Epipolar consistency conditions for motion correction in weight-bearing imaging - Bier, Bastian; Aichert, André; Felsner, Lina; Unberath, Mathias; Levenstone, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin 2017 Algorithmen Systeme Anwendungen (Bildverarbeitung für die Medizin 2017), 2017)

Epipolar Consistency Guided Beam Hardening Reduction - ECC² - Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Huang, Xiaolin; Maier, Andreas (Proceedings of the 14th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.epipol_8

Epipolar Consistency in Fluoroscopy for Image-Based Tracking - Aichert, André; Wang, Jian; Schaffert, Roman; Dörfler, Arnd; Hornegger, Joachim; Maier, Andreas (Proceedings of the British Machine Vision Conference 2015, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.epipol_3

Epipolar Consistency in Transmission Imaging - Aichert, André (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Two X-ray projection images of a rigid object may have different points of view, yet redundant information can be identified in such images. Not unlike a checksum, these occur naturally in the data and are known as consistency conditions. Real acquisitions, however, result from a measurement process which is affected by inaccurate geometric calibration of the scanner, physical effects such as scatter and beam-hardening or in some applications patient motion. These effects can be observed as differences between theoretically redundant information in the data and, to some extent, can be corrected. Consistency conditions have been known for decades, yet only few practical applications have been demonstrated. State-of-the-art often assumes 2D parallel or fan-beam geometries in a perfect circle around the object. Extension of these findings to flat-panel detector geometry are not straight-forward. Meanwhile, however, practical applicability in flat-detector computed tomography has been demonstrated for a set of pairwise conditions known as epipolar consistency (EC). Their advantage is that they can be applied, in principle, to any two 2D projection images. This thesis first gives a brief introduction to data consistency conditions in two and three dimensions, providing a context for the main part of this work. The reader is then introduced to projective geometry of real two- and three-space and the geometry of X-ray imaging. This provides the mathematical tools for a derivation of the novel epipolar consistency conditions and demonstrates the connection to wellunderstood computer vision tasks. Three flavors of a metric to measure epipolar inconsistency in two images are suggested and a framework for motion compensation is introduced. Finally, the metric is used for motion correction in three applications of FDCT imaging. First, an unknown object under fluoroscopy is tracked relative to a small set of reference views. Second, respiratory and cardiac motion in rotational angiography is estimated. And third, the alignment of two computed tomography acquisitions is estimated from their raw data.<br
Keywords: Calibration Correction; Computed Tomography; Consistency Conditions; Epipolar Geometry; Motion Correction; X-ray

Epipolar Consistency in Transmission Imaging - Aichert, André; Berger, Martin; Wang, Jian; Maass, Nicole; Dörfler, Arnd; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Medical Imaging, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.epipol
Keywords: X-rays; motion estimation; geometry; stereo vision; X-ray tomography.

Epithelial Cell Detection in Endomicroscopy Images of the Vocal Folds - Mualla, Firas; Schöll, Simon; Bohr, Christopher; Neumann, Helmut; Maier, Andreas (Springer Proceedings in Physics 154, Proceedings of InterM, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.epithe

Erdschlusserkennung basierend auf Künstlichen Neuronalen Netzen - Kordowich, Georg; Lorz, Tobias; Jaworski, Michael; Klumpp, Philipp; Pérez Toro, Paula Andrea; Gaube, Sven; Kereit, Matthias; Böhme, Klaus; Nöth, Elmar; Jäger, Johann (12. VDE ETG-/FNN-Tutorial Schutz- und Leittechnik, 2022)
Erdschlüsse gehören zu den häufigsten Fehlern im Mittelspannungsnetz. Gleichzeitig ist insbesondere die Erkennung von hochohmigen Erdschlüssen eine der schwierigsten Aufgaben der Schutztechnik. Durch die zahlreichen Einflussparameter wie beispielsweise Fehlerursache, Bodenbeschaffenheit und Witterung auf Fehler- und Erdimpedanz können konventionelle Verfahren Erdschlüsse nicht in jedem Fall zuverlässig erkennen. In diesem Poster soll deshalb ein Konzept vorgestellt werden, das durch Verwendung von Künstlichen Neuronalen Netzen eine Ergänzung zu den etablierten Verfahren bilden soll. Durch ein Training sollen die Neuronalen Netze erlernen die komplexen Muster der Erdschlüsse zu erkennen und von anderen Schalthandlungen oder Fehlern abgrenzen können. Im Gegensatz zu konventionellen Schutzkonzepten, deren Erkennung meist von den Werten einzelner Parametern abhängt, sollen die Neuronalen Netze ein generisches Verständnis für die Muster entwickeln und so robuster und vielseitiger einsetzbar sein. Für die Generierung der Trainingsdaten ist ein möglichst akkurates Modell des entsprechenden Einsatzgebietes nötig. Mit diesem können automatisiert Simulationen durchgeführt, und Messdaten exportiert werden. Dabei werden verschiedene Fehlerorte und -zeitpunkte mit verschiedenen Parametern simuliert, um einen möglichst diversen Datensatz für ein robustes Training zu generieren. Die Messdaten aus den Simulationen werden anschließend für das Training der Neuronalen Netze verwendet. Um die Fehlererkennungsfähigkeit der Netze zu verifizieren, können diese anschließend mit Messdaten aus realen elektrischen Netzen validiert werde

