-dimensional body surface data, have been proposed over the last years. A particular challenge with such methods is a fully automatic investigation and assessment of the body surface data, as well as computation times that comply with real-time constraints. This dissertation is concerned with the application of range imaging principles for real-time automatic respiratory motion analysis. The focus is on the development of efficient methods for data preprocessing and fusion as well as machine learning and surface registration techniques. A particular emphasis of this thesis is the design of the proposed algorithms for GPU architectures to enable real-time computation. The first part of this thesis covers the general challenges and requirements for respiratory motion analysis in diagnostic and therapeutic applications. Furthermore, the range imaging technologies that are relevant for this thesis are introduced and the suitability of GPU architectures for accelerating the computation of several tasks inherent to range imaging based respiratory motion analysis are investigated. The second part of this work is concerned with pre-processing and fusion techniques for range data. To account for the low signal-to-noise ratio that is common with range data, this work proposes a processing pipeline that allows to reconstruct the ideal data with an error trueness less than 1.0mm at run-times of 2 ms. For fusing range image data in a multi-camera setup, as it is required for the simultaneous acquisition of frontal and lateral body surface, this thesis proposes a novel framework that enables the computation of a 180° coverage body surface model consisting of more than 3.0 x 105 points with a computation time of less than 5 ms. The third part of this work is concerned with patient-specific respiratory motion models. The thesis proposes machine learning techniques to generate a continuous motion model that features the ability to automatically differentiate between thoracic and abdominal breathing as well as to quantitatively analyze the patient’s respiration magnitude. By using purposely developed surface registration schemes, these models are then brought in congruence with body surface data acquired by range imaging sensors. This allows for respiratory motion compensated patient positioning that reduces the alignment error observed with conventional approaches by a factor of 3 to less than 4.0mm. Further, this approach allows to automatically derive a multi-dimensional respiration surrogate that yields a correlation coefficient greater than 0.97 compared to commonly employed invasive or semi-automatic approaches and that can be computed in 20 ms. The fourth part concludes this thesis with a summary of the presented methods and results, as well as an outlook regarding future research directions and challenges towards clinical translation.<br

Real-Time Respiratory Motion Analysis Using Manifold Ray Casting of Volumetrically Fused Multi-view Range Imaging - Wasza, Jakob; Bauer, Sebastian; Hornegger, Joachim (MICCAI 2013, Part II, LNCS 8150 (16th International Conference on Medical Image Computing and Computer Assisted Intervention), 2013)

Real-Time Respiratory Signal Extraction from X-Ray Sequences using Incremental Manifold Learning - Fischer, Peter; Pohl, Thomas; Hornegger, Joachim (2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.realti_8

Real-Time RGB-D Mapping and 3-D Modeling on the GPU Using the Random Ball Cover - Bauer, Sebastian; Wasza, Jakob; Lugauer, Felix; Neumann, Dominik; Hornegger, Joachim (Consumer Depth Cameras for Computer Vision, 2013)

Real-time RGB-D mapping and 3-D modeling on the GPU using the random ball cover data structure - Neumann, Dominik; Lugauer, Felix; Bauer, Sebastian; Wasza, Jakob; Hornegger, Joachim (2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011, 2011)
The modeling of three-dimensional scene geometry from temporal point cloud streams is of particular interest for a variety of computer vision applications. With the advent of RGB-D imaging devices that deliver dense, metric and textured 6-D data in real-time, on-the-fly reconstruction of static environments has come into reach. In this paper, we propose a system for real-time point cloud mapping based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). In order to achieve robust mappings at real-time performance, our nearest neighbor search evaluates both geometric and photometric information in a direct manner. For acceleration of the search space traversal, we exploit the inherent computing parallelism of GPUs. In this work, we have investigated the fitness of the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, for 6-D data. In particular, we introduce a scheme that enables both fast RBC construction and queries. The proposed system is validated on an indoor scene modeling scenario. For dense data from the Microsoft Kinect sensor (640×480 px), our implementation achieved ICP runtimes of< 20 ms on an off-the-shelf consumer GPU. © 2011 IEEE.

Real-time Simulation of Human Vision using Temporal Compositing with CUDA on the GPU - Nießner, Matthias; Kuhnert, Nadine; Selgrad, Kai; Stamminger, Marc; Michelson, Georg (Proceedings 25th Workshop on Parallel Systems and Algorithms (PARS 2013), 2013)
We present a novel approach that simulates human vision including visual defects such as glaucoma by temporal composition of human vision in real-time on the GPU. Therefore, we determine eye focus points every time step and adapt the lens accommodation of our virtual eye model accordingly. The focal distance is then used to determine bluriness of observed scene regions; i.e., we compute defocus for all visible pixels. In order to simulate the visual memory we introduce a sharpness field where we integrate defocus values temporally. That allows for memorizing sharply perceived scene points. For visualization, we ray trace the virtual scene environment while incorporating depth of field based on the sharpness field data. Thus, our algorithm facilitates the simulation of human vision mimicing the visual memory. We consider this to be particularly useful for illustration purposes for patients with visual defects such as glaucoma. In order to run our algorithm in real-time we employ massively parallel graphics hardware.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD9.realti_9
Keywords: GRK-1773

Real time surveying and monitoring of Athletes Using Mobile Phones and GPS - Eskofier, Björn; Hartmann, Elmar; Kühner, Pascal; Griffin, Janet; Schlarb, Heiko; Schmitt, Manfred; Hornegger, Joachim (International Journal of Computer Science in Sport, 2008)

