Volumetric Histology-3D-reconstruction of histological tissue slices for a better functional understanding - Gaffling, Simone (JOURNAL OF ANATOMY, 2020)
CRIS-Team WoS Importer:2020-04-24

Vowel- and Text-based Cepstral Analysis of Chronic Hoarseness - Haderlein, Tino; Moers, Cornelia; Moebius, Bernd; Rosanowski, Frank; Nöth, Elmar; Eysholdt, Ulrich; Haderlein, Tino (Journal of Voice, 2012)

Walk Up Jump Down - a new Hybrid Algorithm for Time Tabling Problems - Wilke, Peter; Killer, Helmut (Proc. PATAT 2010, 2010)

Water-Fat Separation Using a Locally Low-Rank Enforcing Reconstruction - Lugauer, Felix; Nickel, Dominik; Wetzl, Jens; Kiefer, Berthold; Hornegger, Joachim (Proceedings of the 23rd Annual Meeting of the ISMRM (ISMRM 2015), 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.waterf

Wav2vec behind the Scenes: How end2end Models learn Phonetics - tom Dieck, Teena; Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Nöth, Elmar; Klumpp, Philipp (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2022)
End2end models became extremely popular in recent years. Whilst they excel at tasks like acoustic modelling or full-fledged speech recognition, the decision making process can be quite complex to retrace due to their black-box character. As end2end models learn high-level feature extraction on-the-fly, outputs from hidden layers from within the network had been used as feature vectors in various studies to perform transfer learning. It is therefore crucial to understand how extracted hidden activations transport information collected from the signal. Furthermore, is the traditional categorization into feature extractor and temporal analysis still applicable on the sub-parts of end2end models? By the example of Wav2vec 2.0, we show how an acoustic model learns to perform a frequency analysis on a speech waveform. Our experiments also show that phonetic information about speech production is preserved in extracted feature vectors. Ultimately, our findings highlight how different parts of an end2end model encode information on an entirely different level. Whilst the influence of gender is quite large on early feature vectors, it vanished after temporal contextualization. At the same time, hidden activations which included context information were superimposed by language-related patterns.
CRIS-Team Scopus Importer:2022-10-28
Keywords: Acoustic Modelling; Feature Extraction; Phonetic Analysis

Wav2vec behind the Scenes: How end2end Models learn Phonetics - tom Dieck, Teena; Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Nöth, Elmar; Klumpp, Philipp (23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022, 2022)
Keywords: Phonetic Analysis; Feature Extraction; Acoustic Modelling

Wavelet Based Noise Reduction by Identification of Correlations - Borsdorf, Anja; Raupach, Rainer; Hornegger, Joachim (Springer-verlag, 2006)
In this paper we present a novel wavelet based method for edge preserving noise reduction. In contrast to most common methods, the algorithm introduced here does not work on single input data. It takes two or more spatially identical images, which are both impaired by noise. Assuming the statistical independence of noise in the different images, correlation computations can be used in order to preserve structures while reducing noise. Different methods for correlation analysis have been investigated, on the one hand based directly on the original input images and on the other hand taking into account the wavelet representation of the input data. The presented approach proves to be suited for the application in computed tomography, where high noise reduction rates of approximately 50% can be achieved without loss of structure information. © Springer-Verlag Berlin Heidelberg 2006.
UnivIS-Import:2015-04-16:Pub.2006.tech.IMMD.IMMD5.wavele
Keywords: wavelets; noise reduction; correlation analysis

Wavelet based noise reduction in CT-images using correlation analysis - Borsdorf, Anja; Raupach, Rainer; Flohr, Thomas; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2008)
The projection data measured in computed tomography (CT) and, consequently, the slices reconstructed from these data are noisy. We present a new wavelet based structure-preserving method for noise reduction in CT-images that can be used in combination with different reconstruction methods. The approach is based on the assumption that data can be decomposed into information and temporally uncorrected noise. In CT two spatially identical images can be generated by reconstructions from disjoint subsets of projections: using the latest generation dual source CT-scanners one image can be reconstructed from the projections acquired at the first, the other image from the projections acquired at the second detector. For standard CT-scanners the two images can be generated by splitting up the set of projections into even and odd numbered projections. The resulting images show the same information but differ with respect to image noise. The analysis of correlations between the wavelet representations of the input images allows separating information from noise down to a certain signal-to-noise level. Wavelet coefficients with small correlation are suppressed, while those with high correlations are assumed to represent structures and are preserved. The final noise-suppressed image is reconstructed from the averaged and weighted wavelet coefficients of the input images. The proposed method is robust, of low complexity and adapts itself to the noise in the images. The quantitative and qualitative evaluation based on phantom as well as real clinical data showed, that high noise reduction rates of around 40% can be achieved without noticable loss of image resolution. © 2008 IEEE.

