Talbot-Lau X-ray phase contrast for tiling-based acquisitions without reference scanning - Käppler, Sebastian; Seifert, Maria; Horn, Florian; Pelzer, Georg; Rieger, Jens; Michel, Thilo; Maier, Andreas; Anton, Gisela; Riess, Christian (Medical Physics, 2017)
Keywords: grating interferometer; reference image; phase-contrast; x-ray imaging

Talbot-Lau x-ray phase-contrast setup for fast scanning of large samples - Seifert, Maria; Ludwig, Veronika; Käppler, Sebastian; Horn, Florian; Meyer, Pascal; Pelzer, Georg; Rieger, Jens; Sand, Daniel; Michel, Thilo; Mohr, Juergen; Riess, Christian; Anton, Gisela (Scientific Reports, 2019)
Compared to conventional attenuation x-ray radiographic imaging, the x-ray Talbot-Lau technique provides further information about the scattering and the refractive properties of the object in the beam path. Hence, this additional information should improve the diagnostic process concerning medical applications and non-destructive testing. Nevertheless, until now, due to grating fabrication process, Talbot-Lau imaging suffers from small grating sizes (70 mm diameter). This leads to long acquisition times for imaging large objects. Stitching the gratings is one solution. Another one consists of scanning Talbot-Lau setups. In this publication, we present a compact and very fast scanning setup which enables imaging of large samples. With this setup a maximal scanning velocity of 71.7 mm/s is possible. A resolution of 4.1 lines/mm can be achieved. No complex alignment procedures are necessary while the field of view comprises 17.5 x 150 cm2. An improved reconstruction algorithm concerning the scanning approach, which increases robustness with respect to mechanical instabilities, has been developed and is presented. The resolution of the setup in dependence of the scanning velocity is evaluated. The setup imaging qualities are demonstrated using a human knee ex-vivo as an example for a high absorbing human sample.
CRIS-Team WoS Importer:2019-03-29

Tales of Tuning - Prototyping for Automatic Classification of Emotional User States - Batliner, Anton; Steidl, Stefan; Hacker, Christian; Nöth, Elmar; Niemann, Heinrich (Proceedings of the 9th European Conference on Speech Communication and Technology, 2005)
Classification performance for emotional user states found in the few realistic, spontaneous databases available is as yet not very high. We present a database with emotional children’s speech in a human-robot scenario. Baseline classification performance for seven classes is 44.5%, for four classes 59.2%. We discuss possible strategies for tuning, e.g., using only prototypes (based on annotation correspondence or classification scores), or taking into account requirements and feasibility in possible applications (weighting of false alarms or speaker-specific overall frequencies).

Tandem decoding of children's speech for keyword detection in a child-robot interaction scenario - Wöllmer, Martin; Schuller, Björn; Batliner, Anton; Steidl, Stefan; Seppi, Dino (ACM Transactions on Speech and Language Processing, 2011)
In this article, we focus on keyword detection in children's speech as it is needed in voice command systems. We use the FAU Aibo Emotion Corpus which contains emotionally colored spontaneous children's speech recorded in a child-robot interaction scenario and investigate various recent key-word spotting techniques. As the principle of bidirectional Long Short-Term Memory (BLSTM) is known to be well-suited for context-sensitive phoneme prediction, we incorporate a BLSTM network into a Tandem model for exible coarticulation modeling in children's speech. Our experiments reveal that the Tandem model prevails over a triphone-based Hidden Markov Model approach.
Keywords: children's speech; dynamic Bayesian networks; keyword spotting; long short-term memory

Technical feasibility of 2D-3D coregistration for visualization of self-expandable microstents to facilitate coil embolization of broad-based intracranial aneurysms: An in vitro study - Richter, Gregor; Pfister, Marcus; Struffert, Tobias; Engelhorn, Tobias; Dölken, Marc; Spiegel, Martin; Hornegger, Joachim; Dörfler, Arnd (Neuroradiology, 2009)
Introduction: The use of self-expandable microstents for treatment of broad-based intracranial aneurysms is widely spread. However, poor fluoroscopic visibility of the stents remains disadvantageous during the coiling procedure. Flat detector angiographic computed tomography (ACT) provides high resolution imaging of microstents even though integration of this imaging modality in the neurointerventional workflow has not been widely reported. Methods: An acrylic glass model was used to simulate the situation of a broad-based sidewall aneurysm. After insertion of a self-expandable microstent, ACT was performed. The resulting 3D dataset of the Microstent was subsequently projected into a conventional 2D fluoroscopic roadmap. This 3D visualization of the stent supported the coil embolization procedure of the in vitro aneurysm. Results: In vitro 2D-3D coregistration with integration of 3D ACT data of a self-expandable microstent in a conventional 2D roadmap is feasible. Conclusions: Unsatisfying stent visibility constrains clinical cases with complex parent vessel anatomy and challenging aneurysm geometry; hence, this technique potentially may be useful in such cases. In our opinion, the clinical feasibility and utility of this new technique should be verified in a clinical aneurysm embolization study series using 2D-3D coregistration. © 2009 Springer-Verlag.

Technical Note: PYRO-NN: Python reconstruction operators in neural networks - Syben-Leisner, Christopher; Michen, Markus; Stimpel, Bernhard; Seitz, Stephan; Ploner, Stefan; Maier, Andreas (Medical Physics, 2019)
Purpose: Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the computed tomography (CT) reconstruction as a known operator into a neural network. However, most of the approaches presented lack an efficient CT reconstruction framework fully integrated into deep learning environments. As a result, many approaches use workarounds for mathematically unambiguously solvable problems. Methods: PYRO-NN is a generalized framework to embed known operators into the prevalent deep learning framework Tensorflow. The current status includes state-of-the-art parallel-, fan-, and cone-beam projectors, and back-projectors accelerated with CUDA provided as Tensorflow layers. On top, the framework provides a high-level Python API to conduct FBP and iterative reconstruction experiments with data from real CT systems. Results: The framework provides all necessary algorithms and tools to design end-to-end neural network pipelines with integrated CT reconstruction algorithms. The high-level Python API allows a simple use of the layers as known from Tensorflow. All algorithms and tools are referenced to a scientific publication and are compared to existing non-deep learning reconstruction frameworks. To demonstrate the capabilities of the layers, the framework comes with baseline experiments, which are described in the supplementary material. The framework is available as open-source software under the Apache 2.0 licence at https://github.com/csyben/PYRO-NN. Conclusions: PYRO-NN comes with the prevalent deep learning framework Tensorflow and allows to setup end-to-end trainable neural networks in the medical image reconstruction context. We believe that the framework will be a step toward reproducible research and give the medical physics community a toolkit to elevate medical image reconstruction with new deep learning techniques.
CRIS-Team Scopus Importer:2019-10-22
Keywords: inverse problems; known operator learning; machine learning; open source; reconstruction

Technical Note: RabbitCT-an open platform for benchmarking 3D cone-beam reconstruction algorithms - Rohkohl, Christopher; Keck, Benjamin; Hofmann, Hannes; Hornegger, Joachim (Medical Physics, 2009)
Purpose: Fast 3D cone beam reconstruction is mandatory for many clinical workflows. For that reason, researchers and industry work hard on hardware-optimized 3D reconstruction. Backprojection is a major component of many reconstruction algorithms that require a projection of each voxel onto the projection data, including data interpolation, before updating the voxel value. This step is the bottleneck of most reconstruction algorithms and the focus of optimization in recent publications. A crucial limitation, however, of these publications is that the presented results are not comparable to each other. This is mainly due to variations in data acquisitions, preprocessing, and chosen geometries and the lack of a common publicly available test dataset. The authors provide such a standardized dataset that allows for substantial comparison of hardware accelerated backprojection methods. Methods: They developed an open platform RabbitCT (www.rabbitCT.com) for worldwide comparison in backprojection performance and ranking on different architectures using a specific high resolution C-arm CT dataset of a rabbit. This includes a sophisticated benchmark interface, a prototype implementation in C++, and image quality measures. Results: At the time of writing, six backprojection implementations are already listed on the website. Optimizations include multithreading using Intel threading building blocks and OpenMP, vectorization using SSE, and computation on the GPU using CUDA 2.0. Conclusions: There is a need for objectively comparing backprojection implementations for reconstruction algorithms. RabbitCT aims to provide a solution to this problem by offering an open platform with fair chances for all participants. The authors are looking forward to a growing community and await feedback regarding future evaluations of novel software- and hardware-based acceleration schemes. © 2009 American Association of Physicists in Medicine.

Technik und physikalische Aspekte der SPECT / CT - Ritt, Philipp; Hornegger, Joachim; Kuwert, Torsten (Der Nuklearmediziner, 2011)

Technology Integration in the Daily Activities of the Elderly - Soutschek, Stefan; Spiegl, Werner; Steidl, Stefan; Hornegger, Joachim; Erzigkeit, Hellmut; Kornhuber, Johannes (Künstliche Intelligenz, 2008)
A continuous increase of the anticipated average life is expected in the near future. We have to deal with the consequences of aging, such as loneliness in an environment characterized by technology or the need to help elderly people. Technical solutions are required that solve the occurring problems associated with aging and are accepted and used. This work faces the challenge of improving the early detection of diseases like dementia, using an intelligent dialogue system that improves the chance for integrating applications of telemedicine in an intelligent home environment.

Telephone-based speech dialog systems - Haas, Jürgen; Gallwitz, Florian; Horndasch, Axel; Huber, Richard; Warnke, Volker (Pattern Recognition, 2005)
In this contribution we look back on the last years in the history of telephone-based speech dialog systems. We will start in 1993 when the world wide first natural language understanding dialog system using a mixed-initiative approach was made accessible for the public, the well-known EVAR system from the Chair for Pattern Recognition of the University of Erlangen-Nuremberg. Then we discuss certain requirements we consider necessary for the successful application of dialog systems. Finally we present trends and developments in the area of telephone-based dialog system
Keywords: Telephone-based speech dialog systems; mixed-initiative dialog systems; natural language understanding; EVAR

Tempo and its Change in Spontaneous Speech - Batliner, Anton; Kießling, Andreas; Kompe, Ralf; Niemann, Heinrich; Nöth, Elmar (Proc. European Conf. on Speech Communication and Technology, 1997)

Temporal and spatial detection of the onset of local necking and assessment of its growth behavior - Jaremenko, Christian; Affronti, Emanuela; Merklein, Marion; Maier, Andreas (Materials, 2020)
This study proposes a method for the temporal and spatial determination of the onset of local necking determined by means of a Nakajima test set-up for a DC04 deep drawing and a DP800 dual-phase steel, as well as an AA6014 aluminum alloy. Furthermore, the focus lies on the observation of the progress of the necking area and its transformation throughout the remainder of the forming process. The strain behavior is learned by a machine learning approach on the basis of the images when the process is close to material failure. These learned failure characteristics are transferred to new forming sequences, so that critical areas indicating material failure can be identified at an early stage, and consequently enable the determination of the beginning of necking and the analysis of the necking area. This improves understanding of the necking behavior and facilitates the determination of the evaluation area for strain paths. The growth behavior and traceability of the necking area is objectified by the proposed weakly supervised machine learning approach, thereby rendering a heuristic-based determination unnecessary. Furthermore, a simultaneous evaluation on image and pixel scale is provided that enables a distinct selection of the failure quantile of the probabilistic forming limit curve.
CRIS-Team Scopus Importer:2020-07-17
Keywords: Artificial intelligence; Classification; Deep learning; Forming limit curve; Machine learning; Pattern recognition; Segmentation; Sheet metal forming

Temporal and Volumetric Denoising via Quantile Sparse Image Prior - Schirrmacher, Franziska; Köhler, Thomas; Endres, Jürgen; Lindenberger, Tobias; Husvogt, Lennart; Fujimoto, James G.; Hornegger, Joachim; Dörfler, Arnd; Maier, Andreas; Hölter, Philip (Medical Image Analysis, 2018)
This paper introduces an universal and structure-preserving regularization term, called quantile sparse image (QuaSI) prior. The prior is suitable for denoising images from various medical imaging modalities. We demonstrate its effectiveness on volumetric optical coherence tomography (OCT) and computed tomography (CT) data, which show different noise and image characteristics. OCT offers high-resolution scans of the human retina but is inherently impaired by speckle noise. CT on the other hand has a lower resolution and shows high-frequency noise. For the purpose of denoising, we propose a variational framework based on the QuaSI prior and a Huber data fidelity model that can handle 3-D and 3-D+t data. Efficient optimization is facilitated through the use of an alternating direction method of multipliers (ADMM) scheme and the linearization of the quantile filter. Experiments on multiple datasets emphasize the excellent performance of the proposed method.<br
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.tempor
Keywords: Spatio-temporal denoising; Variational approach; QuaSI prior; ADMM

Tenfold your photons: A physically-sound approach to filtering-based variance reduction of monte-carlo-simulated dose distributions - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik aktuell, 2020)
X-ray dose constantly gains interest in the interventional suite. With dose being generally diffcult to monitor reliably, fast computational methods are desirable. A major drawback of the gold standard based on Monte Carlo (MC) methods is its computational complexity. Besides common variance reduction techniques, filter approaches are often applied to achieve conclusive results within a fraction of time. Inspired by these methods, we propose a novel approach. We down-sample the target volume based on the fraction of mass, simulate the imaging situation, and then revert the down-sampling. To this end, the dose is weighted by the mass energy absorption, up-sampled, and distributed using a guided filter. Eventually, the weighting is inverted resulting in accurate high resolution dose distributions. The approach has the potential to considerably speed-up MC simulations since less photons and boundary checks are necessary. First experiments substantiate these assumptions. We achieve a median accuracy of 96.7% to 97.4% of the dose estimation with the proposed method and a down-sampling factor of 8 and 4, respectively. While maintaining a high accuracy, the proposed method provides for a tenfold speed-up. The overall findings suggest the conclusion that the proposed method has the potential to allow for further effciency.
CRIS-Team Scopus Importer:2020-04-21

Tetrahedral and polyhedral mesh evaluation for cerebral hemodynamic simulation–a comparison - Spiegel, Martin; Redel, Thomas; Zhang, Yao; Struffert, Tobias; Hornegger, Joachim; Grossman, Robert G.; Dörfler, Arnd; Karmonik, Christof (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2009)
Computational fluid dynamic (CFD) based on patient-specific medical imaging data has found widespread use for visualizing and quantifying hemodynamics in cerebrovascular disease such as cerebral aneurysms or stenotic vessels. This paper focuses on optimizing mesh parameters for CFD simulation of cerebral aneurysms. Valid blood flow simulations strongly depend on the mesh quality. Meshes with a coarse spatial resolution may lead to an inaccurate flow pattern. Meshes with a large number of elements will result in unnecessarily high computation time which is undesirable should CFD be used for planning in the interventional setting. Most CFD simulations reported for these vascular pathologies have used tetrahedral meshes. We illustrate the use of polyhedral volume elements in comparison to tetrahedral meshing on two different geometries, a sidewall aneurysm of the internal carotid artery and a basilar bifurcation aneurysm. The spatial mesh resolution ranges between 5,119 and 228,118 volume elements. The evaluation of the different meshes was based on the wall shear stress previously identified as a one possible parameter for assessing aneurysm growth. Polyhedral meshes showed better accuracy, lower memory demand, shorter computational speed and faster convergence behavior (on average 369 iterations less).
UnivIS-Import:2017-01-09:Pub.2009.tech.IMMD.IMMD5.tetrah_3

Tetrahedral vs. polyhedral mesh size evaluation on flow velocity and wall shear stress for cerebral hemodynamic simulation - Spiegel, Martin; Redel, Thomas; Zhang, Y. Jonathan; Struffert, Tobias; Hornegger, Joachim; Grossman, Robert G.; Dörfler, Arnd; Karmonik, Christof (Computer Methods in Biomechanics and Biomedical Engineering, 2011)
Haemodynamic factors, in particular wall shear stresses (WSSs) may have significant impact on growth and rupture of cerebral aneurysms. Without a means to measure WSS reliably in vivo, computational fluid dynamic (CFD) simulations are frequently employed to visualise and quantify blood flow from patient-specific computational models. With increasing interest in integrating these CFD simulations into pretreatment planning, a better understanding of the validity of the calculations in respect to computation parameters such as volume element type, mesh size and mesh composition is needed. In this study, CFD results for the two most common aneurysm types (saccular and terminal) are compared for polyhedral- vs. tetrahedral-based meshes and discussed regarding future clinical applications. For this purpose, a set of models were constructed for each aneurysm with spatially varying surface and volume mesh configurations (mesh size range: 5119-258, 481 volume elements). WSS distribution on the model wall and point-based velocity measurements were compared for each configuration model. Our results indicate a benefit of polyhedral meshes in respect to convergence speed and more homogeneous WSS patterns. Computational variations of WSS values and blood velocities are between 0.84 and 6.3% from the most simple mesh (tetrahedral elements only) and the most advanced mesh design investigated (polyhedral mesh with boundary layer). © 2011 Taylor & Francis.

Text-based vs. Vowel-based Automatic Evaluation of Tracheoesophageal Substitute Voice - Haderlein, Tino; Bocklet, Tobias; Nöth, Elmar; Rosanowski, Frank (Proceedings of IWSSIP 2008, 2008)

Text-independent Speaker Identification using Temporal Patterns - Bocklet, Tobias; Maier, Andreas; Nöth, Elmar (Text, Speech and Dialogue, 2007)

The Added Value of Volume-of-Interest C-Arm CT Imaging during Endovascular Treatment of Intracranial Aneurysms - Chintalapani, Gouthami; Maier, Andreas; Chinnadurai, Ponraj; Xia, Yan; Bauer, Sebastian; Shaltomi, Hashem; Morsi, Hesham; Mawad, Michel (American Journal of Neuroradiology, 2015)
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.IMMD5.theadd

The Automatic Assessment of Non-native Prosody: Combining Classical Prosodic Analysis with Acoustic Modelling - Hönig, Florian Thomas; Bocklet, Tobias; Riedhammer, Korbinian Thomas; Batliner, Anton; Nöth, Elmar (Proceedings of the 13th Annual Conference of the International Speech Communication Association, 2012)

The Automatic Recognition of Emotions in Speech - Batliner, Anton; Schuller, Björn; Seppi, Dino; Steidl, Stefan; Devillers, Laurence; Vidrascu, Laurence; Vogt, Thurid; Aharonson, Vered; Amir, Noam (Emotion-Oriented Systems – The HUMAINE Handbook, 2011)
In this chapter, we focus on the automatic recognition of emotional states using acoustic and linguistic parameters as features, and classifiers as tools to predict the ‘correct’ emotional states. We first sketch history and state-of-the art in this field; then we describe the process of ‘corpus engineering’, i.e. the design and recording of databases, the annotation of emotional states, and further processing such as manual or automatic segmentation. Next we present an overview of acoustic and linguistic features that are extracted automatically or manually. In the section on classifiers, we deal with topics such as the curse of dimensionality and the sparse data problem, classifiers, and evaluation. At the end of each section, we point out important aspects that should be taken into account for the planning or the assessment of studies. The subject area of this chapter is not emotions in some narrow sense but in a wider sense encompassing emotion-related states such as moods, attitudes, or interpersonal stances as well. We do not aim at an in-depth treatise of some specific aspects or algorithms but at an overview of approaches and strategies that have been used or should be used.