Recent Machine Learning Advancements in Sensor-Based Mobility Analysis: Deep Learning for Parkinson’s Disease Assessment - Eskofier, Björn; Lee, Sunghoon I.; Daneault, Jean-Francois; Golabchi, Fatemeh N.; Ferreira-Carvalho, Gabriela; Vergara-Diaz, Gloria; Sapienza, Stefano; Costante, Gianluca; Klucken, Jochen; Kautz, Thomas; Bonato, Paolo (Proceedings of the 38th IEEE Engineering in Medicine and Biology Society Conference (EMBC 2016), 2016)
The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson’s disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge - Schuller, Björn; Batliner, Anton; Steidl, Stefan; Seppi, Dino (Speech Communication, 2011)
More than a decade has passed since research on automatic recognition of emotion from speech has become a new eld of research in line with its `big brothers' speech and speaker recognition. This article attempts to provide a short overview on where we are today, how we got there and what this can reveal us on where to go next and how we could arrive there. In a rst part, we address the basic phenomenon reflecting the last fteen years, commenting on databases, modelling and annotation, the unit of analysis and prototypicality. We then shift to automatic processing including discussions on features, classification, robustness, evaluation, and implementation and system integration. From there we go to the first comparative challenge on emotion recognition from speech - the INTERSPEECH 2009 Emotion Challenge, organised by (part of) the authors, including the description of the Challenge's database, Sub-Challenges, participants and their approaches, the winners, and the fusion of results to the actual learnt lessons before we finally address the ever-lasting problems and future promising attempts.
Keywords: emotion; affect; automatic classification; feature types; feature selection; noise robustness; adaptation; standardisation; usability; evaluation

Recognition and labelling of prosodic events in Slovenian speech - Mihelic, France; Gros, Jerneia; Nöth, Elmar; Warnke, Volker (Springer-verlag, 2000)
The paper describes prosodic annotation procedures of the GOPOLIS Slovenian speech data database and methods for automatic classification of different prosodic events. Several statistical parameters concerning duration and loudness of words, syllables and allophones were computed for the Slovenian language, for the first time on such a large amount of speech data. The evaluation of the annotated data showed a close match between automatically determined syntactic-prosodic boundary marker positions and those obtained by a rule-based approach.

Recognition of Non-Prototypical Emotions in Reverberated and Noisy Speech by Non-Negative Matrix Factorization - Weninger, Felix; Schuller, Björn; Batliner, Anton; Steidl, Stefan; Seppi, Dino (EURASIP Journal on Advances in Signal Processing, 2011)
We present a comprehensive study on the effect of reverberation and background noise on the recognition of nonprototypical emotions from speech. We carry out our evaluation on a single, well-defined task based on the FAU Aibo Emotion Corpus consisting of spontaneous children's speech, which was used in the INTERSPEECH 2009 Emotion Challenge, the first of its kind. Based on the challenge task, and relying on well-proven methodologies from the speech recognition domain, we derive test scenarios with realistic noise and reverberation conditions, including matched as well as mismatched condition training. As feature extraction based on supervised Nonnegative Matrix Factorization (NMF) has been proposed in automatic speech recognition for enhanced robustness, we introduce and evaluate different kinds of NMF-based features for emotion recognition. We conclude that NMF features can significantly contribute to the robustness of state-of-the-art emotion recognition engines in practical application scenarios where different noise and reverberation conditions have to be faced.

Reconstruction from truncated projections in cone-beam CT using an efficient 1D filtering - Xia, Yan; Maier, Andreas; Hofmann, Hannes; Dennerlein, Frank; Müller, Kerstin; Hornegger, Joachim (Medical Imaging 2013: Physics of Medical Imaging, 2013)
In X-ray imaging, a reduction of the field of view (FOV) is proportional to a reduction in radiation dose. The resulting truncation, however, is incompatible with conventional tomographic reconstruction algorithms. This problem has been studied extensively. Very recently, a novel method for region of interest (ROI) reconstruction from truncated projections with neither the use of prior knowledge nor explicit extrapolation has been published, named Approximated Truncation Robust Algorithm for Computed Tomography (ATRACT). It is based on a decomposition of the standard ramp filter into a 2D Laplace filtering (local operation) and a 2D Radon-based filtering step (non-local operation). The 2D Radon-based filtering that involves many interpolations complicates the filtering procedure in ATRACT, which essentially limits its practicality. In this paper, an optimization for this shortcoming is presented. That is to apply ATRACT in one dimension, which implies that we decompose the standard ramp filter into the 1D Laplace filter and a 1D convolutionbased filter. The convolution kernel was determined numerically by computing the 1D impulse response of the standard ramp filtering coupled with the second order anti-derivative operation. The proposed algorithm was evaluated by using a reconstruction benchmark test, a real phantom and a clinical data set in terms of spatial resolution, computational efficiency as well as robustness of correction quality. The evaluation outcomes were encouraging. The proposed algorithm showed improvement in computational performance with respect to the 2D ATRACT algorithm and furthermore maintained reconstructions of high accuracy in presence of data truncation. © 2013 SPIE.