Wavelet denoising of multi frame optical coherence tomography data - Mayer, Markus Anton; Borsdorf, Anja; Wagner, Martin; Hornegger, Joachim; Mardin, Christian Y.; Tornow, Ralf-Peter (Biomedical Optics Express, 2012)
We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation. Based on this analysis, the wavelet detail coefficients are weighted, averaged and reconstructed. At a signal-to-noise gain at about 100% we observe only a minor sharpness decrease, as measured by a full-width-half-maximum reduction of 10.5%. While a similar signal-to-noise gain would require averaging of 29 frames, we achieve this result using only 8 frames as input to the algorithm. A possible application of the proposed algorithm is preprocessing in retinal structure segmentation algorithms, to allow a better differentiation between real tissue information and unwanted speckle noise. © 2012 Optical Society of America.

Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography - Jiang, Zhe; Huang, Zhiyu; Qiu, Bin; Meng, Xiangxi; You, Yunfei; Liu, Xi; Geng, Mufeng; Liu, Gangjun; Zhou, Chuanqing; Yang, Kun; Maier, Andreas; Ren, Qiushi; et al. (IEEE transactions on medical imaging, 2021)
Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among images. However, these existing deep learning-based methods depend on high-quality labels, which are hard to acquire considering imaging hardware limitations and practical data acquisition conditions. In this article, we proposed an unprecedented weakly supervised deep learning-based pipeline for OCTA reconstruction task, in the absence of high-quality training labels. The proposed pipeline was investigated on an in vivo animal dataset and a human eye dataset by a cross-validation strategy. Compared with supervised learning approaches, the proposed approach demonstrated similar or even better performance in the OCTA reconstruction task. These investigations indicate that the proposed weakly supervised learning strategy is well capable of performing OCTA reconstruction, and has a certain potential towards clinical applications.
CRIS-Team Scopus Importer:2021-02-26
Keywords: noise2noise; OCTA; ophthalmology; vascular imaging; Weakly supervised learning

Wearable sensors provide objective gait parameters correlating to clinical scores in Parkinson’s disease - Gaßner, Heiko; Schlachetzki, Johannes; Marxreiter, Franz; Kohl, Zacharias; Barth, Jens; Goßler, Julia; Reinfelder, Samuel; Pasluosta, Cristian Federico; Winkler, Jürgen; Eskofier, Björn; Klucken, Jochen (89th Congress of the Deutschen Gesellschaft für Neurologie mit Fortbildungsakademie (DGN), 2016)

Wearable Static Posturography Solution Using a Novel Pressure Sensor Sole - Reinfelder, Samuel; Durlak, Felix; Barth, Jens; Klucken, Jochen; Eskofier, Björn (Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.wearab

Wearable Trick Classification in Freestyle Snowboarding - Groh, Benjamin; Fleckenstein, Martin; Eskofier, Björn; Groh, Benjamin; Fleckenstein, Martin; Eskofier, Björn (IEEE EMBS 13th Annual International Body Sensor Networks Conference, 2016)
Digital motion analysis in freestyle snowboarding requires a stable trick detection and accurate classification. Freestyle snowboarding contains several trick categories that all have to be recognized for an application in training sessions or competitions. While previous work already addressed the classification of specific tricks or turns, there is no known method that contains a full pipeline for detection and classification of tricks from multiple categories. In this paper, we suggest a classification pipeline containing the detection, categorization and classification of tricks of two major freestyle trick categories. We evaluated our algorithm based on data from two different acquisitions with a total number of eleven athletes and 275 trick events. Tricks of both categories were categorized with recall results of 96.6% and 97.4%. The classification of the tricks was evaluated to an accuracy of 90.3% for the first and 93.3% for the second category.

Werkstoff- und prozessoptimierte Herstellung flächiger Kunststoff-Kunststoff und Kunststoff-Metall-Verbundbauteile - Amesöder, Simon; Fernandez, Luis; Niemann, Heinrich; Deventer, Rainer; Ehrenstein, Gottfried (DFG Sonderforschungsbereich 396 Berichts- und Industriekolloqium 15./16. Oktober 2003, 2003)

What Makes the Cepstral Peak Prominence Different to Other Acoustic Correlates of Vocal Quality? - Ferrer Riesgo, Carlos A.; Nöth, Elmar (Journal of Voice, 2020)
CRIS-Team Scopus Importer:2020-04-07
Keywords: CPP—Cepstrum—Autocorrelation—HNR—Voice analysis