The EMBASSI Speech Corpus - Haderlein, Tino; Nöth, Elmar (2003)
UnivIS-Import:2016-06-30:Pub.2003.tech.IMMD.IMMD5.theemb

The Erlangen Advanced Time Tabling System (EATTS) Unified XML File Format for the Specification of Time Tabling Systems - Wilke, Peter; Ostler, Johannes (Proc. PATAT 2010, 2010)

The Erlangen Advanced Time Tabling System (EATTS) version 5 - Wilke, Peter; Ostler, Johannes; Merdenoglu, Kerim; Kremer, Eugen; Killer, Helmut (Proc. PATAT 2010, 2010)

The Erlangen Slot Machine: A Highly Flexible FPGA-Based Reconfigurable Platform - Ahmadinia, Ali; Bobda, Christophe; Fekete, Sandor P.; Haller, Thomas; Linarth, Andre Guilherme; Majer, Mateusz; Teich, Jürgen; Van Der Veen, Jan C. (Proceedings of the 13th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM'05), 2005)
We present a new concept as well as the implementation of an FPGA-based reconfigurable platform, the Erlangen Slot Machine (ESM). The main advantages of this platform are: first, the possibility for each module to access its peripheries independent from its location through a programmable crossbar, and distributed SRAMs among slices. This allows an unrestricted relocation of modules on the device. Second, the intermodule structure allows an unlimited communication among running modules. © 2005 IEEE.
UnivIS-Import:2015-04-16:Pub.2005.tech.IMMD.inform.theerl

The Facial Expression Module - Frank, Carmen Manuela; Adelhardt, Johann; Batliner, Anton; Nöth, Elmar; Shi, Rui Ping; Zeißler, Viktor; Niemann, Heinrich (SmartKom: Foundations of Multimodal Dialogue Systems, 2006)

THE FAU VIDEO LECTURE BROWSER SYSTEM - Riedhammer, Korbinian Thomas; Gropp, Martin; Nöth, Elmar (2012)
A growing number of universities and other educational institutions provide recordings of lectures and seminars as an additional resource to the students. In contrast to educational films that are scripted, directed and often shot by film professionals, these plain recordings are typically not post-processed in an editorial sense. Thus, the videos often contain longer periods of inactivity or silence, unnecessary repetitions, or corrections of prior mistakes. This paper describes the FAU Video Lecture Browser system, a web-based platform for the interactive assessment of video lectures, that helps to close the gap between a plain recording and a useful e-learning resource by displaying automatically extracted and ranked key phrases on an augmented time line based on stream graphs. In a pilot study, users of the interface were able to complete a topic localization task about 29% faster than users provided with the video only while achieving about the same accuracy. The user interactions can be logged on the server to collect data to evaluate the quality of the phrases and rankings, and to train systems that produce customized phrase rankings.
Keywords: automatic speech recognition; key phrase extraction; key phrase ranking; visualization; user interaction

The Gesture Interpretation Module - Shi, Rui Ping; Adelhardt, Johann; Batliner, Anton; Frank, Carmen Manuela; Nöth, Elmar; Zeißler, Viktor; Niemann, Heinrich (SmartKom: Foundations of Multimodal Dialogue Systems, 2006)

The Hinterland of Emotions: Facing the Open-Microphone Challenge - Steidl, Stefan; Schuller, Björn; Batliner, Anton; Seppi, Dino (Proceedings of ACII 2009, 2009)
We first depict the challenge to address all non-prototypical varieties of emotional states signalled in speech in an open microphone setting, i.e. using all data recorded. In the remainder of the article, we illustrate promising strategies, using the FAU Aibo Emotion Corpus, by showing different degrees of classification performance for different degrees of prototypicality, and by elaborating on the use of ROC curves, classification confidences, and the use of correlation-based analyses.

The Impact of F0 Extraction Errors on the Classification of Prominence and Emotion - Batliner, Anton; Steidl, Stefan; Schuller, Björn; Seppi, Dino; Vogt, Thurid; Devillers, Laurence; Vidrascu, Laurence; Amir, Noam; Kessous, Loic; Aharonson, Vered (Proceedings of the 16th International Congress of Phonetic Sciences, 2007)
Traditionally, it has been assumed that pitch is the most important prosodic feature for the marking of prominence, and of other phenomena such as the marking of boundaries or emotions. This role has been put into question by recent studies. As nowadays larger databases are always being processed automatically, it is not clear up to what extent the possibly lower relevance of pitch can be attributed to extraction errors or to other factors. We present some ideas as for a phenomenological difference between pitch and duration, and compare the performance of automatically extracted F0 values and of manually corrected F0 values for the automatic recognition of prominence and emotion in spontaneous speech (children giving commands to a pet robot). The difference in classification performance between corrected and automatically extracted pitch features turns out to be consistent but not very pronounced.
Keywords: pitch; automatic extraction; manual correction; automatic classification

The Impact of Specular Highlights on 3D-2D Face Recognition - Christlein, Vincent; Riess, Christian; Angelopoulou, Elli; Evangelopoulos, Georgios; Kakadiaris, Ioannis (Biometric and Surveillance Technology for Human and Activity Identification, 2013)
Keywords: Reectance model; Face recognition; Specularity removal; Specular highlights

The Influence of Dataset Partitioning on Dysfluency Detection Systems - Bayerl, Sebastian P.; Wagner, Dominik; Nöth, Elmar; Bocklet, Tobias; Riedhammer, Korbinian (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
This paper empirically investigates the influence of different data splits and splitting strategies on the performance of dysfluency detection systems. For this, we perform experiments using wav2vec 2.0 models with a classification head as well as support vector machines (SVM) in conjunction with the features extracted from the wav2vec 2.0 model to detect dysfluencies. We train and evaluate the systems with different non-speaker-exclusive and speaker-exclusive splits of the Stuttering Events in Podcasts (SEP-28k) dataset to shed some light on the variability of results w.r.t. to the partition method used. Furthermore, we show that the SEP-28k dataset is dominated by only a few speakers, making it difficult to evaluate. To remedy this problem, we created SEP-28k-Extended (SEP-28k-E), containing semi-automatically generated speaker and gender information for the SEP-28k corpus, and suggest different data splits, each useful for evaluating other aspects of methods for dysfluency detection.
CRIS-Team Scopus Importer:2022-10-14
Keywords: dysfluencies; pathological speech; SEP-28k; stuttering

The INTERSPEECH 2009 Emotion Challenge - Schuller, Björn; Steidl, Stefan; Batliner, Anton (Proceedings of Interspeech 2009, 2009)
The last decade has seen a substantial body of literature on the recognition of emotion from speech. However, in comparison to related speech processing tasks such as Automatic Speech and Speaker Recognition, practically no standardised corpora and test-conditions exist to compare performances under exactly the same conditions. Instead a multiplicity of evaluation strategies employed – such as cross-validation or percentage splits without proper instance definition – prevents exact reproducibility. Further, in order to face more realistic scenarios, the community is in desperate need of more spontaneous and less prototypical data. This INTERSPEECH 2009 Emotion Challenge aims at bridging such gaps between excellent research on human emotion recognition from speech and low compatibility of results. The FAU Aibo Emotion Corpus serves as basis with clearly defined test and training partitions incorporating speaker independence and different room acoustics as needed in most real-life settings. This paper introduces the challenge, the corpus, the features, and benchmark results of two popular approaches towards emotion recognition from speech.
Keywords: emotion; challenge; feature types; classification

The INTERSPEECH 2010 Paralinguistic Challenge - Age, Gender, and Affect - Schuller, Björn; Steidl, Stefan; Batliner, Anton; Burkhardt, Felix; Devillers, Laurence; Müller, Christian; Narayanan, Shrikanth (Proceedings of Interspeech, 2010)
Most paralinguistic analysis tasks are lacking agreed-upon evaluation procedures and comparability, in contrast to more ‘traditional’ disciplines in speech analysis. The INTERSPEECH 2010 Paralinguistic Challenge shall help overcome the usually low compatibility of results, by addressing three selected sub-challenges. In the Age Sub-Challenge, the age of speakers has to be determined in four groups. In the Gender Sub-Challenge, a three-class classification task has to be solved and finally, the Affect Sub-Challenge asks for speakers’ interest in ordinal representation. This paper introduces the conditions, the Challenge corpora “aGender” and “TUM AVIC” and standard feature sets that may be used. Further, baseline results are given.
Keywords: paralinguistic challenge; age; gender; affect

The INTERSPEECH 2011 Speaker State Challenge - Schuller, Björn; Steidl, Stefan; Batliner, Anton; Schiel, Florian; Krajewski, Jarek (Proceedings of the 12th Annual Conference of the International Speech Communication Association (INTERSPEECH 2011), 2011)
While the first open comparative challenges in the field of paralinguistics targeted more ‘conventional’ phenomena such as emotion, age, and gender, there still exists a multiplicity of not yet covered, but highly relevant speaker states and traits. The INTERSPEECH 2011 Speaker State Challenge thus addresses two new sub-challenges to overcome the usually low compatibility of results: In the Intoxication Sub-Challenge, alcoholisation of speakers has to be determined in two classes; in the Sleepiness Sub-Challenge, another two-class classification task has to be solved. This paper introduces the conditions, the Challenge corpora “Alcohol Language Corpus” and “Sleepy Language Corpus”, and a standard feature set that may be used. Further, baseline results are given.
Keywords: Speaker State Challenge; intoxication; sleepiness

The INTERSPEECH 2012 Speaker Trait Challenge - Schuller, Björn; Steidl, Stefan; Batliner, Anton; Nöth, Elmar; Vinciarelli, Alessandro; Burkhardt, Felix; van Son, Rob; Weninger, Felix; Eyben, Florian; Bocklet, Tobias; Mohammadi, Gelareh; Weiss, Benjamin (Proceedings of the 13th Annual Conference of the International Speech Communication Association (INTERSPEECH 2012), 2012)
The INTERSPEECH 2012 Speaker Trait Challenge provides for the first time a unified test-bed for ‘perceived’ speaker traits: Personality in the five OCEAN personality dimensions, likability of speakers, and intelligibility of pathologic speakers. In this paper, we describe these three Sub-Challenges, Challenge conditions, baselines, and a new feature set by the openSMILE toolkit, provided to the participants.
Keywords: computational paralinguistics; speaker traits; personality; likability; pathology

The INTERSPEECH 2013 Computational Paralinguistics Challenge: Social Signals, Conflict, Emotion, Autism - Schuller, Björn; Steidl, Stefan; Batliner, Anton; Vinciarelli, Alessandro; Scherer, Klaus; Ringeval, Fabien; Chetouani, Mohamed; Weninger, Felix; Eyben, Florian; Marchi, Erik; Martillaro, Marcello; Salamin, Hugues; et al. (Proceedings of the 14th Annual Conference of the International Speech Communication Association (INTERSPEECH 2013), 2013)
The INTERSPEECH 2013 Computational Paralinguistics Challenge provides for the first time a unified test-bed for Social Signals such as laughter in speech. It further introduces conflict in group discussions as a new task and deals with autism and its manifestations in speech. Finally, emotion is revisited as task, albeit with a broader range of overall twelve enacted emotional states. In this paper, we describe these four Sub-Challenges, their conditions, baselines, and a new feature set by the openSMILE toolkit, provided to the participants.
Keywords: computational paralinguistics; challenge; social signals; conflict; emotion; autism

The INTERSPEECH 2014 Computational Paralinguistics Challenge: Cognitive & Physical Load - Schuller, Björn; Steidl, Stefan; Batliner, Anton; Epps, Julien; Eyben, Florian; Ringeval, Fabien; Marchi, Erik; Zhang, Yue (Proceedings of the 15th Annual Conference of the International Speech Communication Association (INTERSPEECH 2014), 2014)
The INTERSPEECH 2014 Computational Paralinguistics Challenge provides for the first time a unified test-bed for the automatic recognition of speakers' cognitive and physical load in speech. In this paper, we describe these two Sub-Challenges, their conditions, baseline results and experimental procedures, as well as the COMPARE baseline features generated with the openSMILE toolkit and provided to the participants in the Challenge.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.theint_7
Keywords: computational paralinguistics; cognitive load; physical load; challenge

The INTERSPEECH 2015 computational paralinguistics challenge: Nativeness, Parkinson's & eating condition - Schuller, Björn; Steidl, Stefan; Batliner, Anton; Hantke, Simone; Hönig, Florian Thomas; Orozco Arroyave, Juan Rafael; Nöth, Elmar; Zhang, Yue; Weninger, Felix (16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015, 2015)
The INTERSPEECH 2015 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: The estimation of the degree of nativeness, the neurological state of patients with Parkinson's condition, and the eating conditions of speakers, i. e., whether and which food type they are eating in a seven-class problem. In this paper, we describe these sub-challenges, their conditions, and the baseline feature extraction and classifiers, as provided to the participants.
Keywords: Challenge; Computational Paralinguistics; Degree of Nativeness; Eating Condition; Parkinson's Condition

The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats - Schuller, Bjorn W.; Steidl, Stefan; Batliner, Anton; Marschik, Peter B.; Baumeister, Harald; Dong, Fengquan; Hantke, Simone; Pokorny, Florian B.; Rathner, Eva-Maria; Bartl-Pokorny, Katrin D.; Einspieler, Christa; Zhang, Dajie; et al. (19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6, 2018)
CRIS-Team WoS Importer:2019-06-04

The INTERSPEECH 2019 Computational Paralinguistics Challenge: Styrian Dialects, Continuous Sleepiness, Baby Sounds & Orca Activity - Schuller, Björn; Batliner, Anton; Bergler, Christian; Porkony, Florian B.; Krajewski, Jarek; Cychosz, Margaret; Vollmann, Ralf; Roelen, Sonja-Dana; Schnieder, Sebastian; Bergelson, Elika; Cristia, Alejandrina; Seidl, Amanda; et al. (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2019, 2019)
Keywords: Computational Paralinguistics; Challenge; Styrian Dialects; Sleepiness; Baby Sounds; Orca Activity

The INTERSPEECH 2020 Computational Paralinguistics Challenge: Elderly Emotion, Breathing & Masks - Schuller, Björn; Batliner, Anton; Bergler, Christian; Messner, Eva-Maria; Hamilton, Antonia; Amiriparian, Shahin; Baird, Alice; Rizos, Georgios; Schmitt, Maximilian; Stappen, Lukas; Baumeister, Harald; MacIntyre, Alexis Deighton; et al. (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020, 2020)
Keywords: Computational Paralinguistics; Challenge; Elderly Emotion; Breathing; Speech under Mask

The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates - Schuller, Björn; Batliner, Anton; Bergler, Christian; Mascolo, Cecilia; Han, Jing; Lefter, Iulia; Kaya, Heysem; Amiriparian, Shahin; Baird, Alice; Stappen, Lukas; Ottl, Sandra; Gerczuk, Maurice; et al. (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2021, 2021)
Keywords: Computational Paralinguistics; Challenge; COVID-19; Escalation; Primates

The missing information principle in computer vision - Hornegger, Joachim; Niemann, Heinrich (3-D Scene Acquisition, Modeling and Understanding, 1994)

The missing information principle in computer vision - Hornegger, Joachim; Niemann, Heinrich (International Journal of Computer and Information Technology, 1994)

The Notary in the Haystack – Countering Class Imbalance in Document Processing with CNNs - Leipert, Martin; Vogeler, Georg; Seuret, Mathias; Maier, Andreas; Christlein, Vincent (Document Analysis Systems, 2020)
Notarial instruments are a category of documents. A notarial instrument can be distinguished from other documents by its notary sign, a prominent symbol in the certificate, which also allows to identify the document’s issuer. Naturally, notarial instruments are underrepresented in regard to other documents. This makes a classification difficult because class imbalance in training data worsens the performance of Convolutional Neural Networks. In this work, we evaluate different countermeasures for this problem. They are applied to a binary classification and a segmentation task on a collection of medieval documents. In classification, notarial instruments are distinguished from other documents, while the notary sign is separated from the certificate in the segmentation task. We evaluate different techniques, such as data augmentation, under- and oversampling, as well as regularizing with focal loss. The combination of random minority oversampling and data augmentation leads to the best performance. In segmentation, we evaluate three loss-functions and their combinations, where only class-weighted dice loss was able to segment the notary sign sufficiently. <br

The papilla as screening parameter for early diagnosis of glaucoma - Michelson, Georg; Hornegger, Joachim; Wärntges, Simone; Lausen, Berthold (Deutsches Ärzteblatt, 2008)
Introduction: In the industrialized countries 67 million people suffer from glaucoma, which represents the third most common cause of blindness and thus has a high economic impact. Early diagnosis of glaucoma, which does not necessarily involve raised intraocular pressure, is essential because by the time the patient notices functional impairment the damage is irreversible. Early treatment can decrease the rate of blindness 20 years later by about 50%. Methods: Selective literature review and clinical investigation of early glaucoma detection and of screening methods. Results: Currently, no evidence-based recommendations for glaucoma screening can be found in the literature. No single method or combination of screening procedures can be recommended unambiguously on economic grounds. Discussion: From the clinical perspective sensitive, specific, and cost-effective glaucoma screening seems feasible. The high-risk group would need to be defined on the basis of age and family history. A two-stage screening process would then have to be established with initial computer-supported telemedical sorting followed by telemedical ophthalmological diagnosis of cases selected for clarification.

The PF-STAR Children's Speech Corpus - Batliner, Anton; Blomberg, Mats; D'Arcy, Shona; Elenius, Daniel; Giuliani, Diego; Gerosa, Matteo; Hacker, Christian; Russell, Martin; Steidl, Stefan; Wong, Michael (Proceedings of the 9th European Conference on Speech Communication and Technology, 2005)
This paper describes the corpus of recordings of children’s speech which was collected as part of the EU FP5 PF_STAR project. The corpus contains more than 60 hours of speech, including read and imitated native-language speech in British English, German and Swedish, read and imitated non-native-language English speech from German, Italian and Swedish children, and native-language spontaneous and emotional speech in English and German.