ER-Force Extended Team Description Paper for Robocup 2015 - Eischer, Michael; Blank, Peter; Danzer, Alexander; Hauck, Adrian; Hoffmann, Markus; Reck, Benjamin; Eskofier, Björn (Robot Soccer World Cup XIX Proceedings, 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.erforc

ER-Force Team Description Paper for RoboCup 2009 - Blank, Peter; Bleier, Michael; Drexler, Sebastian; Kallwies, Jan; Kugler, Patrick; Lahmann, Dominik; Nordhus, Philipp; Riess, Christian; Swadzba, Thaddäus; Tully, Jan (RoboCup 2009: Robot Soccer World Cup XIII Proceedings, 2009)

ER-Force Team Description Paper for RoboCup 2010 - Blank, Peter; Bleier, Michael; Kallwies, Jan; Kugler, Patrick; Lahmann, Dominik; Nordhus, Philipp; Riess, Christian (RoboCup 2010: Robot Soccer World Cup XIV Proceedings, 2010)

ER-Force Team Description Paper for RoboCup 2011 - Bauer, Florian; Blank, Peter; Bleier, Michael; Dohrn, Hannes; Eischer, Michael; Friedrich, Stefan; Hauck, Adrian; Kallwies, Jan; Kugler, Patrick; Lahmann, Dominik; Nordhus, Philipp; Reck, Benjamin; et al. (RoboCup 2011: Robot Soccer World Cup XV Proceedings, 2011)
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD5.erforc

Erlangen-CLP: A Large Annotated Corpus of Speech from Children with Cleft Lip and Palate - Bocklet, Tobias; Maier, Andreas; Riedhammer, Korbinian Thomas; Eysholdt, Ulrich; Nöth, Elmar (2014)
In this paper we describe Erlangen-CLP, a large speech database of children with Cleft Lip and Palate. More than 800 German children with CLP (most of them between 4 and 18 years old) and 380 age matched control speakers spoke the semi-standardized PLAKSS test that consists of words with all German phonemes in different positions. So far 250 CLP speakers were manually transcribed, 120 of these were analyzed by a speech therapist and 27 of them by four additional therapists. The tharapists marked 6 different processes/criteria like pharyngeal backing and hypernasality which typically occur in speech of people with CLP. We present detailed statistics about the the marked processes and the inter-rater agreement.
Keywords: Cleft lip and palate; pathologic speech; Children's speech

Estimate, Compensate, Iterate: Joint Motion Estimation and Compensation in 4-D Cardiac C-arm Computed Tomography - Taubmann, Oliver; Lauritsch, Günter; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim (Medical Image Computing and Computer-Assisted Intervention MICCAI 2015, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.estima_9

Estimating the body portion of CT volumes by matching histograms of visual words - Feulner, Johannes; Zhou, S. Kevin; Seifert, Sascha; Hornegger, Joachim; Comaniciu, Dorin; Cavallaro, Alexander Josef (Medical Imaging 2009 - Image Processing, 2009)
Being able to automatically determine which portion of the human body is shown by a CT volume image offers various possibilities like automatic labeling of images or initializing subsequent image analysis algorithms. This paper presents a method that takes a CT volume as input and outputs the vertical body coordinates of its top and bottom slice in a normalized coordinate system whose origin and unit length are determined by anatomical landmarks. Each slice of a volume is described by a histogram of visual words: Feature vectors consisting of an intensity histogram and a SURF descriptor are first computed on a regular grid and then classified into the closest visual words to form a histogram. The vocabulary of visual words is a quantization of the feature space by offline clustering a large number of feature vectors from prototype volumes into visual words (or cluster centers) via the K-Means algorithm. For a set of prototype volumes whose body coordinates are known the slice descriptions are computed in advance. The body coordinates of a test volume are computed by a 1D rigid registration of the test volume with the prototype volumes in axial direction. The similarity of two slices is measured by comparing their histograms of visual words. Cross validation on a dataset of 44 volumes proved the robustness of the results. Even for test volumes of ca. 20cm height, the average error was 15.8mm. © 2009 Copyright SPIE - The International Society for Optical Engineering.