Whodunnit - Searching for the Most Important Feature Types Signalling Emotion-Related User States in Speech - Batliner, Anton; Steidl, Stefan; Schuller, Björn; Seppi, Dino; Vogt, Thurid; Wagner, Johannes; Devillers, Laurence; Vidrascu, Laurence; Aharonson, Vered; Kessous, Loic; Amir, Noam (Computer Speech and Language, 2011)
In this article, we describe and interpret a set of acoustic and linguistic features that characterise emotional/emotion-related user states con ned to the one database processed: four classes in a German corpus of children interacting with a pet robot. To this end, we collected a very large feature vector consisting of more than 4000 features extracted at di erent sites. We performed extensive feature selection (Sequential Forward Floating Search) for seven acoustic and four linguistic types of features, ending up in a small number of `most important' features which we try to interpret by discussing the impact of di erent feature and extraction types. We establish di erent measures of impact and discuss the mutual in uence of acoustics and linguistics.
Keywords: feature types; feature selection; automatic classification; emotion

Whole Body MRI Intersity Standardization - Jäger, Florian; Nyul, Laszlo Gabor; Frericks, Bernd; Wacker, Frank; Hornegger, Joachim (Bildverarbeitung für die Medizin 2007, 2007)

Wissensbasierte Nicht-Starre Registrierung von SPECT/CT Datensätzen - Jäger, Florian; Han, Jingfeng; Hornegger, Joachim; Kuwert, Torsten (Bildverarbeitung für die Medizin 2006, 2006)

With Semantics and Hidden Markov Models to an Adaptive Log File Parser - Kuhnert, Nadine; Maier, Andreas (International Journal on Natural Language Computing, 2020)
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text clustering based on semantics of log file lines. Next, we only take the most relevant cluster into account and focus only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file parsing. With changes in the raw log file distorting learned patterns, we aim the model to adapt automatically in order to maintain high quality output. After training our model on one system type and applying the resulting, adaptive parsing rules to a different system with slightly different log file patterns, we achieve an accuracy over 99.99%.<br
Keywords: Hidden Markov Models; Parameter Extraction; Parsing; Text Mining; Information Retrieval

Word Accent and Emotion - Seppi, Dino; Batliner, Anton; Steidl, Stefan; Schuller, Björn; Nöth, Elmar (Proceedings of Speech Prosody 2010, 2010)

Word accuracy and dynamic time warping to assess intelligibility deficits in patients with Parkinsons disease - Vasquez Correa, Juan; Rafael Orozco-Arroyave, Juan; Nöth, Elmar (21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016, 2016)
Keywords: Dynamic time Warping; Classification; Parkinon's disease; Intelligibility; Word accuracy; Regression

Workflow Phase Detection in Fluoroscopic Images Using Convolutional Neural Networks - Arbogast, Nikolaus; Kurzendorfer, Tanja; Breininger, Katharina; Mountney, Peter; Toth, Daniel; Narayan, Srinivas A.; Maier, Andreas (Informatik aktuell, 2019)
In image guided interventions, the radiation dose to the patient and personnel can be reduced by positioning the blades of a collimator to block off unnecessary X-rays and restrict the irradiated area to a region of interest. In a certain stage of the operation workflow phase detection can define objects of interest to enable automatic collimation. Workflow phase detection can be beneficial for clinical time management or operating rooms of the future. In this work, we propose a learningbased approach for an automatic classification of three surgical workflow phases. Our data consists of 24 congenital cardiac interventions with a total of 2985 fluoroscopic 2D X-ray images. We compare two different convolutional neural network architectures and investigate their performance regarding each phase. Using a residual network, a class-wise averaged accuracy of 86.14% was achieved. The predictions of the trained models can then be used for context specific collimation.
CRIS-Team Scopus Importer:2019-05-14

Writer Identification and Verification Using GMM Supervectors - Christlein, Vincent; Bernecker, David; Hönig, Florian Thomas; Angelopoulou, Elli (Proceedings of the 2014 IEEE Winter Conference on Applications of Computer Vision, 2014)
This paper proposes a new system for offline writer identification and writer verification. The proposed method uses GMM supervectors to encode the feature distribution of individual writers. Each supervector originates from an individual GMM which has been adapted from a background model via a maximum-a-posteriori step followed by mixing the new statistics with the background model. We show that this approach improves the TOP-1 accuracy of the current best ranked methods evaluated at the ICDAR-2013 competition dataset from 95.1% [13] to 97.1%, and from 97.9% [11] to 99.2% at the CVL dataset, respectively. Additionally, we compare the GMM supervector encoding with other encoding schemes, namely Fisher vectors and Vectors of Locally Aggregated Descriptors. © 2014 IEEE.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.writer_4
Keywords: writer identification; writer verifcication; gmm supervectors

Writer Identification Using GMM Supervectors and Exemplar-SVMs - Christlein, Vincent; Bernecker, David; Hönig, Florian Thomas; Maier, Andreas; Angelopoulou, Elli (Pattern Recognition, 2017)
UnivIS-Import:2017-01-09:Pub.2017.tech.IMMD.IMMD5.writer
Keywords: Writer identification; GMM supervectors; Exemplar-SVM