The Phonetic Footprint of Covid-19 - Klumpp, Philipp; Bocklet, Tobias; Arias Vergara, Tomás; Vasquez Correa, Juan; Pérez Toro, Paula Andrea; Bayerl, Sebastian; Orozco-Arroyave, Juan Rafael; Nöth, Elmar (Proc. Interspeech 2021, 2021)

The Phonetic Footprint of Parkinson's Disease - Klumpp, Philipp; Arias Vergara, Tomás; Vásquez-Correa, Juan Camilo; Pérez Toro, Paula Andrea; Orozco-Arroyave, Juan Rafael; Batliner, Anton; Nöth, Elmar (Computer Speech and Language, 2021)
As one of the most prevalent neurodegenerative disorders, Parkinson’s disease (PD) has a significant impact on the fine motor skills of patients. The complex interplay of different articulators during speech production and realization of required muscle tension become increasingly difficult, thus leading to a dysarthric speech. Characteristic patterns such as vowel instability, slurred pronunciation and slow speech can often be observed in the affected individuals and were analyzed in previous studies to determine the presence and progression of PD. In this work, we used a phonetic recognizer trained exclusively on healthy speech data to investigate how PD affected the phonetic footprint of patients. We rediscovered numerous patterns that had been described in previous contributions although our system had never seen any pathological speech previously. Furthermore, we could show that intermediate activations from the neural network could serve as feature vectors encoding information related to the disease state of individuals. We were also able to directly correlate the expert-rated intelligibility of a speaker with the mean confidence of phonetic predictions. Our results support the assumption that pathological data is not necessarily required to train systems that are capable of analyzing PD speech.<br
Keywords: Parkinson’s Disease; Phonetic Analysis; Phoneme Recognition; Pathological Speech

The Prosody of Pet Robot Directed Speech: Evidence from Children - Batliner, Anton; Steidl, Stefan; Biersack, Sonja (Proc. Speech Prosody, 3rd International Conference, 2006)
In this paper, we present a database with emotional children’s speech in a human-robot scenario: the children were giving instructions to Sony’s pet robot dog AIBO, with AIBO showing both obedient and disobedient behaviour. In such a scenario, a specific type of partner-centered interaction can be observed. We aimed at finding prosodic correlates of children’s emotional speech and were interested to see which speech registers children use when talking to AIBO. For interpretation, we left the weighting and categorization of prosodic features to a statistic classifier. The parameters found to be most important were word duration, average energy, variation in pitch and energy, and harmonics-to-noise ratio. The data moreover suggests that the children used a register that resembled mostly child-directed and pet-directed speech and to some extent computer-directed speech.

The Random Forest Classifier in WEKA: Discussion and New Developments for Imbalanced Data - Amrehn, Mario; Mualla, Firas; Angelopoulou, Elli; Steidl, Stefan; Maier, Andreas (2018)
Data analysis and machine learning have become an integrative part of the modern scientific methodology, providing automated techniques to predict further information based on observations. One of these classification and regression techniques is the random forest approach. Those decision tree based predictors are best known for their good computational performance and scalability. However, in case of severely imbalanced training data, as often seen in medical studies' data with large control groups, the training algorithm or the sampling process has to be altered in order to improve the prediction quality for minority classes. In this work, a balanced random forest approach for WEKA is proposed. Furthermore, the prediction quality of the unmodified random forest implementation and the new balanced random forest version for WEKA are evaluated against reference implementations in R. Two-class problems on balanced data sets and imbalanced medical studies' data are investigated. A superior prediction quality using the proposed method for imbalanced data is shown compared to the other three techniques

The Recognition of Emotion - Batliner, Anton; Huber, Richard; Niemann, Heinrich; Nöth, Elmar; Spilker, Jörg; Fischer, Kerstin (Verbmobil: Foundations of Speech-to-Speech Translations, 2000)

The Relevance of Feature Type for the Automatic Classification of Emotional User States: Low Level Descriptors and Functionals - Schuller, Björn; Batliner, Anton; Seppi, Dino; Steidl, Stefan; Vogt, Thurid; Wagner, Johannes; Devillers, Laurence; Vidrascu, Laurence; Amir, Noam; Kessous, Loic; Aharonson, Vered (Proceedings Interspeech, 2007)
In this paper, we report on classification results for emotional user states (4 classes, German database of children interacting with a pet robot). Six sites computed acoustic and linguistic features independently from each other, following in part different strategies. A total of 4244 features were pooled together and grouped into 12 low level descriptor types and 6 functional types. For each of these groups, classification results using Support Vector Machines and Random Forests are reported for the full set of features, and for 150 features each with the highest individual Information Gain Ratio. The performance for the different groups varies mostly between ≈50% and ≈60%.
Keywords: emotional user states; automatic classification; feature types; functionals

The Spectral Relation between the Cube-Connected Cycles and the Shuffle-Exchange Network - Riess, Christian; Strehl, Volker; Wanka, Rolf (Proc. 10th Workshop on Parallel Systems and Algorithms (PASA) of the 25th Int. Conf. on Architecture of Computing Systems (ARCS), 2012)

Thickness related textural properties of retinal nerve fiber layer in color fundus images - Odstrcilik, Jan; Kolar, Radim; Tornow, Ralf-Peter; Jan, Jiri; Budai, Attila; Mayer, Markus Anton; Vodakova, Martina; Lämmer, Robert; Lamos, Martin; Kuna, Zdenek; Gazarek, Jiri; Kubena, Tomas; et al. (Computerized Medical Imaging and Graphics, 2014)
Images of ocular fundus are routinely utilized in ophthalmology. Since an examination using fundus camera is relatively fast and cheap procedure, it can be used as a proper diagnostic tool for screening of retinal diseases such as the glaucoma. One of the glaucoma symptoms is progressive atrophy of the retinal nerve fiber layer (RNFL) resulting in variations of the RNFL thickness. Here, we introduce a novel approach to capture these variations using computer-aided analysis of the RNFL textural appearance in standard and easily available color fundus images. The proposed method uses the features based on Gaussian Markov random fields and local binary patterns, together with various regression models for prediction of the RNFL thickness. The approach allows description of the changes in RNFL texture, directly reflecting variations in the RNFL thickness. Evaluation of the method is carried out on 16 normal ("healthy") and 8 glaucomatous eyes. We achieved significant correlation (normals: ?=0.72±0.14; p<<0.05, glaucomatous: ?=0.58±0.10; p<<0.05) between values of the model predicted output and the RNFL thickness measured by optical coherence tomography, which is currently regarded as a standard glaucoma assessment device. The evaluation thus revealed good applicability of the proposed approach to measure possible RNFL thinning.
EVALuna2:21169

Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT - Maier, Andreas; Wigström, Lars; Hofmann, Hannes; Hornegger, Joachim; Zhu, Lei; Strobel, Norbert; Fahrig, Rebecca (Medical Physics, 2011)
Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8.9-fold speed-up of the processing (from 1336 to 150 s). Conclusions: Adaptive anisotropic filtering has the potential to substantially improve image quality and/or reduce the radiation dose required for obtaining 3D image data using cone beam CT. © 2011 American Association of Physicists in Medicine.

Three-Dimensional Enhanced Imaging of Vitreoretinal Interface in Diabetic Retinopathy Using Swept-Source Optical Coherence Tomography - Adhi, Mehreen; Badaro, Emmerson; Liu, Jonathan J.; Kraus, Martin; Baumal, Caroline R.; Witkin, Andre J.; Hornegger, Joachim; Fujimoto, James G.; Duker, Jay S.; Waheed, Nadia K. (American Journal of Ophthalmology, 2016)
CONCLUSION: SSOCT with motion-correction and vitreous windowing provides wide-field 3-dimensional information of vitreoretinal interface in diabetic eyes. This may be useful in assessing progression of retinopathy, planning diabetic vitreous surgery, and predicting treatment outcomes. (C) 2016 by Elsevier Inc. All rights reserved.

Thrombus Detection in Non-Contrast Head CT using Graph Deep Learning - Popp, Antonia; Taubmann, Oliver; Thamm, Florian; Ditt, Hendrik; Maier, Andreas; Breininger, Katharina (Bildverarbeitung in der Medizin, 2022)
In case of an acute ischemic stroke, rapid diagnosis and removal of the occluding thrombus (blood clot) are crucial for a successful recovery. We present an automated thrombus detection system for noncontrast computed tomography (NCCT) images to improve the clinical workflow, where NCCT is typically acquired as a first-line imaging tool to identify the type of the stroke. The system consists of a candidate detection model and a subsequent classification model. The detection model generates a volumetric heatmap from the NCCT and extracts multiple potential clot candidates, sorted by their likeliness in descending order. The classification model performs reprioritization of these candidates using graph-based deep learning methods, where the candidates are no longer considered independently, but in a global context. It was optimized to classify the candidates as clot or no clot. The candidate detection model, which also serves as a baseline, yields a ROC AUC of 79.8%, which could be improved to 85.2% by the proposed graph-based classification model.<br

Time Dependent ARMA for Automatic Recognition of Fear-Type Emotions in Speech - Vasquez-Correa, J. C.; Orozco-Arroyave, J. R.; Arias-Londono, J. D.; Vargas-Bonilla, J. F.; Avendano, L. D.; Nöth, Elmar (Springer-verlag, 2015)
The speech signals are non-stationary processes with changes in time and frequency. The structure of a speech signal is also affected by the presence of several paralinguistics phenomena such as emotions, pathologies, cognitive impairments, among others. Non-stationarity can be modeled using several parametric techniques. A novel approach based on time dependent auto-regressive moving average (TARMA) is proposed here to model the non-stationarity of speech signals. The model is tested in the recognition of "fear-type" emotions in speech. The proposed approach is applied to model syllables and unvoiced segments extracted from recordings of the Berlin and enterface05 databases. The results indicate that TARMA models can be used for the automatic recognition of emotions in speech.
Keywords: Non-stationary signals; Speech emotion recognition; Continuous speech; Time dependent ARMA models

Timed Up-and-Go phase segmentation in Parkinson's disease patients using unobtrusive inertial sensors - Hauer, Roland; Eskofier, Björn; Barth, Jens; Klucken, Jochen; Reinfelder, Samuel; Klucken, Jochen (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015)
A widely accepted functional motor test for measuring basic mobility capabilities is the `Timed Up-and-Go' (TUG) test. Although several basic mobility tasks are included, only the total time is used as outcome parameter. It has been shown that timings of sub-phases can be used as relevant clinical parameters for the assessment of Parkinson's disease patients. A variety of systems and methods have been proposed for instrumenting the TUG test, but only limited information has been published regarding phase classification. In this paper an automated TUG phase classification methodology is proposed and validated in a study with 16 Parkinson's disease patients. Statistical, signal energy, chronological and gait features were extracted from acceleration and orientation signals of shoe mounted inertial measurement units. The phases `sit to walk', `walking', `first turn', `second turn' and `turn to sit' were segmented in a two stage classifier approach. Strides were used for a separation of the walking phase and classifiers like NaiveBayes, k-Nearest-Neighbor, Support Vector Machine (SVM) and Random Forest for the final phase segmentation. SVM performed best with a mean sensitivity of 81.80 % over all phases. Additionally, the impact of UPDRS and Hoehn & Yahr ratings on the phase times was assessed. The proposed methodology could be used to analyze gait parameters of sub-phases like stride length, stride time, foot clearance, heel-strike or toe-off angle for an improved assessment of Parkinson's disease patients.
EVALuna2:25339

Time-of-Flight 3-D Endoscopy - Penne, Jochen; Höller, Kurt Emmerich; Stürmer, Michael; Schrauder, Thomas; Schneider, Armin; Engelbrecht, Rainer; Feußner, Hubertus; Schmauß, Bernhard; Hornegger, Joachim (Springer-verlag, 2009)
This paper describes the first accomplishment of the Time-of-Flight (ToF) measurement principle via endoscope optics. The applicability of the approach is verified by in-vitro experiments. Off-the-shelf ToF camera sensors enable the per-pixel, on-chip, real-time, marker-less acquisition of distance information. The transfer of the emerging ToF measurement technique to endoscope optics is the basis for a new generation of ToF rigid or flexible 3-D endoscopes. No modification of the endoscope optic itself is necessary as only an enhancement of illumination unit and image sensors is necessary. The major contribution of this paper is threefold: First, the accomplishment of the ToF measurement principle via endoscope optics; second, the development and validation of a complete calibration and post-processing routine; third, accomplishment of extensive in-vitro experiments. Currently, a depth measurement precision of 0.89 mm at 20 fps with 3072 3-D points is achieved. © 2009 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.timeof_24

Time-of-Flight sensor for patient positioning - Schaller, Christian; Adelt, Andre; Penne, Jochen; Hornegger, Joachim (Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2009)
In this paper we present a system that uses Time-of-Flight (ToF) technology to correct the position of a patient in respect to a previously acquired reference surface. A ToF sensor enables the acquisition of a 3-D surface model containing more than 25,000 points using a single sensor in real time. One advantage of this technology is that the high lateral resolution makes it possible to accurately compute translation and rotation of the patient in respect to a reference surface. We are using an Iterative Closest Point (ICP) algorithm to determine the 6 degrees of freedom (DOF) vector. Current results show that for rigid phantoms it is possible to obtain an accuracy of 2.88 mm and 0.28? respectively. Tests with human persons validate the robustness and stability of the proposed system. We achieve a mean registration error of 3.38 mm for human test persons. Potential applications for this system can be found within radiotherapy or multimodal image acquisition with different devices. © 2009 SPIE.

Time-of-flight sensor for respiratory motion gating - Schaller, Christian; Penne, Jochen; Hornegger, Joachim (Medical Physics, 2008)
In this technical note we present a system that uses time-of-flight (ToF) technology to acquire a real-time multidimensional respiratory signal from a 3D surface reconstruction of the patient's chest and abdomen without the use of markers. Using ToF sensors it is feasible to acquire a 3D model in real time with a single sensor. An advantage of ToF sensors is that their high lateral resolution makes it possible to define multiple regions of interest to compute an anatomy-adaptive multi-dimensional respiratory signal. We evaluated the new approach by comparing a ToF based respiratory signal with the signal acquired by a commercially available external respiratory gating system and achieved an average correlation coefficient of 0.88. © 2008 American Association of Physicists in Medicine.

TMAP @ IFE - A Framework for Guiding and Monitoring Thermal Ablations - Rothgang, Eva; Gilson, Wesley D.; Roland, Jörg; Hornegger, Joachim; Lorenz, Christine H. (Proceedings of International Society for Magnetic Resonance in Medicine (ISMRM), 2010)

ToF Meets RGB: Novel Multi-Sensor Super-Resolution for Hybrid 3-D Endoscopy - Köhler, Thomas; Haase, Sven; Bauer, Sebastian; Wasza, Jakob; Kilgus, Thomas; Maier-Hein, Lena; Feußner, Hubertus; Hornegger, Joachim (Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013, Lecture Notes in Computer Science, 2013)

ToF/RGB Sensor Fusion for 3-D Endoscopy - Haase, Sven; Forman, Christoph; Kilgus, Thomas; Bammer, Roland; Maier-Hein, Lena; Hornegger, Joachim (Current Medical Imaging Reviews, 2013)

ToF/RGB Sensor Fusion for Augmented 3-D Endoscopy using a Fully Automatic Calibration Scheme - Haase, Sven; Forman, Christoph; Kilgus, Thomas; Bammer, Roland; Maier-Hein, Lena; Hornegger, Joachim (Bildverarbeitung für die Medizin, 2012)

Topic Spotting using Subword Units - Warnke, Volker; Harbeck, Stefan; Niemann, Heinrich; Nöth, Elmar (9. Aachener Kolloqium "Signaltheorie", Bild- und Sprachsignale, 1997)

Torsional Heart Motion in Cone-beam Computed Tomography Reconstruction - Unberath, Mathias; Mentl, Katrin; Taubmann, Oliver; Achenbach, Stephan; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Proceedings of the Fully3D, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.torsio_3

Total retinal blood flow measurement with ultrahigh speed swept source/Fourier domain OCT - Baumann, Bernhard; Potsaid, Benjamin; Kraus, Martin; Liu, Jonathan J.; Huang, David; Hornegger, Joachim; Cable, Alex E.; Duker, Jay S.; Fujimoto, James G. (Biomedical Optics Express, 2011)
Doppler OCT provides depth-resolved information on flow in biological tissues. In this article, we demonstrate ultrahigh speed swept source/Fourier domain OCT for visualization and quantitative assessment of retinal blood flow. Using swept laser technology, the system operated in the 1050-nm wavelength range at a high axial scan rate of 200 kHz. The rapid imaging speed not only enables volumetric imaging with high axial scan densities, but also enables measurement of high flow velocities in the central retinal vessels. Deep penetration in the optic nerve and lamina cribrosa was achieved by imaging at 1-μm wavelengths. By analyzing en-face images extracted from 3D Doppler data sets, absolute flow in single vessels as well as total retinal blood flow was measured using a simple and robust protocol that does not require measurement of Doppler angles. The results from measurements in healthy eyes suggest that ultrahigh speed swept source/Fourier domain OCT could be a promising technique for volumetric imaging of retinal vasculature and quantitation of retinal blood flow in a wide range of retinal diseases. © 2011 Optical Society of America.