Reconstruction method for curvilinear structures from two views - Hoffmann, Matthias; Brost, Alexander; Jakob, Carolin; Koch, Martin; Bourier, Felix; Kurzidim, Klaus; Hornegger, Joachim; Strobel, Norbert (Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 2013)

Reconstruction of scalar and vectorial components in X-ray dark-field tomography - Bayer, Florian; Hu, Shiyang; Maier, Andreas; Weber, Thomas; Anton, Gisela; Michel, Thilo; Riess, Christian (Proceedings of the National Academy of Sciences of the United States of America, 2014)
Grating-based X-ray dark-field imaging is a novel technique for obtaining image contrast for object structures at size scales below setup resolution. Such an approach appears particularly beneficial for medical imaging and nondestructive testing. It has already been shown that the dark-field signal depends on the direction of observation. However, up to now, algorithms for fully recovering the orientation dependence in a tomographic volume are still unexplored. In this publication, we propose a reconstruction method for grating-based X-ray dark-field tomography, which models the orientation- dependent signal as an additional observable from a standard tomographic scan. In detail, we extend the tomographic volume to a tensorial set of voxel data, containing the local orientation and contributions to dark-field scattering. In our experiments, we present the first results of several test specimens exhibiting a heterogeneous composition in microstructure, which demonstrates the diagnostic potential of the metho
UnivIS-Import:2015-04-14:Pub.2014.nat.dphy.PI.LEPDF.recons
Keywords: Anisotropic scattering; Grating interferometer; Microstructure orientation; X-ray phase contrast

Reconstruction of Voxels with Position- and Angle-Dependent Weightings - Felsner, Lina; Würfl, Tobias; Syben-Leisner, Christopher; Roser, Philipp; Preuhs, Alexander; Maier, Andreas; Riess, Christian (The 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020)

Reconstruction Techniques for Dynamic Radial MRI - Grimm, Robert (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2015)
Today, magnetic resonance imaging (MRI) is an essential clinical imaging modality and routinely used for orthopedic, neurological, cardiovascular, and oncological diagnosis. The relatively long scan times lead to two limitations in oncological MRI. Firstly, in dynamic contrast-enhanced MRI (DCE-MRI), spatial and temporal resolution have to be traded off against each other. Secondly, conventional acquisition techniques are highly susceptible to motion artifacts. As an example, in DCE-MRI of the liver, the imaging volume spans the whole abdomen and the scan must take place within a breath-hold to avoid respiratory motion. Dynamic imaging is achieved by performing multiple breath-hold scans before and after the injection of contrast agent. In practice, this requires patient cooperation, exact timing of the contrast agent injection, and limits the temporal resolution to about 10 seconds. This thesis addresses both challenges by combining a radial k-space sampling technique with advanced reconstruction algorithms for higher temporal resolution and improved respiratory motion management. A novel reconstruction technique, golden-angle radial sparse parallel MRI (GRASP), enables performing DCE-MRI at simultaneously high spatial and temporal resolution. Iterative gradient-based and alternating optimization techniques were implemented and evaluated. GRASP is based on a single, continuous scan during free breathing, allowing for a simplified and more patient-friendly examination workflow. The technique is augmented by an automatic detection of the contrast agent bolus arrival and by incorporating variable temporal resolution. These proposed extensions reduce the number of generated image volumes, resulting in faster reconstruction and post-processing. The radial trajectory also allows to extract a respiratory signal directly from the scan data. This self-gating property can be used for dynamic imaging in such a way that different, time-averaged phases of respiration are retrospectively reconstructed from a free-breathing scan. Automated algorithms for deriving, processing, and applying the self-gating signal were developed. The clinical relevance of self-gating was demonstrated by generating a motion model to correct for respiratory motion in a simultaneous positron emission tomography (PET) examination on hybrid PET/MRI scanners. This approach reduces the motion blur and, thus, improves tracer uptake quantification in moving lesions, while avoiding an increased noise level as it would be the case for conventional gating techniques. In conclusion, the presented advanced reconstruction techniques help to improve the spatio-temporal resolution as well as the robustness with respect to motion of dynamic radial MRI. The effectiveness of the proposed methods was supported by numerous studies in patient settings, showing that non-Cartesian k-space sampling can be advantageous in a variety of applications.<br
UnivIS-Import:2016-07-26:Pub.2015.tech.IMMD.IMMD5.recons
Keywords: MRT; 3D-Rekonstruktion; Medizinische Bildgebung; Positronen-Emissions-Tomographie; Onkologie

Rectification of Endoscopic Images by Gravity - Höller, Kurt Emmerich; Hempel, Sarah-Marie; Schneider, Armin; Jahn, Jasper; Gutierrez, Javier; Wittenberg, Thomas; Hornegger, Joachim; Feußner, Hubertus (Proc. Pattern Recognition in Medical and Health Engineering ’10, 2010)

Recursive Convolutional Neural Networks for Epigenomics - Symeonidi, Aikaterini; Nicolaou, Anguelos; Johannes, Frank; Christlein, Vincent (25th International Conference on Pattern Recognition (ICPR), 2021)
Deep learning methods have proved to be powerful classification tools in the fields of structural and functional genomics. In this paper, we introduce Recursive Convolutional Neural Networks (RCNN) for the analysis of epigenomic data. We focus on the task of predicting gene expression from the intensity of histone modifications. The proposed RCNN architecture can be applied to data of an arbitrary size, and has a single meta-parameter that quantifies the models capacity, thus making it flexible for experimenting. The proposed architecture outperforms state-of-the-art systems, while having several orders of magnitude fewer parameters.<br
Keywords: Deep learning; Genomics; Tools; Data models; Pattern recognition; Convolutional neural networks; Gene expression