Writer Identification Using VLAD Encoded Contour-Zernike Moments - Christlein, Vincent; Bernecker, David; Angelopoulou, Elli (Document Analysis and Recognition (ICDAR), 2015 13th International Conference on, 2015)
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.IMMD5.writer_3

Writer Retrieval and Writer Identification in Greek Papyri - Christlein, Vincent; Marthot-Santaniello, Isabella; Mayr, Martin; Nicolaou, Anguelos; Seuret, Mathias (Intertwining Graphonomics with Human Movements, 2022)
The analysis of digitized historical manuscripts is typically addressed by paleographic experts. Writer identification refers to the classification of known writers while writer retrieval seeks to find the writer by means of image similarity in a dataset of images. While automatic writer identification/retrieval methods already provide promising results for many historical document types, papyri data is very challenging due to the fiber structures and severe artifacts. Thus, an important step for an improved writer identification is the preprocessing and feature sampling process. We investigate several methods and show that a good binarization is key to an improved writer identification in papyri writings. We focus mainly on writer retrieval using unsupervised feature methods based on traditional or self-supervised-based methods. It is, however, also comparable to the state of the art supervised deep learning-based method in the case of writer classification/re-identification.<br

XDose: toward online cross-validation of experimental and computational X-ray dose estimation - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Ochs, Philipp; Stepina, Elizaveta; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2020)
Purpose: As the spectrum of X-ray procedures has increased both for diagnostic and for interventional cases, more attention is paid to X-ray dose management. While the medical benefit to the patient outweighs the risk of radiation injuries in almost all cases, reproducible studies on organ dose values help to plan preventive measures helping both patient as well as staff. Dose studies are either carried out retrospectively, experimentally using anthropomorphic phantoms, or computationally. When performed experimentally, it is helpful to combine them with simulations validating the measurements. In this paper, we show how such a dose simulation method, carried out together with actual X-ray experiments, can be realized to obtain reliable organ dose values efficiently. Methods: A Monte Carlo simulation technique was developed combining down-sampling and super-resolution techniques for accelerated processing accompanying X-ray dose measurements. The target volume is down-sampled using the statistical mode first. The estimated dose distribution is then up-sampled using guided filtering and the high-resolution target volume as guidance image. Second, we present a comparison of dose estimates calculated with our Monte Carlo code experimentally obtained values for an anthropomorphic phantom using metal oxide semiconductor field effect transistor dosimeters. Results: We reconstructed high-resolution dose distributions from coarse ones (down-sampling factor 2 to 16) with error rates ranging from 1.62 % to 4.91 %. Using down-sampled target volumes further reduced the computation time by 30 % to 60 %. Comparison of measured results to simulated dose values demonstrated high agreement with an average percentage error of under 10 % for all measurement points. Conclusions: Our results indicate that Monte Carlo methods can be accelerated hardware-independently and still yield reliable results. This facilitates empirical dose studies that make use of online Monte Carlo simulations to easily cross-validate dose estimates on-site.
CRIS-Team Scopus Importer:2020-12-11
Keywords: Anthropomorphic phantom; Dosimetry; Monte Carlo simulation; MOSFET

X-ray Computed Tomography of Flame Structure in Porous Media Burners - Wu, Meng; Dunnmon, Jared; Xia, Yan; Hinshaw, Waldo; Pelc, Norbert; Maier, Andreas; Fahrig, Rebecca; Ihme, Matthias (Proceedings of CT Meeting 2016, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.xrayco

X-ray Imaging - Berger, Martin; Yang, Qiao; Maier, Andreas (Springer, 2018)
UnivIS-Import:2019-03-21:Pub.2018.tech.IMMD.IMMD5.xrayim

X-ray Phase Contrast: Research on a Future Imaging Modality - Bopp, Johannes; Felsner, Lina; Hu, Shiyang; Käppler, Sebastian; Riess, Christian (Springer, 2018)
UnivIS-Import:2019-03-25:Pub.2018.tech.IMMD.IMMD5.xrayph_1

X-ray Phase Contrast: Research on a Future Imaging Modality - Bopp, Johannes; Felsner, Lina; Hu, Shiyang; Käppler, Sebastian; Riess, Christian (Medical Imaging Systems - An Introductory Guide, 2018)

X-ray Scatter Estimation Using Deep Splines - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Felsner, Lina; Hoppe, Elisabeth; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (IEEE Transactions on Medical Imaging, 2021)
X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics’ constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results. Unfortunately, in the context of medical imaging, those may be misleading and could lead to wrong conclusions. To overcome this problem, we propose to embed B-splines as a known operator into neural networks. This inherently constrains their predictions to well-behaved and smooth functions. In a study using synthetic head and thorax data as well as real thorax phantom data, we found that our approach performed on par with U-net when comparing both algorithms based on quantitative performance metrics. However, our approach not only reduces runtime and parameter complexity, but we also found it much more robust to unseen noise levels. While the U-net responded with visible artifacts, the proposed approach preserved the X-ray signal’s frequency characteristics.
CRIS-Team Scopus Importer:2021-05-07
Keywords: Approximation; B-spline; Detectors; Estimation; Neural network; Neural networks; Photonics; Splines (mathematics); Thorax; X-ray imaging; X-ray scatter