Total Variation Regularization Method for 3-D Rotational Coronary Angiography - Wu, Haibo; Rohkohl, Christopher; Hornegger, Joachim (Bildverarbeitung für die Medizin 2011, 2011)

Touchscreen ohne Touch - Berührungslose 3D Gesten-Interaktion für den Operationssaal (Touchscreen without Touch - Touchless 3D Gesture Interaction for the Operation Room) - Penne, Jochen; Soutschek, Stefan; Stürmer, Michael; Schaller, Christian; Placht, Simon; Kornhuber, Johannes; Hornegger, Joachim (i-com : Zeitschrift für interaktive und kooperative Medien, 2009)

Toward analyzing mutual interference on infrared-enabled depth cameras - Adams Seewald, Lucas; Facco Rodrigues, Vinicius; Ollenschläger, Malte; Stoffel Antunes, Rodolfo; Andre da Costa, Cristiano; da Rosa Righi, Rodrigo; da Silveira Junior, Luiz Gonzaga; Maier, Andreas; Eskofier, Björn; Fahrig, Rebecca (Computer Vision and Image Understanding, 2019)
Camera setups with multiple devices are a key aspect of ambient monitoring applications. These types of setups can result in data corruption when applied to recent RGB-D camera models because of mutual interference of the infrared light emitters employed by such devices. Consequently, the behavior of such interference must be appropriately evaluated to provide data that will allow monitoring systems to handle possible errors introduced into the data captured by depth sensors. However, multi-device setups have been explored in few studies in the current literature, especially in terms of the detailed measurements of the interference’s effect during long-term usage of RGB-D cameras. In this context, a methodology to evaluate the effect of mutual interference on the accuracy and precision of measured depth values is proposedin this article. The results of a series of experiments with different setups based on multiple depth cameras are explored. These setups include three devices that were widely used in studies in the computer vision literature related to depth imaging: the Microsoft Kinect v2 and two Intel RealSense models: R200 and D415. The experimental results indicate that the Kinect v2 yields considerably more stable depth readings than the RealSense R200 in single-camera scenarios, even considering the influence of the warm-up time that is characteristic of time-of-flight devices such as the Kinect v2. In multi-device setups, the Kinect v2 displays periodic peaks of mutual interference that increase in intensity depending on the distance between the cameras, with short-range setups yielding higher interference peaks. Further, the addition of more devices can potentially increase the duration of some interference peaks, albeit their intensity is not greatly affected. In long-range setups, the measured interference is small considering the experiments’ length, with the proportion of bad pixels among all captured frames ranging from 3.74% to 3.97% in a setup comprising three depth cameras. In turn, multi-device setups comprising the RealSense models are not affected by prejudicial interference peaks. In long-range setups, the instability of the R200 leads to its results being less accurate and precise than those of the Kinect v2 under mutual interference. However, in close range multi-device setups, the high interference peaks observed with the Kinect v2 render the RealSense models a more stable alternative.
Keywords: Computer vision; Interference; Multi-camera; Depth camera; Evaluation

Toward automatic detection of vessel stenoses in cerebral 3D DSA volumes - Mualla, Firas; Prümmer, Marcus; Hahn, Dieter; Hornegger, Joachim (Physics in Medicine and Biology, 2012)
Vessel diseases are a very common reason for permanent organ damage, disability and death. This fact necessitates further research for extracting meaningful and reliable medical information from the 3D DSA volumes. Murray's law states that at each branch point of a lumen-based system, the sum of the minor branch diameters each raised to the power x, is equal to the main branch diameter raised to the power x. The principle of minimum work and other factors like the vessel type, impose typical values for the junction exponent x. Therefore, deviations from these typical values may signal pathological cases. In this paper, we state the necessary and the sufficient conditions for the existence and the uniqueness of the solution for x. The second contribution is a scale- and orientation- independent set of features for stenosis classification. A support vector machine classifier was trained in the space of these features. Only one branch was misclassified in a cross validation on 23 branches. The two contributions fit into a pipeline for the automatic detection of the cerebral vessel stenoses. © 2012 Institute of Physics and Engineering in Medicine.

Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data - Köhler, Thomas; Bätz, Michel; Naderi Boldaji, Farzad; Kaup, André; Maier, Andreas; Rieß, Christian (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019)
UnivIS-Import:2019-08-15:Pub.2019.tech.IMMD.IMMD5.toward_0

Toward quantitative OCT angiography: visualizing flow impairment using variable interscan time analysis (VISTA) - Ploner, Stefan; Moult, Eric M.; Waheed, Nadia K.; Husvogt, Lennart; Schottenhamml, Julia; Lee, Byungkun; Hornegger, Joachim; Duker, Jay S.; Rosenfeld, Philip; Fujimoto, James G. (Investigative Ophthalmology & Visual Science, 2016)
Purpose : Optical coherence tomography angiography (OCTA) is a promising modality for visualizing vascular alterations in a variety of ocular diseases, including age-related macular degeneration (AMD) and diabetic retinopathy (DR). However, most OCTA techniques have limited dynamic range and do not provide information about the relative flow velocities within the imaged vasculature. Visualizing relative flow speed would be especially valuable when assessing diseases in which progression is linked to flow impairment, not just vasculature loss. Methods : OCTA imaging of patients with various stages of AMD and DR was performed with a 1050 nm swept source OCT system at a 400 kHz A-scan rate using a 5 repeated B-scan protocol. Variable interscan time analysis (VISTA) was used to compute the OCTA decorrelation data from pairs of B-scans having 1.5 ms and 3.0 ms separations. The two resulting OCTA data sets were used to compute relative flow speeds, which were then mapped to a color space for display. Results : The VISTA flow maps for a representative patient with non-proliferative DR (NPDR), and for a patient with geographic atrophy (GA) are shown in Figures 1 and 2, respectively. The VISTA map of the NPDR eye shows slower flows associated with capillary loops in the OCTA image. The VISTA map of the GA eye shows slower flows in the area of atrophy, and on the borders of atrophy. Conclusions : A method for visualizing VISTA OCTA data is developed and used to differentiate flow speeds in DR and AMD eyes featuring GA. Differentiation of flow speeds is an important first step towards quantitative OCTA and may be useful for assessing vascular diseases at a reversible stag
UnivIS-Import:2018-09-11:Pub.2016.tech.IMMD.IMMD5.toward_7
Keywords: oct; octa; oct angiography; vista; variable interscan time analysis; blood flow

Toward Quantitative Optical Coherence Tomography Angiography: Visualizing Blood Flow Speeds in Ocular Pathology Using Variable Interscan Time Analysis - Ploner, Stefan; Moult, Eric M.; Choi, WooJhon; Waheed, Nadia K.; Lee, Byungkun; Novais, Eduardo A.; Cole, Emily D.; Potsaid, Benjamin; Husvogt, Lennart; Schottenhamml, Julia; Maier, Andreas; Rosenfeld, Philip; et al. (Retina (Philadelphia, Pa.), 2016)
Purpose: Currently available optical coherence tomography angiography systems provide information about blood flux but only limited information about blood flow speed. The authors develop a method for mapping the previously proposed variable interscan time analysis (VISTA) algorithm into a color display that encodes relative blood flow speed. Methods: Optical coherence tomography angiography was performed with a 1,050 nm, 400 kHz A-scan rate, swept source optical coherence tomography system using a 5 repeated B-scan protocol. Variable interscan time analysis was used to compute the optical coherence tomography angiography signal from B-scan pairs having 1.5 millisecond and 3.0 milliseconds interscan times. The resulting VISTA data were then mapped to a color space for display. Results: The authors evaluated the VISTA visualization algorithm in normal eyes (n = 2), nonproliferative diabetic retinopathy eyes (n = 6), proliferative diabetic retinopathy eyes (n = 3), geographic atrophy eyes (n = 4), and exudative age-related macular degeneration eyes (n = 2). All eyes showed blood flow speed variations, and all eyes with pathology showed abnormal blood flow speeds compared with controls. Conclusion: The authors developed a novel method for mapping VISTA into a color display, allowing visualization of relative blood flow speeds. The method was found useful, in a small case series, for visualizing blood flow speeds in a variety of ocular diseases and serves as a step toward quantitative optical coherence tomography angiograph
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.toward_9
Keywords: Age-related macular degeneration; Choroidal neovascularization; Nascent geographic atrophy; Ocular blood flow; Optical coherence tomography (OCT); Optical coherence tomography angiography (OCTA); Variable interscan time analysis (VISTA)

Towards 4-D Cardiac Reconstruction without ECG and Motion Periodicity using C-arm CT - Rohkohl, Christopher; Lauritsch, Günter; Prümmer, Marcus; Boese, Jan; Hornegger, Joachim (Proceedings of 10th Fully 3D Meeting and 2nd HPIR Workshop, 2009)

Towards a CRIS-integrated solution for University Press workflows - Melsheimer, Bastian; Jahn, Annika; Putnings, Markus; Valianos, Stelica; Walther, Marcus (CRIS2022: 15th International Conference on Current Research Information Systems, 2022)
Keywords: current research information systems; system interoperability; information exchange; research libraries; Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Towards a dual phase grating interferometer on clinical hardware - Bopp, Johannes; Ludwig, Veronika; Gallersdörfer, Michael; Seifert, Maria; Pelzer, Georg; Maier, Andreas; Anton, Gisela; Riess, Christian (Medical Imaging 2018: Physics of Medical Imaging, 2018)
Keywords: Phase contrast imaging; Talbot-Lau; Dual phase grating; Grating based interferometry

Towards a Feature Set for Sensor Data Fusion - Riess, Christian; Fuchs, Sven; Angelopoulou, Elli; et al. (2008)

Towards a language-independent intelligibility assessment of children with cleft lip and palate - Bocklet, Tobias; Maier, Andreas; Riedhammer, Korbinian Thomas; Nöth, Elmar (Proceedings of WOCCI 2009, 2009)
We describe a novel evaluation system for the intelligibility assessment of children with CLP on standardized tests. The system is solely based on standard cepstral features in form of MFCCs. No other information like word alignments is used. So the system can be easily adapted to other languages. For each child one GMM is created by adaptation of a UBM to the speaker-specific MFCCs. The components of this GMM are concatenated in order to create a so-called GMM supervector. These GMM supervectors are then used as meta features for an SVR. We evaluated our language-independent system on two different datasets of children suffering from CLP. One dataset contains recordings of 35 German children, where the children named different pictograms. The other dataset contains recordings of 14 Italian speaking children, who repeated standardized sentences. On both datasets we achieved high correlations: up to 0.81 for the German dataset and 0.83 for the Italian dataset. Copyright 2009 ACM.
Keywords: Acoustic analysis; Cleft lip and palate; Gaussian mixture models; Support vector regression

Towards an Algebraic Multigrid Method for Tomographic Image Reconstruction - Improving Convergence of ART - Köstler, Harald; Popa, Constantin; Prümmer, Marcus; Rüde, Ulrich (ECCOMAS CFD 06, 2006)
UnivIS-Import:2015-04-16:Pub.2006.tech.IMMD.lsinfs.toward_37

Towards an analytic model: Describing the effect of scan angle and slice thickness on the in-plane spatial resolution of calcications in digital breast tomosynthesis - Luckner, Christoph; Schebesch, Frank; Mertelmeier, Thomas; Fieselmann, Andreas; Maier, Andreas; Ritschl, Ludwig (Proc. of SPIE, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.toward_6
Keywords: DBT; tomosynthesis; scanning angle; slice thickness; in-plane resolution; analytic model; calcication

Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease - Vasquez Correa, Juan; Orozco-Arroyave, Juan Rafael; Bocklet, Tobias; Nöth, Elmar (Journal of Communication Disorders, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.toward_28
Keywords: Parkinson's disease; Phonation; Articulation; Prosody; Frenchay Dysarthria Assessment; Longitudinal analysis

Towards Arbitrary Noise Augmentation-Deep Learning for Sampling from Arbitrary Probability Distributions - Horger, Felix; Würfl, Tobias; Christlein, Vincent; Maier, Andreas (MACHINE LEARNING FOR MEDICAL IMAGE RECONSTRUCTION, MLMIR 2018, 2018)
CRIS-Team WoS Importer:2019-08-09

Towards Big Data for Activity Recognition: A Novel Database Fusion Strategy - Schuldhaus, Dominik; Leutheuser, Heike; Eskofier, Björn (9th International Conference on Body Area Networks, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.toward_23

Towards C-arm CT Reconstruction on Larrabee - Hofmann, Hannes; Keck, Benjamin; Rohkohl, Christopher; Hornegger, Joachim (Proceedings of 10th Fully 3D Meeting and 2nd HPIR Workshop, 2009)

Towards cartilage diagnosis in X-ray phase-contrast interferometry - Bopp, Johannes; Bartl, Peter; Ritschl, Ludwig; Radicke, Marcus; Maier, Andreas; Anton, Gisela; Riess, Christian (14th IEEE International Symposium on Biomedical Imaging, 2017)

Towards Clinical Application of a Laplace Operator-based Region of Interest Reconstruction Algorithm in C-arm CT - Xia, Yan; Hofmann, Hannes; Dennerlein, Frank; Müller, Kerstin; Schwemmer, Chris; Bauer, Sebastian; Chintalapani, Gouthami; Chinnadurai, Ponraj; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Medical Imaging, 2014)
It is known that a reduction of the field-of-view in 3-D X-ray imaging is proportional to a reduction in radiation dose. The resulting truncation, however, is incompatible with conventional reconstruction algorithms. Recently, a novel method for region of interest reconstruction that uses neither prior knowledge nor 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 2-D Laplace filtering and a 2-D Radon-based residual filtering step. In this paper, we present two variants of the original ATRACT. One is based on expressing the residual filter as an efficient 2-D convolution with an analytically derived kernel. The second variant is to apply ATRACT in 1-D to further reduce computational complexity. The proposed algorithms were evaluated by using a reconstruction benchmark, as well as two clinical data sets. The results are encouraging since the proposed algorithms achieve a speed-up factor of up to 245 compared to the 2-D Radon-based ATRACT. Reconstructions of high accuracy are obtained, e.g., even real-data reconstruction in the presence of severe truncation achieve a relative root mean square error of as little as 0.92% with respect to nontruncated data. © 1982-2012 IEEE.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.toward_3
Keywords: C-arm CT; dose reduction; image reconstruction; region of interest; truncation; truncation artifact

Towards Deformable Shape Modeling of the Left Atrium Using Non-Rigid Coherent Point Drift Registration - Koch, Martin; Bauer, Sebastian; Hornegger, Joachim; Strobel, Norbert (Bildverarbeitung für die Medizin 2013, 2013)

Towards End-to-End Deep Learning-based Writer Identification - Wang, Zhenghua; Maier, Andreas; Christlein, Vincent (INFORMATIK 2020, 2021)

Towards Fully Autonomous Aerial Inspection of Photovoltaic Power Plants - Gallwitz, Florian; Deitsch, Sergiu; Dalsaß, Manuel (Integration of Sustainable Energy Conference (iSEneC), 2016)

Towards Improving Solar Irradiance Forecasts with Methods from Computer Vision - Bernecker, David; Riess, Christian; Angelopoulou, Elli; Hornegger, Joachim (Pattern Recognition (Joint 34th DAGM and 36th OAGM Symposium), 2012)

Towards Intelligent Robust Detection of Anatomical Structures in Incomplete Volumetric Data - Ghesu, Florin-Cristian; Georgescu, Bogdan; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (Medical Image Analysis, 2018)
Robust and fast detection of anatomical structures represents an important component of medical image analysis technologies. Current solutions for anatomy detection are based on machine learning, and are generally driven by suboptimal and exhaustive search strategies. In particular, these techniques do not effectively address cases of incomplete data, i.e., scans acquired with a partial field-of-view. We address these challenges by following a new paradigm, which reformulates the detection task to teaching an intelligent artificial agent how to actively search for an anatomical structure. Using the principles of deep reinforcement learning with multi-scale image analysis, artificial agents are taught optimal navigation paths in the scale-space representation of an image, while accounting for structures that are missing from the field-of-view. The spatial coherence of the observed anatomical landmarks is ensured using elements from statistical shape modeling and robust estimation theory. Experiments show that our solution outperforms marginal space deep learning, a powerful deep learning method, at detecting different anatomical structures without any failure. The dataset contains 5043 3D-CT volumes from over 2000 patients, totaling over 2,500,000 image slices. In particular, our solution achieves 0% false-positive and 0% false-negative rates at detecting whether the landmarks are captured in the field-of-view of the scan (excluding all border cases), with an average detection accuracy of 2.78 mm. In terms of runtime, we reduce the detection-time of the marginal space deep learning method by 20–30 times to under 40 ms, an unmatched performance for high resolution incomplete 3D-CT data.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.toward_1
Keywords: Deep learning; Deep reinforcement learning; Incomplete 3D-data; M-estimator sample consensus; Multi-scale detection; Real-time detection; Robust statistical shape-modeling; Scale-space modeling

Towards in-vivo X-ray nanoscopy: The effect of motion on image quality - Mill, Leonid; Bier, Bastian; Syben-Leisner, Christopher; Kling, Lasse; Grüneboom, Anika; Christiansen, Silke; Schett, Georg; Maier, Andreas (Informatik aktuell, 2018)
Novel X-Ray Microscopy (XRM) systems allow to study the internal structure of a specimen on nanoscale. A possible use of this non-destructive technology is motivated in the medical research area. In-Vivo investigation of medication over a period of time and its effects on perfusion and bony structure might lead to a better understanding of drug mechanisms and diseases like Osteoporosis and could lead to new approaches to their treatment. The first step towards in-vivo XRM imaging is to investigate the suitability of recent XRM systems for this task and subsequently to determine the system parameters. In this context, the impact of mice motion on the image quality is studied in this work. This paper aims to simulate the effects of breathing motion and muscle relaxation of the mice on the reconstructed images, which already effects the projection images. We therefore assume a mouse’s respiration motion pattern, which happens four time during a single projection acquisitions, and the muscle relaxation movement due to anesthesia and simulate its impacts on image quality. Additionally, we show that a frame rate of at least 16 fps is needed to capture in-vivo movements in order to apply state-of-the-art motion correction methods.<br

Towards Material Decomposition on Large Field-of-View Flat Panel Photon-Counting Detectors - First in-vivo Results - Müller, Kerstin; Moiz, Ahmad; Spahn, Martin; Choi, Jang-Hwan; Reitz, Silke; Köster, Niko; Lu, Yanye; Fahrig, Rebecca; Maier, Andreas (Proceedings of the fourth international conference on image formation in x-ray computed tomography, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.toward_8

Towards Monitoring of Children's Speech - A Case Study - Maier, Andreas; Schuster, Maria; Nöth, Elmar (Workshop on Child, Computer, and Interaction 2008, 2008)

Towards more Reality in the Recognition of Emotional Speech - Schuller, Björn; Seppi, Dino; Batliner, Anton; Maier, Andreas; Steidl, Stefan (ICASSP, 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings, 2007)

Towards Mouse Bone X-ray Microscopy Scan Simulation - Fu, Weilin; Mill, Leonid; Seitz, Stephan; Geimer, Tobias; Kling, Lasse; Possart, Dennis; Christiansen, Silke; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Towards Optimal Channels for a Detection Channelized Hotelling Observer - Schebesch, Frank; Maier, Andreas (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2018-02-22:Pub.2017.tech.IMMD.IMMD5.toward_13

Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation - Voigt, Ingmar; et al. (Medical Image Analysis, 2017)
UnivIS-Import:2017-01-09:Pub.2017.tech.IMMD.IMMD5.toward
Keywords: Finite element biomechanical model; Mitral valve; Mitral valve closure simulation; Patient-specific model; Transesophageal echocardiography

Towards quantification of kidney stones using X-ray dark-field tomography - Hu, Shiyang; Yang, F.; Kaufmann, R.; Griffa, M.; Anton, Gisela; Maier, Andreas; Riess, Christian (14th IEEE International Symposium on Biomedical Imaging, 2017)

Towards quantitative SPECT: Error estimation of SPECT OSEM with 3D resolution recovery, attenuation correction and scatter correction - Zeintl, Johannes; Vija, A. H.; Yahil, A.; Ding, X.; Hornegger, Joachim; Kuwert, Torsten (2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008, 2008)
In this study we systematically investigate biases relevant to quantitative SPECT if OSEM with isotropic (3D) depth dependent resolution recovery (OSEM-3D), attenuation and scatter correction is used. We focus on the dependencies of activity estimation errors on the projection operator, structure size, pixel size, count density and reconstruction parameters. We use Tc-99m to establish a base line. Four Siemens low energy collimators (Low Energy Ultra High Resolution, Low Energy High Resolution, Low Energy All Purpose, Low Energy High Sensitivity) with geometric resolution between 4.4 mm and 13.1 mm at 10 cm distance and sensitivity between 100 cpm/μCi and 1020 cpm/μCi are tested with simulations of spheres with diameters between 9.8 mm and 168 mm in background. Pixel sizes and total counts are varied between 2.4 mm and 9.6 mm and 0.125 and 32 million counts. Images are reconstructed with OS EM-3D (Flash3D) with attenuation and scatter correction. Emission recovery is quantitatively measured for different reconstruction parameter settings. In addition, physical measurements of standard quality control phantoms are performed using an actual SPECT/CT system (Symbia® T6). Cross calibration of the imaging system with a well counter and results from simulations are used to quantitatively estimate the true activity concentration in the physical phantoms. Results show variations of emission recovery between 13.8% and 104.5% depending on sphere volume and number of OSEM-3D updates. After correction for the emission recovery errors and cross calibration of the imaging system the errors in absolute quantitation using the physical sphere phantom are between +0.01±0.61% for the largest (16 ml) and -5.87±1.00% for the smallest (0.5 ml) sphere. As a conclusion, the emission recovery varies over a wide range and is highly dependent on imaging parameters when using OSEM-3D reconstruction. Accurate quantitation in phantoms is possible given that errors at the specific imaging operation point can be estimated. In a clinical setup this is a nontrivial task, and perhaps too cumbersome for routine clinical use. © 2008 IEEE.