Reduction of Metal Artifacts using a New Segmentation Approach - Kuhnert, Nadine; Barth, Karl; Maass, Nicole; Maier, Andreas (Bildverarbeitung für die Medizin, 2016)
Metal artifact reduction (MAR) is crucial for the diagnostic value, as metal artifacts tremendously impair the image quality of a CT scan. Wang et al. [1] presented an iterative MAR approach, whereas Kachelriess et al. [2] proposed a filtering method to reduce metal artifacts. However, both techniques are very time-consuming. Most MAR methods contain a metal object segmentation step and the resulting image quality highly depends on the validity of the segmentation [3]. However, segmenting the metal parts correctly poses a non-trivial problem due to the metal artifacts. We present a novel approach of an automatic, object independent segmentation which starts with the state-of-the-art segmentation. This is improved by applying graph cut [4] onto every projection. We extend the graph cut idea by more information and apply knowledge about the distance, a classification probability and a bias to the edges as well as a similarity measure of pixels to their direct neighbors. By additionally considering global consistency, we receive a more precise segmentation result. For the evaluation, our new segmentation approach was combined with the frequency split MAR (FSMAR) [5]. The resulting CT images yielded higher image quality compared with the standard threshold-based FSMAR.
Keywords: Segmentation Result; Metal Artifact; Metal Object; Segmentation Approach; Initial Segmentation

Reduction of metal artifacts using a new segmentation approach: Extension of graph cuts for a more precise segmentation used in metal artifact reduction - Kuhnert, Nadine; Maass, Nicole; Barth, Karl; Maier, Andreas (Informatik aktuell, 2017)
Metal artifact reduction (MAR) is crucial for the diagnostic value, as metal artifacts tremendously impair the image quality of a CT scan. Existing techniques are time-consuming. Most MAR methods contain a metal object segmentation step and the resulting image quality highly depends on the validity of the segmentation. However, segmenting the metal parts correctly still poses a non-trivial problem. We present a novel approach of an automatic, object independent segmentation which starts with the state-of-the-art segmentation. This is improved by applying graph cut onto every projection. We extend the graph cut idea by more information and apply knowledge about the distance, a classification probability and a bias to the edges as well as a similarity measure of pixels to their direct neighbors. By additionally considering global consistency, we receive a more precise segmentation result. For the evaluation, our new segmentation approach was combined with the frequency split MAR (FSMAR). The resulting CT images yielded higher image quality compared with the standard threshold-based FSMAR.
CRIS-Team Scopus Importer:2022-06-05

Reduction of Respiratory Motion Artifacts for Free-Breathing Whole-Heart Coronary MRA by Weighted Iterative Reconstruction - Forman, Christoph; Piccini, Davide; Grimm, Robert; Hutter, Jana; Hornegger, Joachim; Zenge, Michael O. (Magnetic Resonance in Medicine, 2014)
Purpose: To combine weighted iterative reconstruction with self-navigated free-breathing coronary magnetic resonance angiography for retrospective reduction of respiratory motion artifacts. Methods: One-dimensional self-navigation was improved for robust respiratory motion detection and the consistency of the acquired data was estimated on the detected motion. Based on the data consistency, the data fidelity term of iterative reconstruction was weighted to reduce the effects of respiratory motion. In vivo experiments were performed in 14 healthy volunteers and the resulting image quality of the proposed method was compared to a navigator-gated reference in terms of acquisition time, vessel length, and sharpness. Result: Although the sampling pattern of the proposed method contained 60% more samples with respect to the reference, the scan efficiency was improved from 39.5±10.1% to 55.1±9.1%. The improved self-navigation showed a high correlation to the standard navigator signal and the described weighting efficiently reduced respiratory motion artifacts. Overall, the average image quality of the proposed method was comparable to the navigator-gated reference. Conclusion: Self-navigated coronary magnetic resonance angiography was successfully combined with weighted iterative reconstruction to reduce the total acquisition time and efficiently suppress respiratory motion artifacts. The simplicity of the experimental setup and the promising image quality are encouraging toward future clinical evaluation. © 2014 Wiley Periodicals, Inc.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.reduct
Keywords: Compressed sensing; Coronary magnetic resonance angiography; Motion suppression; Weighted iterative reconstruction

Reduktion von Metallartefakten durch multipositionale Datenfusion in der industriellen Röntgen-Computertomographie - Herl, Gabriel; Hiller, Jochen; Kasperl, Stefan; Maier, Andreas (Technisches Messen, 2019)
Zusammenfassung Metallartefakte stellen eine große Herausforderung für das Messen mit Röntgen-Computertomographie dar. Dieser Beitrag stellt die Methode der multipositionalen Datenfusion zur Reduktion von Metallartefakten vor. Dazu werden mehrere Scans desselben Objekts bei unterschiedlicher Objekt-positionierung durchgeführt, aufeinander registriert und zur Fusion gemeinsam unter Betrachtung eines lokalen Gütemaßes rekonstruiert. In praxisnahen Experimenten wird der Mehrwert der Methode gezeigt. Insbesondere wird dargestellt, wie mit wenig Aufwand und ohne Vorwissen Kunststoffstrukturen trotz starker Metallartefakte sichtbar gemacht werden können, womit das Verfahren ein Alleinstellungsmerkmal gegenüber den existierenden Metallartefaktreduktionsverfahren aufweist. Abstract Metal artifacts represent a major challenge for measuring with X-ray computed tomography. This paper presents a method for multipositional data fusion for the reduction of metal artifacts. For this purpose, several scans of the same object are performed with different object positioning, registered on each other and then reconstructed together using a local quality measure. The added value of the method is demonstrated in practical experiments. In particular, it is shown how plastic structures can be made visible despite strong metal artifacts with little effort and without prior knowledge, giving the method a unique selling point compared to existing metal artefact reduction methods.
Keywords: X-ray computed tomography; CT; metal artifact reduction; multipositional data fusion