X-ray source trajectories and their R-line coverage for long-object CB imaging with a C-arm system - Yu, Zhicong; Noo, Frédéric; Dennerlein, Frank; Lauritsch, Günter; Hornegger, Joachim (The first international conference on image formation in x-ray computed tomography, 2010)

X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery - Bier, Bastian; Unberath, Mathias; Zaech, Jan-Nico; Fotouhi, Javad; Armand, Mehran; Osgood, Greg; Navab, Nassir; Maier, Andreas (MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV, 2018)
CRIS-Team WoS Importer:2019-08-09

'You are sooo cool, Valentina!' Recognizing social attitude in speech-based dialogues with an ECA - de Rosis, Fiorella; Batliner, Anton; Novielli, Nicole; Steidl, Stefan (Affective Computing and Intelligent Interaction, 2007)
We propose a method to recognize the ‘social attitude’ of users towards an Embodied Conversational Agent from a combination of linguistic and prosodic features. After describing the method and the results of applying it to a corpus of dialogues collected with a Wizard of Oz study, we discuss the advantages and disadvantages of statistical and machine learning methods if compared with other knowledge-based methods

You BEEP Machine - Emotion in Automatic Speech Understanding Systems - Huber, Richard; Nöth, Elmar; Batliner, Anton; Buckow, Jan-Constantin; Warnke, Volker; Niemann, Heinrich (Proceedings of the First Workshop on Text, Speech, Dialogue - TSD'98, 1998)

Your Personal Movie Producer: Generating Highlight Videos in Soccer Using Wearables - Schuldhaus, Dominik; Jakob, Carolin; Zwick, Constantin; Koerger, Harald; Eskofier, Björn (Proceedings of the 2016 ACM International Symposium on Wearable Computers, 2016)
Manually browsing through high amount of sports videos, selecting interesting highlight scenes, and applying video effects are time-consuming and one major burden these days. Automatic approaches are preferred, but currently require high-quality TV broadcast material which is usually not available in recreational sports. Thus, the purpose of this paper was to develop a personal low-cost movie producer for highlight videos using wearables. The feasibility of the proposed approach was shown for soccer scenarios. The automatic highlight video generation included three contributions: (i) sensor-based full-instep kick detection and extraction of corresponding video segments, (ii) sensor-based ball speed estimation for provision of highlight-related metadata shown in the final video, and (iii) sensor-driven video effect generation. The proposed system was evaluated on eleven subjects which were equipped with inertial sensors in the cavity of soccer shoes. A mean sensitivity of 95.6 % and a mean absolute error of 7.7 km/h were achieved for the full-instep kick classification and the ball speed estimation, respectively. This personal movie producer based on wearables is a novel idea to provide recreational athletes with attractive automatically generated highlight videos in sports.
Keywords: Wearables; Inertial Sensors; Soccer; Data Mining; Highlight Videos

"You stupid tin box" - children interacting with the AIBO robot: A cross-linguistic emotional speech corpus. - Batliner, Anton; Hacker, Christian; Steidl, Stefan; Nöth, Elmar; D'Arcy, S.; Russell, Martin; Wong, M. (Proceedings of the 4th International Conference of Language Resources and Evaluation LREC 2004, 2004)

Zusammenhänge und Unterschiede zwischen Graph-Grammatiken und Petri-Netzen sowie verwandter Systeme Arbeitsberichte des IMMD der Universität Erlangen-Nürnberg - Wilke, Peter (1983)

Zusammenhänge und Unterschiede zwischen Graph-Grammatiken und Petri-Netzen sowie verwandter Systeme - Wilke, Peter (1997)

Zwischenbericht Entwicklung eines Software Werkzeuges für Risiko-Management - Wilke, Peter; et al. (2001)