Towards Real-time Guidewire Detection and Tracking in the Field of Neuroradiology - Spiegel, Martin; Pfister, Markus; Hahn, Dieter; Daum, Volker; Hornegger, Joachim; Struffert, Tobias; Dörfler, Arnd (Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2009)
Two-dimensional roadmapping is considered state-of-the-art in guidewire navigation during endovascular interventions. This paper presents a methodology for extracting the guidewire from a sequence of 2-D roadmap images in almost real time. The detected guidewire can be used to improve its visibility on noisy fluoroscopic images or to do a back projection of the guidewire into a registered 3-D vessel tree. A lineness filter based on the Hessian matrix is used to detect only those line structures in the image that lie within the vessel tree. Loose wire fragments are properly linked by a novel connection method fulfilling clinical processing requirements. We show that Dijkstra's algorithm can be applied to efficiently compute the optimal connection path. The entire guidewire is finally approximated by a B-spline curve in a least-squares manner. The proposed method is both integrated into a commercial clinical prototype and evaluated on five different patient data sets containing up to 249 frames per image series. © 2009 SPIE.

TOWARDS ROBUST AUTOMATIC EVALUATION OF PATHOLOGIC TELEPHONE SPEECH - Riedhammer, Korbinian Thomas; Stemmer, Georg; Haderlein, Tino; Schuster, Maria; Rosanowski, Frank; Nöth, Elmar; Maier, Andreas (Proceedings of ASRU 2007, 2007)
For many aspects of speech therapy an objective evaluation of the intelligibility of a patient's speech is needed. We investigate the evaluation of the intelligibility of speech by means of automatic speech recognition. Previous studies have shown that measures like word accuracy are consistent with human experts' ratings. To ease the patient's burden, it is highly desirable to conduct the assessment via phone. However, the telephone channel influences the quality of the speech signal which negatively affects the results. To reduce inaccuracies, we propose a combination of two speech recognizers. Experiments on two sets of pathological speech show that the combination results in consistent improvements in the correlation between the automatic evaluation and the ratings by human experts. Furthermore, the approach leads to reductions of 10% and 25% of the maximum error of the intelligibility measure. © 2007 IEEE.
UnivIS-Import:2015-10-26:Pub.2007.tech.IMMD.IMMD5.toward_7
Keywords: Speech Intelligibility; Pathologic Speech; Speech Recognition

Towards Robust Distant-Talking Automatic Speech Recognition in Reverberant Environments - Riedhammer, Korbinian Thomas; Stemmer, Georg; Haderlein, Tino; Schuster, Maria; Rosanowski, Frank; Nöth, Elmar; Maier, Andreas (Proceedings of ASRU 2007, 2007)
UnivIS-Import:2015-04-02:Pub.2008.tech.IE.LEN.toward

Towards (smart) Full-body X-ray Images - Luckner, Christoph; Mertelmeier, Thomas; Maier, Andreas; Ritschl, Ludwig (Bildverarbeitung für die Medizin 2018. Informatik aktuell, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.toward_9

Towards the Automatic Classification of Reading Disorders in Continuous Text Passages - Maier, Andreas; Bocklet, Tobias; Hönig, Florian Thomas; Horndasch, Stefanie; Nöth, Elmar (Text, Speech and Dialogue, 2009)

Towards the Automatic Evaluation of Dysarthric Speech - Maier, Andreas; Nöth, Elmar; Rosanowski, Frank; Sous-Kulke, Christa; Schupp, Wilfried (Technically Assisted Rehabilitation - TAR 2009, 2009)

Towards the Estimation of Non-Uniform Illumination in Real-World Scenes - Riess, Christian; Pfaller, Sven; Bernecker, David; Angelopoulou, Elli (19. Workshop Farbbildverarbeitung, 2013)

Towards Understanding Preservation of Periodic Object Motion in Computed Tomography - Schirrmacher, Franziska; Taubmann, Oliver; Unberath, Mathias; Maier, Andreas (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.toward_1

Traditional machine learning for limited angle tomography - Huang, Yixing; Lu, Yanye; Taubmann, Oliver; Lauritsch, Günter; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2018)
Purpose The application of traditional machine learning techniques, in the form of regression models based on conventional, “hand-crafted” features, to artifact reduction in limited angle tomography is investigated. Methods Mean-variation-median (MVM), Laplacian, Hessian, and shift-variant data loss (SVDL) features are extracted from the images reconstructed from limited angle data. The regression models linear regression (LR), multilayer perceptron (MLP), and reduced-error pruning tree (REPTree) are applied to predict artifact images. Results REPTree learns artifacts best and reaches the smallest root-mean-square error (RMSE) of 29 HU for the Shepp–Logan phantom in a parallel-beam study. Further experiments demonstrate that the MVM and Hessian features complement each other, whereas the Laplacian feature is redundant in the presence of MVM. In fan-beam, the SVDL features are also beneficial. A preliminary experiment on clinical data in a fan-beam study demonstrates that REPTree can reduce some artifacts for clinical data. However, it is not sufficient as a lot of incorrect pixel intensities still remain in the estimated reconstruction images. Conclusion REPTree has the best performance on learning artifacts in limited angle tomography compared with LR and MLP. The features of MVM, Hessian, and SVDL are beneficial for artifact prediction in limited angle tomography. Preliminary experiments on clinical data suggest that the investigation on more features is necessary for clinical applications of REPTre
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.tradit_4
Keywords: Machine learning; limited angle tomography; decision tree

Traditional Machine Learning Techniques for Streak Artifact Reduction in Limited Angle Tomography - Huang, Yixing; Lu, Yanye; Taubmann, Oliver; Lauritsch, Günter; Maier, Andreas (Bildverarbeitung für die Medizin 2018 Algorithmen - Systeme - Anwendungen, 2018)
In this work, the application of traditional machine learning techniques, in the form of regression models based on conventional, “hand-crafted” features, to streak reduction in limited angle tomography is investigated. Specifically, linear regression (LR), multi-layer perceptron (MLP), and reduced-error pruning tree (REPTree) are investigated. When choosing the mean-variation-median (MVM), Laplacian, and Hessian features, REPTree learns streak artifacts best and reaches the smallest root-mean-square error (RMSE) of 29HU for the Shepp-Logan phantom. Further experiments demonstrate that the MVM and Hessian features complement each other, whereas the Laplacian feature is redundant in the presence of MVM. Preliminary experiments on clinical data suggests that further investigation of clinical applications using REPTree may be worthwhile.<br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.tradit
Keywords: machine learning; limited angle tomography; artifact reduction

Training Deep Learning Models for 2D Spine X-rays Using Synthetic Images and Annotations Created from 3D CT Volumes - Sukesh, Richin; Fieselmann, Andreas; Jaganathan, Srikrishna; Shetty, Karthik; Kaergel, Rainer; Kordon, Florian Johannes; Kappler, Steffen; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
When training deep learning models in the medical domain, one is always burdened with the task of obtaining reliable medical data annotated by experts. However, the availability of annotated data is often limited. To overcome such limitations, this paper addresses the idea of using synthetic spine X-ray data to train a deep learning model to aid in the detection of vertebrae. For this purpose, a pipeline for automatic generation of synthetic datasets comprising synthetic Xray images and their corresponding annotations is developed and evaluated. The results of these experiments show improvements in detection rates of the model when synthetic X-ray data is added to the training dataset.<br
Keywords: X-ray Imaging; Spine; Object Detection; Synthetic Data

Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract - Aubreville, Marc; Goncalves, Miguel; Knipfer, Christian; Oetter, Nicolai; Würfl, Tobias; Neumann, Helmut; Stelzle, Florian; Bohr, Christopher; Maier, Andreas (Communications in Computer and Information Science, 2019)
Squamous Cell Carcinoma (SCC) is the most common cancer type of the epithelium and is often detected at a late stage. Besides invasive diagnosis of SCC by means of biopsy and histo-pathologic assessment, Confocal Laser Endomicroscopy (CLE) has emerged as noninvasive method that was successfully used to diagnose SCC in vivo. For interpretation of CLE images, however, extensive training is required, which limits its applicability and use in clinical practice of the method. To aid diagnosis of SCC in a broader scope, automatic detection methods have been proposed. This work compares two methods with regard to their applicability in a transfer learning sense, i.e. training on one tissue type (from one clinical team) and applying the learnt classification system to another entity (different anatomy, different clinical team). Besides a previously proposed, patch-based method based on convolutional neural networks, a novel classification method on image level (based on a pre-trained Inception V.3 network with dedicated preprocessing and interpretation of class activation maps) is proposed and evaluated. The newly presented approach improves recognition performance, yielding accuracies of 91.63% on the first data set (oral cavity) and 92.63% on a joint data set. The generalization from oral cavity to the second data set (vocal folds) lead to similar area-under-the-ROC curve values than a direct training on the vocal folds data set, indicating good generalization.
CRIS-Team Scopus Importer:2019-09-17
Keywords: Confocal Laser Endomicroscopy; Head and neck squamous cell carcinoma; Transfer learning

Transfer learning helps to improve the accuracy to classify patients with different speech disorders in different languages - Vásquez-Correa, Juan Camilo; Rios-Urrego, Cristian David; Arias Vergara, Tomás; Schuster, Maria; Rusz, Jan; Nöth, Elmar; Orozco-Arroyave, Juan Rafael (Pattern Recognition Letters, 2021)
Patients suffering from neurodegenerative disorders such as Parkinson's or Huntington's disease exhibit speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows to develop computer aided tools to support the diagnosis and to evaluate the disease severity, which helps clinicians to make timely decisions about the treatment of the patients. This paper extends our previous studies about methods to classify patients with neurodegenerative diseases from speech. The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy to classify different speech impairments in patients that are native of different languages. The transfer learning schemes aim to improve the accuracy of the models when the weights of a neural network are initialized with utterances from a different corpus than the one used for the test set. The proposed methodology is evaluated with speech data from Parkinson's disease patients, who are Spanish, German, and Czech native speakers, Huntington's disease patients, who are Czech native speakers, and English native speakers affected by laryngeal impairments. We performed experiments in two scenarios: (1) transfer learning among languages, where a base model is transferred to classify patients with the same disease, but who speak a different language, and (2) transfer learning among diseases, where the base model is transferred to a corpus from patients with a different disease. The results suggest that the transfer learning schemes improve the accuracy in the target corpus only when the base model is accurate enough to transfer the knowledge to the target corpus. This behavior is observed in different scenarios of both transfer learning among languages and diseases.
CRIS-Team Scopus Importer:2021-05-21
Keywords: Convolutional neural networks; Deep learning; Huntington's disease; Parkinson's disease; Pathological speech; Transfer learning

Transfer learning to detect parkinson’s disease from speech in different languages using convolutional neural networks with layer freezing - Rios-Urrego, Cristian David; Vasquez Correa, Juan; Orozco Arroyave, Juan Rafael; Nöth, Elmar (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Parkinson’s Disease is a neurodegenerative disorder characterized by motor symptoms such as resting tremor, bradykinesia, rigidity and freezing of gait. The most common symptom in speech is called hypokinetic dysarthria, where speech is characterized by monotone intensity, low pitch variability and poor prosody that tends to fade at the end of the utterance. This study proposes the classification of patients with Parkinson’s Disease and healthy controls in three different languages (Spanish, German, and Czech) using a transfer learning strategy. The process is further improved by freezing consecutive different layers of the architecture. We hypothesize that some convolutional layers characterize the disease and others the language. Therefore, when a fine-tuning in the transfer learning is performed, it is possible to find the topology that best adapts to the target language and allows an accurate detection of Parkinson’s Disease. The proposed methodology uses Convolutional Neural Networks trained with Mel-scale spectrograms. Results indicate that the fine-tuning of the neural network does not provide good performance in all languages while fine-tuning of individual layers improves the accuracy by up to 7%. In addition, the results show that Transfer Learning among languages improves the performance in up to 18% when compared to a base model used to initialize the weights of the network.
CRIS-Team Scopus Importer:2020-11-06
Keywords: Convolutional neural networks; Parkinson’s disease; Speech processing; Transfer Learning

Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer’s Disease Using Forestnets - Pérez Toro, Paula Andrea; Rodriguez Salas, Dalia; Arias Vergara, Tomás; Bayerl, Sebastian P.; Klumpp, Philipp; Riedhammer, Korbinian Thomas; Schuster, Maria; Nöth, Elmar; Maier, Andreas; Orozco Arroyave, Juan Rafael (ICASSP 2023, 2023)
Progressive loss of memory is the most known symptom of Alzheimer’s Disease (AD); however, it also affects other cognitive skills and leads to depression symptoms. This paper presents a transfer learning strategy for automatically detecting AD and depression in AD patients using acoustic information and ForestNet, an artificial neural network that allows computing the contribution of a set of features to a model’s decision. The methodology consists of training ForestNet with a dataset commonly used for emotion recognition; then, we fine-tune the pre-trained model to detect AD and depression in AD. We trained the models with several acoustic features commonly used for emotion and AD applications. Unweighted average recalls of up to 0.87 were achieved to classify the disease and up to 0.82 to detect depression in AD. Our results indicate that the information obtained from the Arousal Valence plane may be suitable for discriminating and analyzing depression in AD.<br
Keywords: Emotion recognition; Analytical models; Computational modeling; Transfer learning; Forestry; Artificial neural networks; Depression

Transient Enlargement in Meningiomas Treated with Stereotactic Radiotherapy - Maksoud, Ziad; Schmidt, Manuel; Huang, Yixing; Rutzner, Sandra; Mansoorian, Sina; Weissmann, Thomas; Bert, Christoph; Distel, Luitpold; Semrau, Sabine; Lettmaier, Sebastian; Eyüpoglu, Ilker Yasin; Fietkau, Rainer; et al. (Cancers, 2022)
To investigate the occurrence of pseudoprogression/transient enlargement in meningiomas after stereotactic radiotherapy (RT) and to evaluate recently proposed volumetric RANO meningioma criteria for response assessment in the context of RT. Sixty-nine meningiomas (benign: 90%, atypical: 10%) received stereotactic RT from January 2005–May 2018. A total of 468 MRI studies were segmented longitudinally during a median follow-up of 42.3 months. Best response and local control were evaluated according to recently proposed volumetric RANO criteria. Transient enlargement was defined as volumetric increase ≥20% followed by a subsequent regression ≥20%. The mean best volumetric response was −23% change from baseline (range, −86% to +19%). According to RANO, the best volumetric response was SD in 81% (56/69), MR in 13% (9/69) and PR in 6% (4/69). Transient enlargement occurred in only 6% (4/69) post RT but would have represented 60% (3/5) of cases with progressive disease if not accounted for. Transient enlargement was characterized by a mean maximum volumetric increase of +181% (range, +24% to +389 %) with all cases occurring in the first year post-RT (range, 4.1–10.3 months). Transient enlargement was significantly more frequent with SRS or hypofractionation than with conventional fractionation (25% vs. 2%, p = 0.015). Five-year volumetric control was 97.8% if transient enlargement was recognized but 92.9% if not accounted for. Transient enlargement/pseudoprogression in the first year following SRS and hypofractionated RT represents an important differential diagnosis, especially because of the high volumetric control achieved with stereotactic RT. Meningioma enlargement during subsequent post-RT follow-up and after conventional fractionation should raise suspicion for tumor progression. <
Keywords: meningioma; volumetric analysis; segmentation; transient enlargement; pseudoprogression; stereotactic radiotherapy; radiosurgery; response assessment

Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model - Maul, Noah; Zinn, Katharina; Wagner, Fabian; Thies, Mareike; Rohleder, Maximilian; Pfaff, Laura; Kowarschik, Markus; Birkhold, Annette; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Patient-specific hemodynamics assessment could support diagnosis and treatment of neurovascular diseases. Currently, conventional medical imaging modalities are not able to accurately acquire high-resolution hemodynamic information that would be required to assess complex neurovascular pathologies. Instead, computational fluid dynamics (CFD) simulations can be applied to tomographic reconstructions to obtain clinically relevant information. However, three-dimensional (3D) CFD simulations require enormous computational resources and simulation-related expert knowledge that are usually not available in clinical environments. Recently, deep-learning-based methods have been proposed as CFD surrogates to improve computational efficiency. Nevertheless, the prediction of high-resolution transient CFD simulations for complex vascular geometries poses a challenge to conventional deep learning models. In this work, we present an architecture that is tailored to predict high-resolution (spatial and temporal) velocity fields for complex synthetic vascular geometries. For this, an octree-based spatial discretization is combined with an implicit neural function representation to efficiently handle the prediction of the 3D velocity field for each time step. The presented method is evaluated for the task of cerebral hemodynamics prediction before and during the injection of contrast agent in the internal carotid artery (ICA). Compared to CFD simulations, the velocity field can be estimated with a mean absolute error of 0.024 m s - 1, whereas the run time reduces from several hours on a high-performance cluster to a few seconds on a consumer graphical processing unit.
Created from Fastlane, Scopus look-up
Keywords: Hemodynamics; Octree; Operator learning

Translating satisfaction determination from health care to the automotive industry - Meinzer, Stefan; Prenninger, Johann; Vesel, Patrick; Kornhuber, Johannes; Volmer, Judith; Hornegger, Joachim; Eskofier, Björn (Service Business, 2015)
This study is based on the case of BMW, and aims to improve the determination of perceived consumer satisfaction in the automotive industry by transferring existing knowledge from the health care sector. A literature analysis of the health care sector and the automotive industry was conducted to identify the common concepts of determining satisfaction. These were the service encounter, situational factors, and sociodemographics. The practical application was tested by analyzing a contemporary survey from BMW. Based on the findings, managers responsible for customer satisfaction in after-sales services in the automotive industry could improve measurement of customer satisfaction.
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.transl
Keywords: Automotive industry; BMW; Customer satisfaction; Health care; Patient satisfaction