Redundancies in X-ray images due to the epipolar geometry for transmission imaging - Aichert, André; Maass, Nicole; Deuerling-Zheng, Yu; Berger, Martin; Manhart, Michael; Hornegger, Joachim; Maier, Andreas; Dörfler, Arnd (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.redund_1
Keywords: GRK-1773

Reflectance Normalization in Illumination-Based Image Manipulation Detection - Riess, Christian; Pfaller, Sven; Angelopoulou, Elli (New Trends in Image Analysis and Processing - ICIAP 2015 Workshops - ICIAP 2015 International Workshops: BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, 2015)

Region-of-Interest Imaging with C-arm Computed Tomography - Xia, Yan (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2016)
C-arm based flat-detector computed tomography (FDCT) is a promising approach for neurovascular diagnosis and intervention since it facilitates proper analysis of surgical implants and intra-procedural guidance. In the majority of endovascular treatments, intra-procedural updates of the imaged object often are restricted to a small diagnostic region of interest (ROI). Such targeted ROI is often the region of intervention that contains device/vessel specific information such as stent expansion or arterial wall apposition. Following the principle of as low as reasonably achievable (ALARA), it is highly desirable to reduce unnecessary peripheral doses outside an ROI by using physical X-ray collimation, leading to substantial reduction of patient dose. However, such a technique gives rise to severely truncated projections from which conventional reconstruction algorithms generally yield images with strong truncation artifacts. The primary research goal of this thesis, therefore, lies on the algorithmic development of various truncation artifact reduction techniques that are dedicated for different imaging scenarios. First, a new data completion method is proposed that utilizes sinogram consistency conditions to estimate the missing sinogram. Although it is only extended to a 2D fan-beam geometry, preliminary results suggest the method is promising regarding truncation artifact reduction and attenuation coefficient recovery. Thereafter, three algorithms are presented, which either follow the analytic filtered backprojection (FBP) frame or are by construction in an iterative manner. They are capable of generating a 3D image from transaxially truncated data and thus appear to be closer to clinical applications. The first approach is the refinement of an existing truncation robust algorithm – ATRACT, which is implicitly effective with respect to severely truncated data. In this thesis, ATRACT is modified to more practically-useful reconstruction methods by expressing its expensive non-local filter as an efficient 1D/2D analytic convolution. The second approach is targeted to particular imaging applications that require an ROI with high image quality for diagnosis, and also a surrounding region with the relatively low resolution for orientation. To accomplish this task, an interleaved acquisition strategy that acquires both a sparse set of global non-truncated data and a dense set of truncated data is presented, along with three associated algorithms. The third approach is an attempt to exploit low-dose patient-specific prior knowledge for the extrapolation of truncated projections. The comparative evaluation clearly depicts the algorithmic performance of all investigated 3D methods under a uniform evaluation framework. In general, ATRACT appears to be more robust than the explicit water cylinder extrapolation in severe truncation case. Contrary to the heuristic methods, the techniques that come with either a sparse set of global data or prior knowledge achieve the ROI reconstructions in a more accurate and robust manner. The decision on which method should be selected relies on multiple factors, but the presented results could be used as the first indicator for the ease of such selection.<br
UnivIS-Import:2016-07-26:Pub.2016.tech.IMMD.IMMD5.region
Keywords: 3D Image Reconstruction; Computed Tomography; Region-of-Interest Imaging; Truncation Artifact Reduction

Region of Interest Reconstruction from Dose-minimized Super Short Scan Data - Xia, Yan; Maier, Andreas; Berger, Martin; Hornegger, Joachim (Bildverarbeitung für die Medizin 2014, 2014)
In this paper, we investigate an ROI image reconstruction in super short scan. The redundant rays in such scan are further shielded by using dynamic collimation. The acquired data coming with an extremely minimized radiation dose are sufficient for image reconstruction. For compensating resulting truncation, we apply a recently proposed algorithm – Approximated Truncation Robust Algorithm for Computed Tomography (ATRACT). The evaluation with two clinical datasets demonstrates that high image quality is achieved using this super short scan, with more dose reduction compared to a standard short scan.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.region_7
Keywords: GRK-1773

Region-Of-Interest Reconstruction on medical C-arms with the ATRACT Algorithm - Dennerlein, Frank; Maier, Andreas (Proc. SPIE 8313, 2012)

Registration of Atrium Models to C-arm X-ray Images Based on Devices Inside the Coronary Sinus and the Esophagus - Hoffmann, Matthias; Strobel, Norbert; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.regist

Registration of cardiac SPECT/CT data through weighted intensity co-occurrence priors - Gütter, Christoph; Wacker, Matthias; Xu, Chenyang; Hornegger, Joachim (Springer-verlag, 2007)
The introduction of hybrid scanners has greatly increased the popularity of molecular imaging techniques. Many clinical applications benefit from combining complementary information based on the precise alignment of the two modalities. In case the alignment is inaccurate, then this crucial assumption often made for subsequent processing steps will be violated. However, this violation may not be apparent to the physician. In CT-based attenuation correction (AC) for cardiac SPECT/CT data, critical misalignments between SPECT and CT can lead to spurious perfusion defects. In this work, we focus on increasing the accuracy of rigid volume registration of cardiac SPECT/CT data by using prior knowledge. A new weighting scheme for an intensity co-occurrence prior is introduced to assure accurate and robust alignment in the local heart region. Experimental results demonstrate that the proposed method out-performs mutual information registration and shows robustness across a selection of learned distributions acquired from 15 different patients. © Springer-Verlag Berlin Heidelberg 2007.