An analytical approach for the simulation of realistic low-dose fluoroscopic images - Hariharan, Sai Gokul; Strobel, Norbert; Kaethner, Christian; Kowarschik, Markus; Fahrig, Rebecca; Navab, Nassir (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose The quality of X-ray images plays an important role in computer-assisted interventions. Although learning-based denoising techniques have been shown to be successful in improving the image quality, they often rely on pairs of associated low- and high-dose X-ray images that are usually not possible to acquire at different dose levels in a clinical scenario. Moreover, since data variation is an important requirement for learning-based methods, the use of phantom data alone may not be sufficient. A possibility to address this issue is a realistic simulation of low-dose images from their related high-dose counterparts.MethodWe introduce a novel noise simulation method based on an X-ray image formation model. The method makes use of the system parameters associated with low- and high-dose X-ray image acquisitions, such as system gain and electronic noise, to preserve the image noise characteristics of low-dose images.ResultsWe have compared several corresponding regions of the associated real and simulated low-dose imagesobtained from two different imaging systemsvisually as well as statistically, using a two-sample Kolmogorov-Smirnov test at 5% significance. In addition to being visually similar, the hypothesis that the corresponding regionsfrom 80 pairs of real and simulated low-dose regionsbelonging to the same distribution has been accepted in 81.43% of the cases.ConclusionThe results suggest that the simulated low-dose images obtained using the proposed method are almost indistinguishable from real low-dose images. Since extensive calibration procedures required in previous methods can be avoided using the proposed approach, it allows an easy adaptation to different X-ray imaging systems. This in turn leads to an increased diversity of the training data for potential learning-based methods.
CRIS-Team WoS Importer:2019-04-02

CLC: Complex Linear Coding for the DNS 2020 Challenge - Schröter, Hendrik; Rosenkranz, Tobias; Escalante Banuelos, Alberto; Maier, Andreas (2020)
Complex-valued processing brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the noise reduction process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram. Complex masks (CM) usually outperform real-valued masks due to their ability to modify the phase. Recent work proposed to use a complex linear combination of coefficients called complex linear coding (CLC) instead of a point-wise multiplication with a mask. This allows to incorporate information from previous and optionally future time steps which results in superior performance over mask-based enhancement for certain noise conditions. In fact, the linear combination enables to model quasi-steady properties like the spectrum within a frequency band. In this work, we apply CLC to the Deep Noise Suppression (DNS) challenge and propose CLC as an alternative to traditional mask-based processing, e.g. used by the baseline. We evaluated our models using the provided test set and an additional validation set with real-world stationary and non-stationary noises. Based on the published test set, we outperform the baseline w.r.t. the scale independent signal distortion ratio (SI-SDR) by about 3dB.
Keywords: speech enhancement; noise reduction; recurrent neural networks

Data-driven estimation of noise variance stabilization parameters for low-dose X-ray images - Hariharan, Sai Gokul; Strobel, Norbert; Kaethner, Christian; Kowarschik, Markus; Fahrig, Rebecca; Navab, Nassir (Physics in Medicine and Biology, 2020)
Purpose. Denoising X-ray images corrupted by signal-dependent mixed noise is usually approached either by considering noise statistics directly or by using noise variance stabilization (NVS) techniques. An advantage of the latter is that the noise variance can be stabilized to a known constant throughout the image, facilitating the application of denoising algorithms designed for the removal of additive Gaussian noise. A well-performing NVS is the generalized Anscombe transform (GAT). To calculate the GAT, the system gain as well as the variance of electronic noise are required. Unfortunately, these parameters are difficult to predict from the X-ray tube settings in clinical practice, because the system gain observed at the detector depends on the beam hardening caused by the patient. Materials and Methods. We propose a data-driven method for estimating the parameters required to carry out an NVS using the GAT. It utilizes the energy compaction property of the discrete cosine transform to obtain the NVS parameters using a robust regression approach relying on a linear Poisson-Gaussian model. The method has been experimentally validated with respect to beam hardening as well as denoising performance for different dose and scatter levels. Results. Across a range of low-dose X-ray settings, the proposed robust regression approach has estimated both system gain and electronic noise level with an average error of only 4.2%. When used to perform a GAT followed by the denoising of low-dose X-ray images, performance gains of 5% for peak-signal-to-noise ratio and 4% for structural similarity index can be obtained. Conclusion. The parameters needed to calculate the GAT can be estimated efficiently and robustly using a data-driven approach. The improved parameter estimation method facilitates a more accurate GAT-based NVS and, hence, better denoising of low-dose X-ray images when algorithms designed for additive Gaussian noise are applied.
CRIS-Team Scopus Importer:2020-12-11
Keywords: Low-dose X-ray imaging; Noise level function estimation; Noise variance stabilization

Influence of X-Ray Radiation on Historical Paper - Zippert, Patrick; Seuret, Mathias; Maier, Andreas; Hausotte, Tino (11th Conference on Industrial Computed Tomography, 2022)
Historical books are often so fragile that they cannot easily be opened in order to read the contents without damaging them. Current research shows, that industrial X-ray computed tomography (CT) can be used to examine closed historical manuscripts. However, the effect of X-rays on ancient paper has rarely been studied, making it difficult to assess the impact of CT measurements on fragile historical manuscripts in terms of additional destruction. To address this problem, various types of paper were exposed to high levels of X-ray radiation in order to examine them for change in optical properties. The investigations showed increasing yellowing due to the increased radiation energy. However, the severity of the yellowing is strongly dependent on the composition of the paper.<br
Keywords: Historical documents; CT measurement side effects; radiation damage