Treadmill exercise intervention improves gait and postural control in alpha-synuclein mouse models without inducing cerebral autophagy - Minakaki, Georgia; Canneva, Fabio; Chevessier, Frederic; Bode, Frederik; Menges, Stefanie; Timotius, Ivanna; Kalinichenko, Liubov; Meixner, Holger; Müller, Christian P.; Eskofier, Björn; Casadei, Nicolas; Riess, Olaf; et al. (Behavioural Brain Research, 2019)
Gait and postural control dysfunction are prototypical symptoms compromising quality of life for patients with Parkinson’s disease (PD). Hallmarks of cellular pathology are dopaminergic degeneration and accumulation of the cytosolic protein alpha-synuclein, linked to impaired autophagy-lysosome pathway (ALP) clearance. Physical exercise improves gait in PD patients and motor function in rodent lesion models. Moreover, exercise is considered neuroprotective and ALP induction has been reported, e.g. in human skeletal muscle, rodent peripheral and cerebral tissues. A combined analysis of how distinct exercise paradigms affect motor and central biochemical aspects of PD could maximize benefits for patients. Here we examine the effect of 4 weeks treadmill exercise intervention in 7-8 month non-lesioned mice on a) distinct gait categories, b) ALP activity, c) dopaminergic and alpha-synuclein homeostasis. The study includes wild type, alpha-synuclein knockout, and mice exclusively expressing human alpha-synuclein. Parameters of gait regularity and stability, activity, and dynamic postural control during unforced walk, were assessed by an automated system (CatWalk XT). At baseline, alpha-synuclein mouse models exhibited irregular and less active gait, with impaired dynamic postural control, compared to wild type mice. Treadmill exercise particularly improved speed and stride length, while increasing dual diagonal versus three-paw body support in both the alpha-synuclein knockout and transgenic mice. Biochemical analyses showed higher striatal tyrosine hydroxylase immuno-reactivity and reduced higher-order alpha-synuclein species in the cerebral cortex. However, no significant cerebral ALP induction was measured. In summary, treadmill exercise improved gait activity and postural stability, and promoted dopaminergic and alpha-synuclein homeostasis, without robustly inducing cerebral ALP.<br
Keywords: alpha-synuclein; autophagy; instrumented gait analysis; mouse gait pattern; Parkinson´s disease; treadmill exercise

Truncation Correction for Non-horizontal Filter Lines - Hoppe, Stefan; Hornegger, Joachim; Lauritsch, Günter; Dennerlein, Frank; Noo, Frédéric (Proceedings Fully 3D Meeting and HPIR Workshop, 2007)

Truncation correction for oblique filtering lines - Hoppe, Stefan; Hornegger, Joachim; Lauritsch, Günter; Dennerlein, Frank; Noo, Frederic (Medical Physics, 2008)
State-of-the-art filtered backprojection (FBP) algorithms often define the filtering operation to be performed along oblique filtering lines in the detector. A limited scan field of view leads to the truncation of those filtering lines, which causes artifacts in the final reconstructed volume. In contrast to the case where filtering is performed solely along the detector rows, no methods are available for the case of oblique filtering lines. In this work, the authors present two novel truncation correction methods which effectively handle data truncation in this case. Method 1 (basic approach) handles data truncation in two successive preprocessing steps by applying a hybrid data extrapolation method, which is a combination of a water cylinder extrapolation and a Gaussian extrapolation. It is independent of any specific reconstruction algorithm. Method 2 (kink approach) uses similar concepts for data extrapolation as the basic approach but needs to be integrated into the reconstruction algorithm. Experiments are presented from simulated data of the FORBILD head phantom, acquired along a partial-circle-plus-arc trajectory. The theoretically exact M-line algorithm is used for reconstruction. Although the discussion is focused on theoretically exact algorithms, the proposed truncation correction methods can be applied to any FBP algorithm that exposes oblique filtering lines. © 2008 American Association of Physicists in Medicine.

Truncation correction for VOI C-arm CT using scattered radiation - Bier, Bastian; Maier, Andreas; Hofmann, Hannes; Schwemmer, Chris; Xia, Yan; Struffert, Tobias; Hornegger, Joachim (Medical Imaging 2013: Physics of Medical Imaging, 2013)
In C-arm computed tomography, patient dose reduction by volume-of-interest (VOI) imaging is of increasing interest for many clinical applications. A remaining limitation of VOI imaging is the truncation artifact when reconstructing a 3D volume. It can either be cupping towards the boundaries of the field-of-view (FOV) or an incorrect offset in the Hounsfield values of the reconstructed voxels. In this paper, we present a new method for correction of truncation artifacts in a collimated scan. When axial or lateral collimation are applied, scattered radiation still reaches the detector and is recorded outside of the FOV. If the full area of the detector is read out we can use this scattered signal to estimate the truncated part of the object. We apply three processing steps: detection of the collimator edge, adjustment of the area outside the FOV, and interpolation of the collimator edge. Compared to heuristic truncation correction methods we were able to reconstruct high contrast structures like bones outside of the FOV. Inside the FOV we achieved similar reconstruction results as with water cylinder truncation correction. These preliminary results indicate that scattered radiation outside the FOV can be used to improve image quality and further research in this direction seems beneficial. © 2013 SPIE.

Truncation Correction for X-ray Phase-Contrast Region-of-Interest Tomography - Felsner, Lina; Käppler, Sebastian; Maier, Andreas; Riess, Christian (IEEE Transactions on Computational Imaging, 2020)

Truncation Correction using a 3D Filter for Cone-beam CT - Xia, Yan; Maier, Andreas; Dennerlein, Frank; Hornegger, Joachim (Fully3D 2013, 2013)

Truncation Robust C-Arm CT Reconstruction for Dynamic Collimation Acquisition Schemes - Kästner, Thomas; Hornegger, Joachim; Maier, Andreas; Xia, Yan; Bauer, Sebastian (Proceedings in Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.trunca_3

Tumor Therapy Monitoring Based on Monomodal Image Fusion - Hornegger, Joachim; Hahn, Dieter; Bautz, Werner; Kuwert, Torsten; Römer, Wolfgang (Proceedings of the Radiological Society of North America (RSNA 2004), 2005)

Tutorial: Deep Learning - Advancing the State-of-the-Art in Medical Image Analysis - Christlein, Vincent; Ghesu, Florin-Cristian; Würfl, Tobias; Maier, Andreas; Isensee, Fabian; Neher, Peter; Maier-Hein, Klaus (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.tutori

TV or not TV? That is the Question - Riess, Christian; Berger, Martin; Wu, Haibo; Manhart, Michael; Fahrig, Rebecca; Maier, Andreas (Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013)

Two-stage Semi-automatic Organ Segmentation Framework using Radial Basis Functions and Level Sets - Wimmer, Andreas; Soza, Grzegorz; Hornegger, Joachim (3D Segmentation in the Clinic - A Grand Challenge MICCAI 2007 Workshop Proceedings, 2007)

UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model - Amrehn, Mario; Gaube, Sven; Unberath, Mathias; Schebesch, Frank; Horz, Tim; Strumia, Maddalena; Steidl, Stefan; Kowarschik, Markus; Maier, Andreas (EG VCBM 2017, 2017)
For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data sets need to be processed for a highly accurate result, semi-automatic segmentation techniques exhibit a clear benefit for the user. One area of application is medical image processing during an intervention for a single patient. We propose a learning-based cooperative segmentation approach which includes the computing entity as well as the user into the task. Our system builds upon a state-of-the-art fully convolutional artificial neural network (FCN) as well as a simple rule based active user model for training. During the segmentation process, a user of the trained system can iteratively add additional hints in form of pictorial scribbles as seed points into the FCN system to achieve an interactive and precise segmentation result. The segmentation quality of interactive FCNs is evaluated. Iterative FCN approaches can yield superior results compared to networks without the user input channel component, due to a consistent improvement in segmentation quality after each interaction.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.uineti
Keywords: CNN; FCN; Machine Learning; Interactive; User; Semi-automatic; Feedback; Seeds; Segmentation; Medical Imaging

Ultrahigh resolution OCT, volume merging, and advanced signal reconstruction improve visualization of the RPE-Bruch's complex - Ploner, Stefan; Chen, Siyu; Moult, Eric; Husvogt, Lennart; Schottenhamml, Julia; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Investigative Ophthalmology & Visual Science, 2022)
CRIS-Team WoS Importer:2022-09-30

Ultrahigh speed endoscopic optical coherence tomography using micromotor imaging catheter and VCSEL technology - Tsai, Tsung-Han; Potsaid, Benjamin; Tao, Yuankai Kenny; Jayaraman, Vijaysekhar; Jiang, James; Heim, Peter J. S.; Kraus, Martin; Zhou, Chao; Hornegger, Joachim; Mashimo, Hiroshi; Cable, Alex E.; Fujimoto, James G. (Biomedical Optics Express, 2013)
We developed a micromotor based miniature catheter with an outer diameter of 3.2 mm for ultrahigh speed endoscopic swept source optical coherence tomography (OCT) using a vertical cavity surfaceemitting laser (VCSEL) at a 1 MHz axial scan rate. The micromotor can rotate a micro-prism at several hundred frames per second with less than 5 V drive voltage to provide fast and stable scanning, which is not sensitive to the bending of the catheter. The side-viewing probe can be pulled back to acquire a three-dimensional (3D) data set covering a large area on the specimen. The VCSEL provides a high axial scan rate to support dense sampling under high frame rate operation. Using a high speed data acquisition system, in vivo 3D-OCT imaging in the rabbit GI tract and ex vivo imaging of a human colon specimen with 8 μm axial resolution, 8 μm lateral resolution and 1.2 mm depth range in tissue at a frame rate of 400 fps was demonstrated. © 2013 Optical Society of America.
UnivIS-Import:2015-03-09:Pub.2013.tech.IMMD.IMMD5.ultrah_8

Ultrahigh speed endoscopic optical coherence tomography using micro-motor imaging catheter and VCSEL technology - Tsai, Tsung-Han; Tao, Yuankai Kenny; Potsaid, Benjamin; Jayaraman, Vijaysekhar; Kraus, Martin; Heim, Peter J. S.; Hornegger, Joachim; Mashimo, Hiroshi; Cable, Alex E.; Fujimoto, James G. (Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XVII, 2013)
We developed a micro-motor based miniature catheter with an outer diameter of 3mm for ultrahigh speed endoscopic vbvvvoptical coherence tomography (OCT) using vertical cavity surface-emitting laser (VCSEL) at a 1MHz axial scan rate. The micro-motor can rotate a micro-prism at 1,200-72,000rpm (corresponding to 20- 1,200fps) with less than 5V driving voltage to provide fast and stable scanning, which is not sensitive to the bending of the catheter. 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. VCSEL provides high a-line rate to support dense sampling under high frame rate operation. With the use of a C++ based high speed data acquisition (DAQ) system, in vivo three-dimensional OCT imaging in rabbit GI tract with 1.6mm depth range, 11μm axial resolution, 8μm lateral resolution, and frame rate of 400fps is demonstrated. © 2013 Copyright SPIE.

Ultra low‐parameter denoising: Trainable bilateral filter layers in computed tomography - Wagner, Fabian; Thies, Mareike; Gu, Mingxuan; Huang, Yixing; Pechmann, Sabrina; Patwari, Mayank; Ploner, Stefan; Aust, Oliver; Uderhardt, Stefan; Schett, Georg; Christiansen, Silke H.; Maier, Andreas (Medical Physics, 2022)
Background Computed tomography (CT) is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution can be severely degraded through low-dose acquisitions, highlighting the importance of effective denoising algorithms. Purpose Most data-driven denoising techniques are based on deep neural networks and, therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity. Methods This work presents an open-source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image-to-image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. In contrast to other models, our bilateral filter layer consists of only four trainable parameters and constrains the applied operation to follow the traditional bilateral filter algorithm by design. Results Although only using three spatial parameters and one intensity range parameter per filter layer, the proposed denoising pipelines can compete with deep state-of-the-art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x-ray microscope bone data and the 2016 Low Dose CT Grand Challenge data set. We report structural similarity index measures (SSIM) of 0.7094 and 0.9674 and peak signal-to-noise ratio (PSNR) values of 33.17 and 43.07 on the respective data sets. Conclusions Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architecture
Keywords: known operator learning; low-dose CT; bilateral filter; denoising

Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease - Klucken, Jochen; Barth, Jens; Kugler, Patrick; Schlachetzki, Johannes; Henze, Thore; Marxreiter, Franz; Kohl, Zacharias; Steidl, Ralph; Hornegger, Joachim; Eskofier, Björn; Winkler, Jürgen (PLoS ONE, 2013)
Motor impairments are the prerequisite for the diagnosis in Parkinson's disease (PD). The cardinal symptoms (bradykinesia, rigor, tremor, and postural instability) are used for disease staging and assessment of progression. They serve as primary outcome measures for clinical studies aiming at symptomatic and disease modifying interventions. One major caveat of clinical scores such as the Unified Parkinson Disease Rating Scale (UPDRS) or Hoehn&Yahr (H&Y) staging is its rater and time-of-assessment dependency. Thus, we aimed to objectively and automatically classify specific stages and motor signs in PD using a mobile, biosensor based Embedded Gait Analysis using Intelligent Technology (eGaIT). eGaIT consist of accelerometers and gyroscopes attached to shoes that record motion signals during standardized gait and leg function. From sensor signals 694 features were calculated and pattern recognition algorithms were applied to classify PD, H&Y stages, and motor signs correlating to the UPDRS-III motor score in a training cohort of 50 PD patients and 42 age matched controls. Classification results were confirmed in a second independent validation cohort (42 patients, 39 controls). eGaIT was able to successfully distinguish PD patients from controls with an overall classification rate of 81%. Classification accuracy increased with higher levels of motor impairment (91% for more severely affected patients) or more advanced stages of PD (91% for H&Y III patients compared to controls), supporting the PD-specific type of analysis by eGaIT. In addition, eGaIT was able to classify different H&Y stages, or different levels of motor impairment (UPDRS-III). In conclusion, eGaIT as an unbiased, mobile, and automated assessment tool is able to identify PD patients and characterize their motor impairment. It may serve as a complementary mean for the daily clinical workup and support therapeutic decisions throughout the course of the disease. © 2013 Klucken et al.
EVALuna2:25318

Unbiased rigid registration using transfer functions - Hahn, Dieter; Hornegger, Joachim; Bautz, Werner; Kuwert, Torsten; Römer, Wolfgang (Medical Imaging 2005 - Image Processing, 2005)
The evaluation of tumor growth as regression under therapy is an important clinical issue. Rigid registration of sequentially acquired SD-images has proven its value for this purpose. Existing approaches to rigid image registration use the whole volume for the estimation of the rigid transform. Non-rigid soft tissue deformation, however, will imply a bias to the registration result, because local deformations cannot be modeled by rigid transforms. Anatomical substructures, like bones or teeth, are not affected by these deformations, but follow a rigid transform. This important observation is incorporated in the proposed registration algorithm. The selection of anatomical substructure is done by manual interaction of medical experts adjusting the transfer function of the volume rendering software. The parameters of the transfer function are used to identify the voxels that are considered for registration. A rigid transform is estimated by a quaternion gradient descent algorithm based on the intensity values of the specified tissue classes. Commonly used voxel intensity measures are adjusted to the modified registration algorithm. The contribution describes the mathematical framework of the proposed registration method and its implementation in a commercial software package. The experimental evaluation includes the discussion of different similarity measures, the comparison of the proposed method to established rigid registration techniques and the evaluation of the efficiency of the new method. We conclude with the discussion of potential medical applications of the proposed registration algorithm.

U-Net for SPECT Image Denoising - Reymann, Maximilian; Würfl, Tobias; Stimpel, Bernhard; Ritt, Philipp; Cachovan, Michal; Vija, A. Hans; Maier, Andreas (2019 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC), 2019)
Single Photon Emitted Computed Tomography (SPECT) is characterized by low photon counts and high degrees of image noise. In this work we investigate deep image postprocessing methods for improving image quality in SPECT projections and propose a new architecture. Specifically, the residual U-Net proposed by Heinrich et al. for low-dose Computed Tomography (CT) and the Convolutional Denoising Autoencoder (CNN DAE) by Gondara et al. were tested for their performance for denoising SPECT projections. Reference images without noise and scatter were obtained from SPECT Monte Carlo simulations of 24 different XCAT phantoms of brains, lungs, livers and skeletons using the SIMIND software. These clean images were then used as training target for the neural networks. Additionally, we propose a U-Net architecture that is more suited for SPECT image denoising, consisting of 4 layers, each with 2 blocks of convolution, batch normalization and ReLU. Our results showed that the proposed U-Net outperforms the residual U-Net and the CNN DAE with a PSNR of 38.25 dB, compared to 27.95 dB and 28.25 dB, respectively. It was shown that the characteristics of the SPECT data posed a challenge to the neural networks, as there are only few distinct photon count values at the detector available and these are superimposed with a high degree of image noise, compared to low-dose CT. Our proposed network was able to decrease the noise within the phantom and additionally decreased the background noise in the image. The image quality was improved by 14.15 dB compared to the noisy input image, demonstrating the suitability of our network. We believe that with increased size and diversity of our dataset we can further boost the performance.<br
Keywords: SPECT; Denoising; Monte Carlo Simulation; XCAT Phantom; SIMIND; U-Net

Universal Plug'n'PEAKS - Towards easy Deployment of Multi-modal Tele-medicine - Janu, Thomas; Spiegl, Werner; Soutschek, Stefan; Maier, Andreas; Steidl, Stefan; Nöth, Elmar (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

Unobtrusive and Energy-Efficient Swimming Exercise Tracking Using On-Node Processing - Jensen, Ulf; Blank, Peter; Kugler, Patrick; Eskofier, Björn (IEEE Sensors Journal, 2016)
UnivIS-Import:2016-06-27:Pub.2016.tech.IMMD.IMMD5.unobtr
Keywords: inertial measurement unit; swimming exercise tracking; on-node processing; wearable computing; body sensor network

Unobtrusive Heart Rate Estimation during Physical Exercise using Photoplethysmographic and Acceleration Data - Mullan, Patrick; Kanzler, Christoph; Lorch, Benedikt; Schroeder, Lea; Winkler, Ludwig; Larissa, Laich; Riedel, Frederik; Richer, Robert; Luckner, Christoph; Leutheuser, Heike; Eskofier, Björn; Pasluosta, Cristian Federico (37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC ’15, 2015)