Registration of Cardiac SPECT/CT Data through Weighted Intensity Co-Occurrence Priors - Gütter, Christoph; Wacker, Matthias; Xu, Chenyang; Hornegger, Joachim (Proceedings of the 10th International Conference Brisbane, Australia, 2007)
The introduction of hybrid scanners has greatly increased the popularity of molecular imaging techniques. Many clinical applications benefit from combining complementary information based on the precise alignment of the two modalities. In case the alignment is inaccurate, then this crucial assumption often made for subsequent processing steps will be violated. However, this violation may not be apparent to the physician. In CT-based attenuation correction (AC) for cardiac SPECT/CT data, critical misalignments between SPECT and CT can lead to spurious perfusion defects. In this work, we focus on increasing the accuracy of rigid volume registration of cardiac SPECT/CT data by using prior knowledge. A new weighting scheme for an intensity co-occurrence prior is introduced to assure accurate and robust alignment in the local heart region. Experimental results demonstrate that the proposed method out-performs mutual information registration and shows robustness across a selection of learned distributions acquired from 15 different patients.
UnivIS-Import:2015-04-16:Pub.2007.tech.IMMD.IMMD5.regist
Keywords: cardiac SPECT/CT; rigid image registration; multi-modal; statistical priors; robust

Registration of functional and morphological volume data sets - Hornegger, Joachim; Römer, Wolfgang; Bautz, Werner; Kuwert, Torsten (35th Jahrestagung der Gesellschaft fur Informatik e.V. (GI): Informatik LIVE!, INFORMATIK 2005 35th Annual Conference of the German Informatics Society (GI): Informatics LIVE!, INFORMATIK 2005, 2005)

Registration of vascular structures using a hybrid mixture model - Bayer, Siming; Zhai, Zhiwei; Strumia, Maddalena; Tong, Xiaoguang; Gao, Ying; Staring, Marius; Stoel, Berend; Fahrig, Rebecca; Arya, Nabavi; Maier, Andreas; Ravikumar, Nishant (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose Morphological changes to anatomy resulting from invasive surgical procedures or pathology, typically alter the surrounding vasculature. This makes it useful as a descriptor for feature-driven image registration in various clinical applications. However, registration of vasculature remains challenging, as vessels often differ in size and shape, and may even miss branches, due to surgical interventions or pathological changes. Furthermore, existing vessel registration methods are typically designed for a specific application. To address this limitation, we propose a generic vessel registration approach useful for a variety of clinical applications, involving different anatomical regions. Methods A probabilistic registration framework based on a hybrid mixture model, with a refinement mechanism to identify missing branches (denoted as HdMM+) during vasculature matching, is introduced. Vascular structures are represented as 6-dimensional hybrid point sets comprising spatial positions and centerline orientations, using Student’s t-distributions to model the former and Watson distributions for the latter. Results The proposed framework is evaluated for intraoperative brain shift compensation, and monitoring changes in pulmonary vasculature resulting from chronic lung disease. Registration accuracy is validated using both synthetic and patient data. Our results demonstrate, HdMM+ is able to reduce more than 85%" role="presentation">85% of the initial error for both applications, and outperforms the state-of-the-art point-based registration methods such as coherent point drift and Student’s t-distribution mixture model, in terms of mean surface distance, modified Hausdorff distance, Dice and Jaccard scores. Conclusion The proposed registration framework models complex vascular structures using a hybrid representation of vessel centerlines, and accommodates intricate variations in vascular morphology. Furthermore, it is generic and flexible in its design, enabling its use in a variety of clinical applications.
Keywords: Non-rigid registration; Point matching; Brain shift; Pulmonary vascular diseases

Regression Forest-Based Organ Detection in Normalized PET Images - Fischer, Peter; Daum, Volker; Hahn, Dieter; Prümmer, Marcus; Hornegger, Joachim (Bildverarbeitung für die Medizin 2014, 2014)
The detection of organs from full-body PET images is a challenging task due to the high noise and the limited amount of anatomical information of PET imaging. The knowledge of organ locations can support many clinical applications like image registration or tumor detection. This paper is the first to propose an organ localization framework tailored on the challenges of PET. The algorithm involves intensity normalization, feature extraction and regression forests. Linear and nonlinear intensity normalization methods are compared theoretically and experimentally. From the normalized images, long-range spatial context visual features are extracted. A regression forest predicts the organ bounding boxes. Experiments show that percentile normalization is the best preprocessing method. The algorithm is evaluated on 25 clinical images with a spatial resolution of 5 mm. With 13.8mm mean absolute bounding box error, it achieves state-of-the-art results.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.regres_5

Releasing a thoroughly annotated and processed spontaneous emotional database: the FAU Aibo Emotion Corpus - Batliner, Anton; Steidl, Stefan; Nöth, Elmar (Proc. of a Satellite Workshop of LREC 2008 on Corpora for Research on Emotion and Affect, 2008)
We report on a thoroughly processed and annotated German emotional speech database (children interacting with Sony’s Aibo robot): 51 children, some 48 k words, 9.2 hours of speech, 5 labellers, word-based annotation of emotional user states. Several additional annotations as well as a mapping onto higher units of different granularity have been carried out. The database will eventually be made available for scientific use; in the licensing agreement, we plan to include mandatory benchmark constellations in order to make a comparison across sites possible.