Latent Shape Constraint for Anatomical Landmark Detection on Spine Radiographs - Kordon, Florian Johannes; Maier, Andreas; Kunze, Holger (Bildverarbeitung für die Medizin 2021, 2021)
Vertebral corner points are frequently used landmarks for a vast variety of orthopedic and trauma surgical applications. Algorithmic approaches that are designed to automatically detect them on 2D radiographs have to cope with varying image contrast, high noise levels, and superimposed soft tissue. To enforce an anatomically correct landmark configuration in presence of these limitations, this study investigates a shape constraint technique based on data-driven encodings of the spine geometry. A contractive PointNet autoencoder is used to map numerical landmark coordinate representations onto a low-dimensional shape manifold. A distance norm between prediction and ground truth encodings then serves as an additional loss term during optimization. The method is compared and evaluated on the SpineWeb16 dataset. Small improvements can be observed, recommending further analysis of the encoding design and composite cost function.<br
Keywords: Autoencoder; Shape constraint; X-ray Image Processing

Learning-based Patch-wise Metal Segmentation with Consistency Check - Gottschalk, Tristan; Maier, Andreas; Kordon, Florian Johannes; Kreher, Björn W. (Bildverarbeitung für die Medizin 2021, 2021)
Metal implants that are inserted into the patient's body during trauma interventions cause heavy artifacts in 3D X-ray acquisitions. Metal Artifact Reduction (MAR) methods, whose first step is always a segmentation of the present metal objects, try to remove these artifacts. Thereby, the segmentation is a crucial task which has strong influence on the MAR's outcome. This study proposes and evaluates a learning-based patch-wise segmentation network and a newly proposed Consistency Check as post-processing step. The combination of the learned segmentation and Consistency Check reaches a high segmentation performance with an average IoU score of 0.924 on the test set. Furthermore, the Consistency Check proves the ability to significantly reduce false positive segmentations whilst simultaneously ensuring consistent segmentation
Keywords: Metal Artifact Reduction; Deep Learning; Consistency Check

Learning-Based X-Ray Image Denoising Utilizing Model-Based Image Simulations - Hariharan, Sai Gokul; Kaethner, Christian; Strobel, Norbert; Kowarschik, Markus; Albarqouni, Shadi; Fahrig, Rebecca; Navab, Nassir (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
X-ray guidance is an integral part of interventional procedures, but the exposure to ionizing radiation poses a non-negligible threat to patients and clinical staff. Unfortunately, a reduction in the X-ray dose results in a lower signal-to-noise ratio, which may impair the quality of X-ray images. To ensure an acceptable image quality while keeping the X-ray dose as low as possible, it is common practice to use denoising techniques. However, at very low dose levels, the application of conventional denoising techniques may lead to undesirable artifacts or oversmoothing. On the other hand, supervised learning techniques have outperformed conventional techniques in producing suitable results, provided aligned pairs of associated high- and low-dose X-ray images are available. Unfortunately, it is neither acceptable nor possible to acquire such image pairs during a clinical intervention. To enable the use of learning-based methods for the denoising of X-ray images, we propose a novel strategy that involves the use of model-based simulations of low-dose X-ray images during the training phase. We utilize a data-driven normalization step that increases the robustness of the proposed approach to varying amounts of signal-dependent noise associated with different X-ray image acquisition protocols. A quantitative and qualitative analysis based on clinical and phantom data shows that the proposed strategy outperforms well-established conventional X-ray image denoising methods. It also indicates that the proposed approach allows for a significant dose reduction without sacrificing important image information.
CRIS-Team Scopus Importer:2019-12-10
Keywords: Deep learning; Low-dose X-ray image denoising; Noise simulation

Predicting Hearing Aid Fittings Based on Audiometric and Subject-Related Data: A Machine Learning Approach - Seifer, Ann-Kristin; Schinkel-Bielefeld, Nadja; Schröter, Hendrik; Escalante Banuelos, Alberto; Hoppe, Ulrich; Maier, Andreas (Virtual Conference on Computational Audiology (VCCA2020), 2020)
Hearing aids (HA) are configured to the wearer’s individual needs, which might vary greatly from user to user. Currently, it is common practice, that the initial HA gain settings are based on generic fitting formulas that link a user’s pure-tone hearing threshold to amplification characteristics. Subsequently, a time-consuming fine-tuning process follows, in which a hearing care professional (HCP) adjusts the HA settings to the user’s individual demands. An advanced, more personalized gain prescription procedure could support HCPs by reducing fine-tuning effort and facilitate over-the-counter HAs. We propose a machine learning based prediction for HA gain to minimize subsequent fine-tuning effort. The data-driven approach takes audiometic and personal variables into account, such as age, gender, and the user’s acoustical environment. A random forest regression model was trained on real-world HA fittings from the Connexx database (fitting software provided by Sivantos GmbH). Three months of data from Connexx version 9.1.0.364 were used. A data cleaning framework was implemented to extract a representative data set based on a list of machine learning and audiological criteria. These criteria include, for instance, using only ‘informative’ HCPs who perform fine-tuning for at least some patients. Furthermore, ‘informative’ HCPs are those who perform diagnostics beyond air conduction audiograms, use new technologies and special features. The resulting training data comprised 20,000 HA fittings and a 10-fold cross validation was used to train the random forest.
Keywords: Hearing Aids; Machine Learning

Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system - Hariharan, Sai Gokul; Kaethner, Christian; Strobel, Norbert; Kowarschik, Markus; DiNitto, Julie; Albarqouni, Shadi; Fahrig, Rebecca; Navab, Nassir (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose: 2D digital subtraction angiography (DSA) has become an important technique for interventional neuroradiology tasks, such as detection and subsequent treatment of aneurysms. In order to provide high-quality DSA images, usually undiluted contrast agent and a high X-ray dose are used. The iodinated contrast agent puts a burden on the patients’ kidneys while the use of high-dose X-rays expose both patients and medical staff to a considerable amount of radiation. Unfortunately, reducing either the X-ray dose or the contrast agent concentration usually results in a sacrifice of image quality. Materials and methods: To denoise a frame, the proposed spatiotemporal denoising method utilizes the low-rank nature of a spatially aligned temporal sequence where variation is introduced by the flow of contrast agent through a vessel tree of interest. That is, a constrained weighted rank-1 approximation of the stack comprising the frame to be denoised and its temporal neighbors is computed where the weights are used to prevent the contribution of non-similar pixels toward the low-rank approximation. The method has been evaluated using a vascular flow phantom emulating cranial arteries into which contrast agent can be manually injected (Vascular Simulations Replicator, Vascular Simulations, Stony Brook NY, USA). For the evaluation, image sequences acquired at different dose levels as well as different contrast agent concentrations have been used. Results: Qualitative and quantitative analyses have shown that with the proposed approach, the dose and the concentration of the contrast agent could both be reduced by about 75%, while maintaining the required image quality. Most importantly, it has been observed that the DSA images obtained using the proposed method have the closest resemblance to typical DSA images, i.e., they preserve the typical image characteristics best. Conclusion: Using the proposed denoising approach, it is possible to improve the image quality of low-dose DSA images. This improvement could enable both a reduction in contrast agent and radiation dose when acquiring DSA images, thereby benefiting patients as well as clinicians. Since the resulting images are free from artifacts and as the inherent characteristics of the images are also preserved, the proposed method seems to be well suited for clinical images as well.
CRIS-Team Scopus Importer:2019-05-14
Keywords: Digital subtraction angiography; Low-dose X-ray sequences; Spatiotemporal denoising; Weighted low-rank approximation

View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT – An Investigation of Potential Improvement - Gottschalk, Tristan; Maier, Andreas; Kordon, Florian Johannes; Kreher, Björn W. (Journal Machine Learning for Biomedical Imaging, 2021)
The positive outcome of a trauma intervention depends on an intraoperative evaluation of inserted metallic implants. Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR). The majority of these MAR methods require prior segmentation of the inserted metal objects. Therefore, typically a rather simple thresholding-based segmentation method in the reconstructed 3D volume is applied, despite some major disadvantages. With this publication, the potential of shifting the segmentation task to a learning-based, view-consistent 2D projection-based method on the downstream MAR's outcome is investigated. For segmenting the present metal, a rather simple learning-based 2D projection-wise segmentation network that is trained using real data acquired during cadaver studies, is examined. To overcome the disadvantages that come along with a 2D projection-wise segmentation, a Consistency Filter is proposed. The influence of the shifted segmentation domain is investigated by comparing the results of the standard fsMAR with a modified fsMAR version using the new segmentation masks. With a quantitative and qualitative evaluation on real cadaver data, the investigated approach showed an increased MAR performance and a high insensitivity against metal artifacts. For cases with metal outside the reconstruction's FoV or cases with vanishing metal, a significant reduction in artifacts could be shown. Thus, increases of up to roughly 3 dB w.r.t. the mean PSNR metric over all slices and up to 9 dB for single slices were achieved. The shown results reveal a beneficial influence of the shift to a 2D-based segmentation method on real data for downstream use with a MAR method, like the fsMAR. The nature of the method further suggests the same beneficial behavior for all (also recent data-driven) MAR methods, that for now comprise a 3D-volume-based segmentation step for subsequent inpainting.<br
Keywords: trauma intervention; cone-beam computed tomography; metal artifact reduction; metal segmentation