Unobtrusive Monitoring of Speech Impairments of Parkinson's Disease Patients Through Mobile Devices - Arias Vergara, Tomás; Vasquez Correa, Juan; Orozco Arroyave, Juan Rafael; Klumpp, Philipp; Nöth, Elmar (2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, 2018)
Keywords: Speech enhancement; Speech impairments; Classification; Parkinson's disease; Mobile devices

Unobtrusive Monitoring of Speech Impairments of Parkinson'S Disease Patients Through Mobile Devices - Arias Vergara, Tomás; Vasquez Correa, Juan; Rafael Orozco-Arroyave, Juan; Klumpp, Philipp; Nöth, Elmar (43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018)

Unsupervised Learning for Robust Respiratory Signal Estimation from X-Ray Fluoroscopy - Fischer, Peter; Pohl, Thomas; Faranesh, Anthony; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.unsupe
Keywords: Clustering; Dimensionality reduction; Fluoroscopy; Patch-based; Respiratory signal; X-ray

Unsupervised Segmentation and Classification of Orca Vocalizations using the Fundamental Frequency Variation Spectrum (abstract) - Hönig, Florian Thomas; Sonmez, Reyhan; Nöth, Elmar; Manser, Martha (6th International Workshop on Detection, Classification, Localization, & Density Estimation of Marine Mammals using Passive Acoustics, 2013)

Unsupervised Unstained Cell Detection by SIFT Keypoint Clustering and Self-labeling Algorithm - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim (Lecture Notes in Computer Science, Volume 8675, MICCAI 2014 Proceedings, Part III, 2014)
We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.unsupe_6

Unsupervised unstained cell detection by SIFT keypoint clustering and self-labeling algorithm. - Muallal, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim (International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2014)
We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.
CRIS-Team Scopus Importer:2022-08-05

Unterstuetzung der transseptalen Punktion durch vorherige Überlagerung eines 3D-Volumens von linkem Atrium und Aorta - Bourier, Felix; Schneider, Hans-Jürgen; Ganslmeier, Patrycja; Heißenhuber, Frank; Fischer, Robert; Brost, Alexander; Koch, Martin; Strobel, Norbert; Hornegger, Joachim; Kurzidim, Klaus (77. Jahrestagung, 2011)

Untersuchung von Sprachaufnahmen heiserer Stimmen mittels phonologischer und phonemischer Merkmale - Haderlein, Tino; Middag, Catherine; Martens, Jean-Pierre; Döllinger, Michael; Nöth, Elmar (Aktuelle phoniatrisch-pädaudiologische Aspekte 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.unters_4

User Loss A Forced-Choice-Inspired Approach to Train Neural Networks Directly by User Interaction - Zarei, Shahab; Stimpel, Bernhard; Syben-Leisner, Christopher; Maier, Andreas (Informatik aktuell, 2019)
In this paper, we investigate whether is it possible to train a neural network directly from user inputs. We consider this approach to be highly relevant for applications in which the point of optimality is not well-defined and user-dependent. Our application is medical image denoising which is essential in fluoroscopy imaging. In this field every user, i.e. physician, has a different flavor and image quality needs to be tailored towards each individual. To address this important problem, we propose to construct a loss function derived from a forced-choice experiment. In order to make the learning problem feasible, we operate in the domain of precision learning, i.e., we inspire the network architecture by traditional signal processing methods in order to reduce the number of trainable parameters. The algorithm that was used for this is a Laplacian pyramid with only six trainable parameters. In the experimental results, we demonstrate that two image experts who prefer different filter characteristics between sharpness and de-noising can be created using our approach. Also models trained for a specific user perform best on this users test data. This approach opens the way towards implementation of direct user feedback in deep learning and is applicable for a wide range of application.
CRIS-Team Scopus Importer:2019-05-14

User State Modeling Based on the Arousal-Valence Plane: Applications in Customer Satisfaction and Health-Care - Pérez Toro, Paula Andrea; Vasquez Correa, Juan; Bocklet, Tobias; Nöth, Elmar; Orozco-Arroyave, Juan Rafael (IEEE Transactions on Affective Computing, 2021)
Keywords: Depression; Mood; Parkinson's Disease; Linguistics; Acoustics; Arousal-Valence Plane; Alzheimer's Disease; Customer Satisfaction

User States, User Strategies, and System Performance: How to Match the One with the Other - Batliner, Anton; Hacker, Christian; Steidl, Stefan; Nöth, Elmar; Haas, Jürgen (Proc. of an ISCA Tutorial and Research Workshop on Error Handling in Spoken Dialogue Systems, 2003)
Apart from the ‘normal’ linguistic information entailed in user utterances – segmental (phone/word) information and syntactic/semantic information – there is additional information (supra-segmental and para-linguistic) that can be useful for deciding whether an automatic dialogue system performs well or not. In this paper, we want to deal with such additional information and correlate it with system performance. Moreover, we will examine whether prosodic peculiarities influence word recognition.

Using a C-arm CT for Interventional Perfusion Imaging: A Phantom Study to Measure Linearity Between Iodine Concentration and Hounsfield Values - Fieselmann, Andreas; Ganguly, Arundhuti; Deuerling-Zheng, Yu; Boese, Jan; Fahrig, Rebecca; Hornegger, Joachim (Medizinische Physik 2010, 2010)

Using Artificially Reverberated Training Data in Distant-Talking ASR - Haderlein, Tino; Nöth, Elmar; Herbordt, Wolfgang; Kellermann, Walter; Niemann, Heinrich (Text, Speech and Dialogue; 8th International Conference TSD 2005, 2005)

Using a wireless motion controller for 3D medical image catheter interactions - Vitanovski, Dime; Hahn, Dieter; Daum, Volker; Hornegger, Joachim (Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2009)
State-of-the-art morphological imaging techniques usually provide high resolution 3D images with a huge number of slices. In clinical practice, however, 2D slice-based examinations are still the method of choice even for these large amounts of data. Providing intuitive interaction methods for specific 3D medical visualization applications is therefore a critical feature for clinical imaging applications. For the domain of catheter navigation and surgery planning, it is crucial to assist the physician with appropriate visualization techniques, such as 3D segmentation maps, fly-through cameras or virtual interaction approaches. There has been an ongoing development and improvement for controllers that help to interact with 3D environments in the domain of computer games. These controllers are based on both motion and infrared sensors and are typically used to detect 3D position and orientation. We have investigated how a state-of-the-art wireless motion sensor controller (Wiimote), developed by Nintendo, can be used for catheter navigation and planning purposes. By default the Wiimote controller only measure rough acceleration over a range of +/- 3g with 10% sensitivity and orientation. Therefore, a pose estimation algorithm was developed for computing accurate position and orientation in 3D space regarding 4 Infrared LEDs. Current results show that for the translation it is possible to obtain a mean error of (0.38cm, 0.41cm, 4.94cm) and for the rotation (0.16, 0.28) respectively. Within this paper we introduce a clinical prototype that allows steering of a virtual fly-through camera attached to the catheter tip by the Wii controller on basis of a segmented vessel tree. © 2009 SPIE.

Using Contextual Information to Process Out-of-Vocabulary Words in Spoken Dialog Systems - Horndasch, Axel (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2022)
Speech is a very efficient channel for communication, but it is a challenge to cope with words which are unknown to one of the dialogue partners. Human beings are very good at dealing with words that are not in their vocabulary, for example by exploiting the context the unknown word appears in. In this way, the word can be categorized and, based on this information and the pronunciation, the spelling can be worked out as well. A key source for task-relevant out-of-vocabulary (OOV) words are named entities like names of cities, persons, companies, products etc. in case they are rarely used or newly invented. In a sentence like “Please give me information about the Argentinian soccer player Jorge Burruchaga!” for example, Mr. Burruchaga’s name is the most important piece of information. Automatic speech recognition (ASR) systems usually map words which were not part of the training material to similar sounding words without considering the word class; often, the consequence of such recognition errors is that the human-machine dialogue cannot be finished successfully. In this work, it is described how an automatic speech recognition (ASR) system can be enhanced with a functionality that closely resembles the human method so that OOV words can be detected, categorized and recovered in a written form. The suggested approach, hierarchical hybrid word-class-based OOV detection in combination with sub-word units, is integrated into the widely used Kaldi speech recognition toolkit. Experiments on the speech corpora EVAR and SmartWeb show that more than 70% of unknown city names and about 50% of OOV celebrity names can be detected while at the same time improving the word error rate of the system.<br
Keywords: Kaldi; OOV; Out-of-Vocabulary Words; Speech Recognition; Spoken Dialog Systems; Word Classes

Using Edge Orientation Histograms in Face–based Gender Classification - Timotius, Ivanna; Setyawan, Iwan (International Conference on Information Technology Systems and Innovation, 2014)
In this paper we present our evaluation of the Edge Orientation Histograms (EOH) as feature descriptors in an automatic face-based gender classification application. The feature descriptors extracted from an input image are evaluated using estimated arithmetic means of accuracies to select the feature descriptors that play the most important role in classification success. Our experiments show that features corresponding to the jawline of the subject play the most important role, yielding an average classification accuracy of up to 86%.
Keywords: gender classification; edge orientation histograms; estimated arithmetic means of accuracies.

Using Estimated Arithmetic Means of Accuracies to Select Features for Face–based Gender Classification - Timotius, Ivanna; Setyawan, Iwan (The 5th International Conference on Information Technology and Electrical Engineering (ICITEE 2013), 2013)
Selecting the appropriate features is essential in building a good classifier. This paper aims to use the approach of estimating the arithmetic means of accuracies (ameans) in selecting the features used in a face-based gender classification. In a face-based gender classification, there are many pixels of the input image that may not aid the classification process, such as those belonging to the background. The experiments show that this approach outperforms the approach based on mean difference especially on the data having relatively high variance by up to 2.14%. Compared to the classifier which does not use any feature selection approach, implementing the feature selection approach based on ameans estimation in a gender classification problem increases the accuracy by up to 7.86%. The experiments also show that the face-based gender classifications rely on the presence of long hair on subjects in the images to make their decision.
Keywords: Feature Selection; Arithmetic Means of Accuracies; Gender Classification.

Using test plans for bayesian modeling - Deventer, Rainer; Denzler, Joachim; Niemann, Heinrich; Kreis, Oliver (Machine Learning and Data Mining in Pattern Recognition, 2003)
When modeling technical processes, the training data regularly come from test plans, to reduce the number of experiments and to save time and costs. On the other hand, this leads to unobserved combinations of the input variables. In this article it is shown, that these unobserved configurations might lead to un-trainable parameters. Afterwards a possible design criterion is introduced, which avoids this drawback. Our approach is tested to model a welding process. The results show, that hybrid Bayesian networks are able to deal with yet unobserved in- and output dat
Keywords: Bayesian network; Laserbeam welding; Manufacturing process; Modeling

Using the ISADORA System for Analyzing Fatigue Symptoms and Robustness of Features against Reverberation - Haderlein, Tino (2002)
UnivIS-Import:2016-06-30:Pub.2002.tech.IMMD.IMMD5.usingt

Using the Low-Pass Monogenic Signal Framework for Cell/Background Classification on Multiple Cell Lines in Bright-Field Microscope Images - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim (International Journal of Computer Assisted Radiology and Surgery, 2014)
Purpose: Several cell detection approaches which deal with bright-field microscope images utilize defocusing to increase image contrast. The latter is related to the physical light phase through the transport of intensity equation (TIE). Recently, it was shown that it is possible to approximate the solution of the TIE using a low-pass monogenic signal framework. The purpose of this paper is to show that using the local phase of the aforementioned monogenic signal instead of the defocused image improves the cell/background classification accuracy. Materials and methods: The paper statement was tested on an image database composed of three cell lines: adherent CHO, adherent L929, and Sf21 in suspension. Local phase and local energy images were generated using the low-pass monogenic signal framework with axial derivative images as input. Machine learning was then employed to investigate the discriminative power of the local phase. Three classifier models were utilized: random forest (RF), support vector machine (SVM) with a linear kernel, and SVM with a radial basis function (RBF) kernel. Results: The improvement, averaged over cell lines, of classifying 5 x 5 sized patches extracted from the local phase image instead of the defocused image was 7.3 % using the RF, 11.6 % using the linear SVM, and 10.2 % when a RBF kernel was employed instead of the linear one. Furthermore, the feature images can be sorted by increasing discriminative power as follows: at-focus signal, local energy, defocused signal, local phase. The only exception to this order was the superiority of local energy over defocused signal for suspended cells. Conclusions: Local phase computed using the low-pass monogenic signal framework considerably outperforms the defocused image for the purpose of pixel-patch cell/background classification in bright-field microscopy. © 2013 CARS.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.usingt
Keywords: Bright-field microscopy; Cell detection; Local phase; Machine learning; Monogenic signal; Transport of intensity equation

Using the Monogenic Signal for Cell-Background Classification in Bright-Field Microscope Images - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Hornegger, Joachim (Proceedings des Workshops Bildverarbeitung für die Medizin 2013, 2013)

Utilizing Salient Region Features for 3D Multi-Modality Medical Image Registration - Hahn, Dieter; Wolz, Gabriele; Sun, Yiyong; Sauer, Frank; Hornegger, Joachim; Kuwert, Torsten; Xu, Chenyang (Bildverarbeitung für die Medizin 2006, 2006)

Validation of an automatic speech analysis in children with isolated cleft palate - Schulz, A.; Bocklet, Tobias; Eysholdt, Ulrich; Bohr, Christopher; Döllinger, Michael; Ziethe, A. (HNO, 2014)
Following surgical repair of cleft lip and palate, hearing and speech and language development are important issues for the continued care of affected childhood and adolescent patients. Therefore, PEAKS (Program for Evaluation and Analysis of all Kinds of Speech Disorders) was developed in order to rate speech intelligibility automatically and reduce the time required for diagnostics. PEAKS is based on a speech recognition system and was extended to incorporate a speaker model. This investigation validated PEAKS for isolated cleft palate.From each of the 39 children with isolated cleft palate (3.1-14.5 years), 99 word productions were recorded digitally and analyzed-once "subjectively" by five experts and five nonexperts; once "objectively" using PEAKS.The automatic speech recognition system and the experts arrive at similar results with regard to speech intelligibility. The expert and nonexpert ratings differ significantly from each other. Within the group of nonexperts, a weak interrater reliability demonstrates the uncertainty associated with their ratings.PEAKS delivers reliable and representative results with regard to speech intelligibility among children and adolescents with isolated cleft palate. The automatic measurement of speech quality in children and adolescents with isolated cleft palate is possible.
EVALuna2:19924

Validierter SKT als Multimodale Telemedizinische Applikation - Soutschek, Stefan; Spiegl, Werner; Gropp, Martin; Steidl, Stefan; Nöth, Elmar; Erzigkeit, Hellmut; Hornegger, Joachim; Kornhuber, Johannes (Tagungsband zum 2. deutschen AAL-Kongress (2. Deutscher AAL (Ambient Assisted Living)-Kongress Berlin), 2009)
Ziel dieser Arbeit ist die Verringerung von Beurteilereinflüssen bei psychometrischen Tests, wie dem Syndrom-Kurztest (SKT), d.h. die Steigerung intersubjektiver Übereinstimmung der Beurteiler, durch die Erfassung validitätssteigernder Variablen mit Hilfe multimodaler Sensoren. Beispiele für solche Sensoren sind Mikrofone, Eyetracker oder Touch-Screens. Die Messung kognitiver Leistungsdefizite nimmt insbesondere bei der Diagnose und Therapiekontrolle altersassoziierter Erkrankungen – zu denen etwa die Demenzen zu zählen sind – einen wichtigen Stellenwert ein. Im Rahmen eines von der Bayerischen Forschungsstiftung (BFS) geförderten Forschungsprojektes (FitForAge) wird untersucht, ob es gelingt, ein interaktives Testsystem zur Erfassung von kognitiven Leistungsstörungen zu konstruieren, das für den Einsatz im telemedizinischen Bereich geeignet ist und vom Patienten weitestgehend selbständig ausgeführt werden kann. Als Grundlage für diese Messungen soll der SKT dienen. Der SKT ist ein international validierter Test zur Erfassung von Gedächtnis- und Aufmerksamkeitsstörungen.

Value-based noise reduction for low-dose dual-energy computed tomography - Balda, Michael; Heismann, Björn; Hornegger, Joachim (Springer-verlag, 2010)
We introduce a value-based noise reduction method for Dual-Energy CT applications. It is based on joint intensity statistics estimated from high- and low-energy CT scans of the identical anatomy in order to reduce the noise level in both scans. For a given pair of measurement values, a local gradient ascension algorithm in the probability space is used to provide a noise reduced estimate. As a consequence, two noise reduced images are obtained. It was evaluated with synthetic data in terms of quantitative accuracy and contrast to noise ratio (CNR)-gain. The introduced method allows for reducing patient dose by at least 30% while maintaining the original CNR level. Additionally, the dose reduction potential was shown with a radiological evaluation on real patient data. The method can be combined with state-of-the-art filter-based noise reduction techniques, and makes low-dose Dual-Energy CT possible for the full spectrum of quantitative CT applications. © 2010 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD5.valueb

Variable Temporal Resolution Reconstruction for Golden-Angle Radial Sparse Parallel DCE-MRI - Grimm, Robert; Nickel, Dominik; Hutter, Jana; Forman, Christoph; Kiefer, Berthold; Hornegger, Joachim; Block, Kai Tobias (Proceedings of the 22nd Annual Meeting of the ISMRM, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.variab

Various Information Sources for HMM with Weighted Multiple Codebooks - Hacker, Christian; Stemmer, Georg; Steidl, Stefan; Nöth, Elmar; Niemann, Heinrich (Proceedings of the Speech Processing Workshop, Magdeburg, Germany, September 09, 2003)
When semicontinuous HMM are used for acoustic modeling in speech recognition usually all states share a single code book. In our investigations we split the feature set into independent subsets and use separate codebooks for each part. This provides a higher modeling flexibility while keeping the parameter space compact. Further experiments integrate new information sources by using additional codebooks which are estimated in a supervised training. For instance codebooks for phone transitions are applied. Codebook exponents weight the different information sources. Relative reductions in word error rate up to 20 % have been achieved.