Reliable Detection of Important Word Boundaries Using Prosodic Features - Kaufhold, Caroline; Stemmer, Georg; Nöth, Elmar (Springer-verlag, 2011)
Natural input dialog systems for entering address data in modern GPS units demand a significantly more robust extraction of slot information. In previous work, prosody was used to detect phrase boundaries (PB) which separate particular address parts. Only input without filler words was used. In this work, carrier sentences are allowed. A boosting approach provides twenty strong prosodic features which model the characteristic of PBs. We introduce the concept of a prosodically marked word boundary (PMB), which enables a better location of the provided information in natural input. Our results on a dataset of 5883 input samples reveal that about 67% of the found PBs indicate a PMB, while most of the remaining boundaries occur within compound words.
Keywords: prosody; phrase boundary detection; multi-slot input modality

Reliable measures of syntactic and lexical complexity: The case of Iris Murdoch - Evert, Stephanie; Wankerl, Sebastian; Nöth, Elmar (Proceedings of the Corpus Linguistics 2017 Conference, 2017)
In this paper, we propose a novel methodology for the computation of reliable confidence intervals and significance tests for measures of linguistic complexity, inspired by ideas from bootstrapping and cross-validation. As an illustration, we apply the new method to the detection of early symptoms of Alzheimer's disease in the novels of Iris Murdoch, showing that most of the differences observed in previous work are not signficant and can indeed be accounted for by sampling variation.
Keywords: LNRE; lexical statistics

Repeatability and Reproducibility of a New Method for Quantifying Triglyceride Saturation Using Bipolar Multi-Echo MRI - Schneider, Manuel; Lugauer, Felix; Janas, Gemini; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.repeat

Representation Issues in the ML Estimation of Camera Motion - Hornegger, Joachim; Tomasi, Carlo (Proceedings of the 7th International Conference on Computer Vision (ICCV), 1999)

Representation Learning for Cloud Classification - Bernecker, David; Riess, Christian; Christlein, Vincent; Angelopoulou, Elli; Hornegger, Joachim (Pattern Recognition, 2013)

Re-Ranking for Writer Identification and Writer Retrieval - Jordan, Simon; Seuret, Mathias; Král, Pavel; Lenc, Ladislav; Martínek, Jiří; Wiermann, Barbara; Schwinger, Tobias; Maier, Andreas; Christlein, Vincent (Document Analysis Systems, 2020)
Automatic writer identification is a common problem in document analysis. State-of-the-art methods typically focus on the feature extraction step with traditional or deep-learning-based techniques. In retrieval problems, re-ranking is a commonly used technique to improve the results. Re-ranking refines an initial ranking result by using the knowledge contained in the ranked result, e. g., by exploiting nearest neighbor relations. To the best of our knowledge, re-ranking has not been used for writer identification/retrieval. A possible reason might be that publicly available benchmark datasets contain only few samples per writer which makes a re-ranking less promising. We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification, even if only a few samples per writer are available. We use these reciprocal relationships in two ways: encode them into new vectors, as originally proposed, or integrate them in terms of query-expansion. We show that both techniques outperform the baseline results in terms of mAP on three writer identification datasets.<br
Keywords: Writer identification; Writer retrieval; Re-ranking

Residual-based forensic comparison of video sequences - Mullan, Patrick; Cozzolino, Davide; Verdoliva, Luisa; Riess, Christian (IEEE International Conference on Image Processing, 2017)

Residual Motion Compensation in ECG-Gated Cardiac Vasculature Reconstruction - Schwemmer, Chris; Rohkohl, Christopher; Lauritsch, Günter; Müller, Kerstin; Hornegger, Joachim (Proceedings of the second international conference on image formation in x-ray computed tomography, 2012)
Generating 3-D reconstructions of cardiac vasculature from angiographic C-arm CT (rotational angiography) data is a challenging problem. Currently, many approaches depend on a reconstruction from ECG-gated projection data either as a reference for further processing or as the final result. Due to imperfect gating, e.g. caused by irregular heart movement, residual motion corrupts these reconstructions. We present an algorithm to compensate for this residual motion. The approach is based on a deformable 2-D-2-D registration between the acquired projection data and a forward projection of the initial ECG-gated reconstruction. It does not depend on an explicit segmentation of vasculature or markers, and works without user interaction. The estimated 2-D deformation field is compensated for in the backprojection step of a subsequent reconstruction. The algorithm is evaluated on two clinical datasets, showing a clear decrease in artefact level and better visibility of structure in the compensated reconstructions.