Velo-CT – A Collaborative 3D Racing Game as Incentive for Medical Image Annotation - Sukesh, Richin; Goshika, Sai Kishore; Narayanan, Srividhya Sathya; Bhombore, Vineet Vinay; Fu, Weilin; Kordon, Florian Johannes; Maier, Andreas (HEALTHINF - 14th International Conference on Health Informatics, 2021)
Introduction Recent advances in algorithmic processing of medical data are largely based on the use of deep learning algorithms that rely on large amounts of annotated data [1]. To cope with the often complex and time-consuming nature of data acquisition and annotation, data donation and distributed crowd-labeling are an appealing approach [2]. In this paper, we follow this idea and present a single/multi-player mobile game which aids in the annotation process of CT or MR scan data. Methods To provide users with an exciting and rewarding annotation experience, our game "Velo-CT" integrates labeling of 3D volume slices into a classical racing game. This is achieved by using data annotation as a key to unlock more advanced racing tracks in the game. To this end, the game has two game modes: 1) Track Racing, and 2) Image Annotation. In “Track Racing”, players compete by steering a vehicle through a racetrack. The main task is to overtake the opponents and try to remain first in position until the end of the race. Checkpoints along the track allow additional guidance if players accidentally leave the racetrack. To provide a more challenging and diverse experience, respawning pick-ups can be found at random locations along the track in the form of floating CT scanners. Such pick-up can either be a speed multiplier (multiplies car speed by 1.2) or a size enhancer (increases the car size by 1.5 to threat opponents and impede their view). The racetracks are modeled based on the segmentation contour of clinical CT data, e.g. showing the respiratory region. For slice segmentation we use a simple K-means classifier. Annotation points earned after beating a track allow the player to unlock new data for the second game mode “Image Annotation”. This is where the medical expertise of the player comes into play. Based on the current progress, the player is presented several medical images which may contain abnormalities. If an abnormality is spotted, the precise location can be marked by a finger tap on the area. The more images a player annotates, the more points can be earned to unlock a new track – and eventually a more intricate anatomy to race on. Results Our game was prototyped in Unity3D [3]. As a first anatomy, respiratory tracks based on the “Low Dose CT Grand Challenge” data were constructed. First experiences by players of different gaming background were reported for the single player setting. Users report a fast-paced racing experience with a healthy amount of challenge provided by pick-ups and competitive AI opponents. The organ-aligned layouts result in tracks similar in shape, which should be addressed in the future by adding multiple anatomical regions. Conclusion Velo-CT provides an intuitive and extendable integration of the often tedious data annotation process for 3D medical data into a rewarding gaming context. The prototype is available for free download and serves as a basis for further extensions to related game modes. References [1] Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik, 29(2), 86-101. [2] Servadei, L., Schmidt, R., Eidelloth, C., & Maier, A. (2017, October). Medical Monkeys: A Crowdsourcing Approach to Medical Big Data. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 87-97). Springer, Cham. [3] Murray, J. W. (2014). C# game programming cookbook for Unity 3D. AK Peters/CRC Press. <br
Keywords: Gamification; Medical Image Annotation; Deep Learning.

Verbesserung der Deep Learning basierten Autodetektion von Hirnmetastasen durch Integration von MRT-Voraufnahmen - Huang, Yixing; Bert, Christoph; Sommer, Philipp; Frey, Benjamin; Gaipl, Udo; Distel, Luitpold; Weissmann, Thomas; Uder, Michael; Schmidt, Manuel; Dörfler, Arnd; Maier, Andreas; Fietkau, Rainer; et al. (Strahlentherapie und Onkologie, 2022)
Keywords: Deep learning; brain metastasis

Vergleich der subjektiv-auditiven RBH-Beurteilung mit apparativer prosodischer Analyse und Irregularitätsberechnung - Haderlein, Tino; Nöth, Elmar; Döllinger, Michael; Schwemmle, Cornelia; Ptok, Martin (Aktuelle phoniatrisch-pädaudiologische Aspekte 2013, 2013)

Verständlichkeitsanalyse bei Kindern mit Lippen-Kiefer-Gaumenspalten auf Deutsch und Italienisch mittels automatischer Spracherkennung - Maier, Andreas; Bocklet, Tobias; Scipioni, Marcello; Nöth, Elmar; Eysholdt, Ulrich; Schuster, Maria (Aktuelle phoniatrisch-pädaudiologische Aspekte 2009, 2009)

Verständlichkeitsbewertung von Telefonaufnahmen Larynxteilresezierter mittels der Kombination von automatischer Spracherkennung und prosodischer Analyse - Haderlein, Tino; Nöth, Elmar; Eysholdt, Ulrich; Rosanowski, Frank (Aktuelle phoniatrisch-pädaudiologische Aspekte 2011, 2011)

Vessel Adapted Regularization for Iterative Reconstruction in MR Angiography - Hutter, Jana; Grimm, Robert; Forman, Christoph; Hornegger, Joachim; Schmitt, Peter (Proceedings of the 20th Annual Meeting of ISMRM, 2012)

Vesselness for Text Detection in Historical Document Images - Hofmann, Simon; Gropp, Martin; Bernecker, David; Pollin, Christopher; Maier, Andreas; Christlein, Vincent (2016 IEEE International Conference on Image Processing (ICIP), 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.vessel
Keywords: Historical document analysis; RootSIFT; Text detection; Vesselness; VLAD

Vessel prior-knowledge regularizaton for MR angiography - Hutter, Jana; Hornegger, Joachim; Schmitt, Peter (Magnetic Resonance Materials in Physics, Biology and Medicine, 2011)

Viewpoint Planning for Quantitative Coronary Angiography - Preuhs, Alexander; Berger, Martin; Bauer, Sebastian; Redel, Thomas; Unberath, Mathias; Achenbach, Stephan; Maier, Andreas (Computer Assisted Radiology and Surgery Proceedings of the 32nd International Congress and Exhibition, 2018)
Purpose: In coronary angiography, the condition of myocardial blood supply is assessed by analyzing 2-D X-ray projections of contrasted coronary arteries. This is done using a flexible C-arm system. Due to the X-ray immanent dimensionality reduction projecting the 3-D scene onto a 2-D image, the viewpoint is critical to guarantee an appropriate view onto the affected artery and, thus, enable reliable diagnosis. In this work, we introduce an algorithm computing optimal viewpoints for the assessment of coronary arteries without the need for 3-D models. Methods: We introduce the concept of optimal viewpoint planning solely based on a single angiographic X-ray image. The subsequent viewpoint is computed such that it is rotated precisely around a vessel, while minimizing foreshortening. Results: Our algorithm reduces foreshortening substantially compared to the input view and completely eliminates it for 90 ∘ rotations. Rotations around isocentered foreshortening-free vessels passing the isocenter are exact. The precision, however, decreases when the vessel is off-centered or foreshortened. We evaluate worst-case boundaries, providing insight in the maximal inaccuracies to be expected. This can be utilized to design viewpoints guaranteeing desired requirements, e.g., a true rotation around the vessel of at minimum 30 ∘. In addition, a phantom study is performed investigating the impact of input views to 3-D quantitative coronary angiography (QCA). Conclusion: We introduce an algorithm for optimal viewpoint planning from a single angiographic X-ray image. The quality of the second viewpoint—i.e., vessel foreshortening and true rotation around vessel—depends on the first viewpoint selected by the physician; however, our computed viewpoint is guaranteed to reduce the initial foreshortening. Our novel approach uses fluoroscopy images only and, thus, seamlessly integrates with the current clinical workflow for coronary assessment. In addition, it can be implemented in the QCA workflow without increasing user interaction, making vessel-shape reconstruction more stable by standardizing viewpoints.
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.viewpo_9
Keywords: Active vision; C-arm; Coronary angiography; Foreshortening; Interventional imaging; Patient-specific imaging; QCA

Viewpoint Planning for Quantitative Coronary Angiography (IJCARS) - Preuhs, Alexander; Berger, Martin; Bauer, Sebastian; Redel, Thomas; Unberath, Mathias; Achenbach, Stephan; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.viewpo_5
Keywords: coronary angiography; C-arm; interventional imaging; QCA; active vision; patient specific imaging; foreshortening

Virtual angiography using CFD simulations based on patient-specific parameter optimization - Endres, Jürgen; Redel, Thomas; Kowarschik, Markus; Hutter, Jana; Hornegger, Joachim; Dörfler, Arnd (2012 9th IEEE International Symposium on Biomedical Imaging, 2012)

VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data - Thamm, Florian; Jürgens, Markus; Ditt, Hendrik; Maier, Andreas (Eurographics Workshop on Visual Computing for Biology and Medicine, 2020)
Computed Tomography Angiography (CTA) is one of the most commonly used modalities in the diagnosis of cerebrovascular diseases like ischemic strokes. Usually, the anatomy of interest in ischemic stroke cases is the Circle of Willis and its peripherals, the cerebral arteries, as these vessels are the most prominent candidates for occlusions. The diagnosis of occlusions in these vessels remains challenging, not only because of the large amount of surrounding vessels but also due to the large number of anatomical variants. We propose a fully automated image processing and visualization pipeline, which provides a full segmentation and modelling of the cerebral arterial tree for CTA data. The model itself enables the interactive masking of unimportant vessel structures e.g. veins like the Sinus Sagittalis, and the interactive planning of shortest paths meant to be used to prepare further treatments like a mechanical thrombectomy. Additionally, the algorithm automatically labels the cerebral arteries (Middle Cerebral Artery left and right, Anterior Cerebral Artery short, Posterior Cerebral Artery left and right) detects occlusions or interruptions in these vessels. The proposed pipeline does not require a prior non-contrast CT scan and achieves a comparable segmentation appearance as in a Digital Subtraction Angiography (DSA).<br
Keywords: Computed Tomography; Image Segmentation; Cerebrovascular Systems; Visualisation

Virtual Hematoxylin and Eosin Transillumination Microscopy Using Epi-Fluorescence Imaging - Giacomelli, Michael; Husvogt, Lennart; Vardeh, Hilde; Faulkner-Jones, Beverly E.; Hornegger, Joachim; Connolly, James L.; Fujimoto, James G. (PLoS ONE, 2016)
We derive a physically realistic model for the generation of virtual transillumination, white light microscopy images using epi-fluorescence measurements from thick, unsectioned tissue. We demonstrate this technique by generating virtual transillumination H&E images of unsectioned human breast tissue from epi-fluorescence multiphoton microscopy data. The virtual transillumination algorithm is shown to enable improved contrast and color accuracy compared with previous color mapping methods. Finally, we present an open source implementation of the algorithm in OpenGL, enabling real-time GPU-based generation of virtual transillumination microscopy images using conventional fluorescence microscopy systems.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.virtua_3

Virtual Single-frame Subtraction Imaging - Unberath, Mathias; Aichert, André; Achenbach, Stephan; Maier, Andreas (Proceedings of the 4th CT Meeting, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.virtua_8

Vision, Modeling, and Visualization 2005, Proc. of the International Workshop, Erlangen, Germany - Greiner, Günther; Hornegger, Joachim; Niemann, Heinrich; Stamminger, Marc (Akademische Verlagsgesellschaft, 2005)
UnivIS-Import:2015-07-08:Pub.2005.tech.IMMD.IMMD5.vision

Visual comparison of speaker groups - Wankerl, Sebastian; Hönig, Florian Thomas; Batliner, Anton; Orozco Arroyave, Juan Rafael; Nöth, Elmar (16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015, 2015)
We describe a generic tool for visualising differences between two groups of speakers who produce a given word sequence. We do this by first time-aligning all recordings and then aggregating time-varying information within each group. By that, we can display prototypical loudness and tempo contours, and also spectrograms, together with information on variability and group effect size over time. An optional user-supplied segmentation (just needed for one of the recordings) can be used to relate local differences to individual phonemes. The system is validated with a group of speakers with Parkinson's disease and an age-matched control group. It will be provided as an opensource software package to the community.
Keywords: Acoustic features; Atypical speech; Interpretation; Paralinguistics; Pathological speech; Visualization

Visualisierung der Differenzierung von Stimm- und Sprechbefunden - Maier, Andreas; Haderlein, Tino; Nöth, Elmar; Rosanowski, Frank; Eysholdt, Ulrich; Schuster, Maria (Aktuelle phoniatrische Aspekte 2008, 2008)

Visualization of Changes in Muscular Activation during Barefoot and Shod Running - Kugler, Patrick; von Tscharner, Vinzenz; Eskofier, Björn; Hornegger, Joachim (Proceeding of 17th Congress of the European Society of Biomechanics, 2010)

Visualization of Intelligibility Measured by Language-Independent Features - Haderlein, Tino; Middag, Catherine; Maier, Andreas; Martens, Jean-Pierre; Döllinger, Michael; Nöth, Elmar (Proc. Text, Speech and Dialogue; 17th International Conference, TSD 2014, 2014)
Automatic intelligibility assessment using automatic speech recognition is usually language-specific. In this study, a language-independent approach based on alignment-free phonological and phonemic features is proposed. It utilizes models that are trained with Flemish speech, and it is applied to assess dysphonic German speakers. In order to visualize the results, two techniques were tested: a plain selection of most relevant features emerging from Ensemble Linear Regression involving feature selection, and a Sammon transform of all the features to a 3-D space. The test data comprised recordings of 73 hoarse persons (48.3 ± 16.8 years) who read the German version of the text "The North Wind and the Sun". The reference evaluation was obtained by five speech therapists and physicians who rated intelligibility according to a 5-point Likert scale. In the 3-D visualization, the different levels of intelligibility were clearly separated. This could be the basis for an objective support for diagnostics in voice and speech rehabilitation. © 2014 Springer International Publishing.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.visual_0
Keywords: speech

Visualization of Neural Networks using NeuroGraph, Uses in University Education - Wilke, Peter (Proc. IFIP Working Group 3.2 Working Conference on Visualization in Scientific Computing, 1993)

Visualization of Voice Disorders Using the Sammon Transform - Haderlein, Tino; Zorn, Dominik; Steidl, Stefan; Nöth, Elmar; Shozakai, Makoto; Schuster, Maria (Proc. Text, Speech and Dialogue; 9th International Conference, 2006)

Visualizing microvascular flow variation in OCTA using variable interscan time analysis (VISTA) - Moult, Eric M.; Ploner, Stefan; Choi, Woojhon; Lee, Byungkun; Husvogt, Lennart; Lu, Chen D.; Novais, Eduardo A.; Cole, Emily D.; Potsaid, Benjamin M.; Duker, Jay S.; Hornegger, Joachim; Maier, Andreas; et al. (Proc. SPIE 10053, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI, 2017)
OCT angiography (OCTA) has recently garnered immense interest in clinical ophthalmology, permitting ocular vasculature to be viewed in exquisite detail, in vivo, and without the injection of exogenous dyes. However, commercial OCTA systems provide little information about actual erythrocyte speeds; instead, OCTA is typically used to visualize the presence and/or absence of vasculature. This is an important limitation because in many ocular diseases, including diabetic retinopathy (DR) and age-related macular degeneration (AMD), alterations in blood flow, but not necessarily only the presence or absence of vasculature, are thought to be important in understanding pathogenesis. To address this limitation, we have developed an algorithm, variable interscan time analysis (VISTA), which is capable of resolving different erythrocyte speeds. VISTA works by acquiring >2 repeated B-scans, and then computing multiple OCTA signals corresponding to different effective interscan times. The OCTA signals corresponding to different effective interscan times contain independent information about erythrocyte speed. In this study we provide a theoretical overview of VISTA, and investigate the utility of VISTA in studying blood flow alterations in ocular disease. OCTA-VISTA images of eyes with choroidal neovascularization, geographic atrophy, and diabetic retinopathy are presented.<br
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.visual
Keywords: oct; octa; oct angiography; vista; variable interscan time analysis; blood flow

Vito - A Generic Agent for Multi-physics Model Personalization: Application to Heart Modeling - Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; Comaniciu, Dorin (Medical Image Computing and Computer-Assisted Intervention MICCAI 2015, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.vitoag_0

Vito – a generic agent for multi-physics model personalization: Application to heart modeling - Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; Comaniciu, Dorin (Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part II, 2015)
Precise estimation of computational physiological model parameters from patient data is one of the main hurdles towards their clinical applicability. Designing robust estimation algorithms is often a tedious and model-specific process. We propose to use, for the first time to our knowledge, artificial intelligence (AI) concepts to learn how to personalize a computational model, inspired by how an expert manually personalizes. We reformulate the parameter estimation problem in terms of Markov decision process and reinforcement learning. In an off-line phase, the artificial agent, called Vito, automatically learns a representative state-action-state model through data-driven exploration of the computational model under consideration. In other words, Vito learns how the model behaves under change of parameters and how to personalize it. Vito then controls the on-line personalization by exploiting its automatically derived action policy. Because the algorithm is model-independent, personalizing a completely new model would require only adjusting some simple parameters of the agent and defining the observations to match, without the full knowledge of the model itself. Vito was evaluated on two challenging problems: the inverse problem of cardiac electrophysiology and the personalization of a lumped-parameter whole-body circulation model. Obtained results suggested that Vito could achieve equivalent goodness of fit than standard methods, while being more robust (up to 25% higher success rates) and with faster (up to three times) convergence rate. Our AI approach could thus make model personalization algorithms generalizable and self-adaptable to any patient, like a human operator.

Voice and Speech Assessment From Telephone Recordings Using Prosodic Analysis Based on mu-Law-Companded Features - Haderlein, Tino; Schützenberger, Anne; Döllinger, Michael; Nöth, Elmar (Speech Communication - 12. ITG Fachtagung Sprachkommunikation, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.voicea
Keywords: Sprache

Voice Assessment of Speakers with Laryngeal Cancer by Glottal Excitation Modeling Based on a 2-Mass Model - Bocklet, Tobias; Nöth, Elmar; Stemmer, Georg (Proceedings of TSD2011, 2011)

Voiced/unvoiced transitions in speech as a potential bio-marker to detect Parkinson's disease - Orozco-Arroyave, J. R.; Hönig, Florian Thomas; Arias-Londoño, J. D.; Vargas-Bonilla, J. F.; Skodda, S.; Rusz, J.; Nöth, Elmar (16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015, 2015)
Several studies have addressed the automatic classification of speakers with Parkinson's disease (PD) and healthy controls (HC). Most of the studies are based on speech recordings of sustained vowels, isolated words, and single sentences. Only few investigations have considered read texts and/or spontaneous speech. This paper addresses two main questions still open regarding the automatic analysis speech in patients with PD, (a) "Is it possible to classify PD patients and HC through running speech signals in multiple languages", and (b) "where is the information to discriminate between speech recordings of PD patients and HC" In this paper speech recordings of read texts and monologues spoken in three different languages are considered. The energy content of the borders between voiced and unvoiced sounds is modeled. According to the results with read texts it is possible to achieve accuracies ranging from 91% to 98% depending on the language. With respect to the results on monologues, the accuracies are above 98% in all of the three languages. The presence of discriminant information in the voiced/unvoiced and unvoiced/voiced transitions is validated here, evidencing the problems of PD patients to stop/start the vocal folds movement during the production of running speech.
Keywords: Dysarthria; Energy content; Hesitation in speech; Language and motor planning; Parkinson's disease; Voiced/unvoiced transitions

Volume Quantification of the Thyroid Gland Using 3-D Ultrasound Imaging - Kollorz, Eva Nicole Karin; Hahn, Dieter; Grunewald, Markus; Goecke, Tamme W.; Hornegger, Joachim; Kuwert, Torsten (Nuklearmedizin Kongressausgabe 02/07, 2007)