Residual Motion Compensation in ECG-Gated Interventional Cardiac Vasculature Reconstruction - Schwemmer, Chris; Rohkohl, Christopher; Lauritsch, Günter; Müller, Kerstin; Hornegger, Joachim (Physics in Medicine and Biology, 2013)

Residual Neural Network for Filter Kernel Design in Filtered Back-projection for CT Image Reconstruction - Xu, Jintian; Sun, Chengjin; Huang, Yixing; Huang, Xiaolin (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Resolve Intraoperative Brain Shift as Imitation Game - Zhong, Xia; Bayer, Siming; Ravikumar, Nishant; Strobel, Norbert; Birkhold, Annette; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (MICCAI Challenge 2018 for Correction of Brainshift with Intra-Operative Ultrasound (CuRIOUS 2018), 2018)
Soft tissue deformation induced by craniotomy and tissue manipulation (brain shift) limits the use of preoperative image overlay in an image-guided neurosurgery, and therefore reduces the accuracy of the surgery as a consequence. An inexpensive modality to compensate for the brain shift in real-time is Ultrasound (US). The core subject of research in this context is the non-rigid registration of preoperative MR and intraoperative US images. In this work, we propose a learning based approach to address this challenge. Resolving intraoperative brain shift is considered as an imitation game, where the optimal action (displacement) for each landmark on MR is trained with a multi-task network. The result shows a mean target error of 1.21 ± 0.55 mm.<br

Respiratory Deformation Estimation in X-Ray-Guided IMRT Using a Bilinear Model - Geimer, Tobias; Ploner, Stefan; Keall, Paul; Bert, Christoph; Maier, Andreas (Informatik aktuell, 2019)
Driving a respiratory motion model in X-ray guided radiotherapy can be challenging in treatments with continuous rotation such as VMAT, as data-driven respiratory signal extraction suffers from angular effects overlapping with respiratory changes in the projection images. Compared to a linear model trained on static acquisition angles, the bilinear model gains flexibility in terms of handling multiple viewpoints at the cost of accuracy. In this paper, we evaluate both models in the context of serving as the surrogate input to a motion model. Evaluation is performed on the 20 patient 4D CTs in a leave-one-phase-out approach yielding a median accuracy drop of only 0.14mm in the 3D error of estimated vector fields of the bilinear model compared to the linear one.
CRIS-Team Scopus Importer:2019-05-14

Respiratory Inductance Plethysmography - A Rationale for Validity during Exercise - Heyde, Christian; Leutheuser, Heike; Eskofier, Björn; Röcker, Kai; Gollhofer, Albert (Medicine and science in sports and exercise, 2014)
Introduction: The aim of this study was to provide a rationale for future validations of a priori calibrated respiratory inductance plethysmography (RIP) when used under exercise conditions. Therefore, the validity of a posteriori-adjusted gain factors and accuracy in resultant breath-by-breath RIP data recorded under resting and running conditions were examined. Methods: Healthy subjects, 98 men and 88 women (mean ± SD: height = 175.6 ± 8.9 cm, weight = 68.9 ± 11.1 kg, age = 27.1 ± 8.3 yr), underwent a standardized test protocol, including a period of standing still, an incremental running test on treadmill, and multiple periods of recovery. Least square regression was used to calculate gain factors, respectively, for complete individual data sets as well as several data subsets. In comparison with flowmeter data, the validity of RIP in breathing rate (fR) and inspiratory tidal volume (VTIN) were examined using coefficients of determination (R). Accuracy was estimated from equivalence statistics. Results: Calculated gains between different data subsets showed no equivalence. After gain adjustment for the complete individual data set, fR and VTIN between methods were highly correlated (R = 0.96 ± 0.04 and 0.91 ± 0.05, respectively) in all subjects. Under conditions of standing still, treadmill running, and recovery, 86%, 98%, and 94% (fR) and 78%, 97%, and 88% (VTIN), respectively, of all breaths were accurately measured within ±20% limits of equivalence. Conclusion: In case of the best possible gain adjustment, RIP confidentially estimates tidal volume accurately within ±20% under exercise conditions. Our results can be used as a rationale for future validations of a priori calibration procedures. © 2014 by the American College of Sports Medicine.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.respir
Keywords: VENTILATION; RUNNING EXERCISE; MONITORING; RESPIRATION; AMBULATORY MONITORING

Respiratory motion compensation by model-based catheter tracking during EP procedures - Brost, Alexander; Liao, R.; Strobel, N.; Hornegger, Joachim (Medical Image Analysis, 2010)
In many cases, radio-frequency catheter ablation of the pulmonary veins attached to the left atrium still involves fluoroscopic image guidance. Two-dimensional X-ray navigation may also take advantage of overlay images derived from static pre-operative 3D volumetric data to add anatomical details otherwise not visible under X-ray. Unfortunately, respiratory motion may impair the utility of static overlay images for catheter navigation. We developed a novel approach for image-based 3D motion estimation and compensation as a solution to this problem. It is based on 3D catheter tracking which, in turn, relies on 2D/3D registration. To this end, a bi-plane C-arm system is used to take X-ray images of a special circumferential mapping catheter from two directions. In the first step of the method, a 3D model of the device is reconstructed. Three-dimensional respiratory motion at the site of ablation is then estimated by tracking the reconstructed catheter model in 3D based on bi-plane fluoroscopy. Phantom data and clinical data were used to assess model-based catheter tracking. Our phantom experiments yielded an average 2D tracking error of 1.4. mm and an average 3D tracking error of 1.1mm. Our evaluation of clinical data sets comprised 469 bi-plane fluoroscopy frames (938 monoplane fluoroscopy frames). We observed an average 2D tracking error of 1.0 ± 0.4 mm and an average 3D tracking error of 0.8 ± 0.5 mm. These results demonstrate that model-based motion-compensation based on 2D/3D registration is both feasible and accurate. © 2010 Elsevier B.V.