Estimating the Fundamental Matrix Without Point Correspondences WithApplication to Transmission Imaging - Würfl, Tobias; Aichert, André; Maass, Nicole; Dennerlein, Frank; Maier, Andreas (Proceedings of the IEEE International Conference on Computer Vision, 2019)
We present a general method to estimate the fundamental matrix from a pair of images under perspective projection without the need for image point correspondences. Our method is particularly well-suited for transmission imaging, where state-of-the-art feature detection and matching approaches generally do not perform well. Estimation of the fundamental matrix plays a central role in auto-calibration methods for reflection imaging. Such methods are currently not applicable to transmission imaging. Furthermore, our method extends an existing technique proposed for reflection imaging which potentially avoids the outlier-prone feature matching step from an orthographic projection model to a perspective model. Our method exploits the idea that under a linear attenuation model line integrals along corresponding epipolar lines are equal if we compute their derivatives in orthogonal direction to their common epipolar plane. We use the fundamental matrix to parametrize this equality. Our method estimates the matrix by formulating a non-convex optimization problem, minimizing an error in our measurement of this equality. We believe this technique will enable the application of the large body of work on image-based camera pose estimation to transmission imaging leading to more accurate and more general motion compensation and auto-calibration algorithms, particularly in medical X-ray and Computed Tomography imaging
Keywords: Structure from Motion; CT reconstruction

Estimation accuracy of ejection fraction in gated cardiac SPECT/CT imaging using iterative reconstruction with 3D resolution recovery in rapid acquisition protocols - Zeintl, Johannes; Hornegger, Joachim; Kuwert, Torsten; et al. (2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC, 2007)
Iterative reconstruction methods with 3D resolution recovery, and attenuation and scatter compensations are now common in clinical practice. Still, the reconstruction of ECG gated cardiac SPECT data is often done with fdtered backprojection (FBP) to save reconstruction time. The reconstruction times of the 2007 release of Flash3D (Siemens' OSEM reconstruction with 3D distance dependent resolution recovery and optional scatter and attenuation corrections) have been significantly improved and it is now possible to process an entire clinical gated cardiac data set in clinically acceptable times. It has been shown that Flash3D enables the use of rapid acquisition protocols, yet maintains clinical diagnostic ability. In this work we evaluated the estimation accuracy of the cardiac ejection fraction of gated myocardial SPECT/CT perfusion images, acquired with currently used clinical protocols, as well as rapid acquisition protocols. For this, we acquired ECG gated image data of a dynamic cardiac torso phantom manufactured by Data Spectrum. Images were reconstructed using FBP and the new Flash3D with and without scatter and attenuation corrections. We analyzed the reconstructed volumes of the dynamic phantom using our own quantitative analysis tool as well as 4D-MSPECT and compared the image-based estimated ejection fraction (EF) to the true ejection fraction delivered by the dynamic phantom. Results show high correlation (r >0.97) between conventional clinical protocols with FBP reconstruction and half time protocols using Flash3D except for 16 gates an 128×128 matrix (r=0.95). Best ejection fraction accuracy was found for 16 gates and 64x64 matrix with a mean deviation of 0.5±1.2% (full time FBP) 0.9±1.6% (half time Flash3D) from the true value. ©2007 IEEE.

Estimation of convolution kernels for X-ray scatter signal correction Schätzung von Faltungskernen zur Röntgen-Streusignalkorrektur - Luckner, Christoph; Maier, Andreas; Dennerlein, Frank (Informatik aktuell, 2014)
CRIS-Team Scopus Importer:2022-08-05

Estimation of femoral artery access location for anatomic deformation correction - Breininger, Katharina; Pfister, Marcus; Koutouzi, Giasemi; Kowarschik, Markus; Maier, Andreas (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.estima

Estimation of missing fan-beam projections using frequency consistency conditions - Pohlmann, Marcel; Berger, Martin; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Proceedings of the third international conference on image formation in x-ray computed tomography, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.estima
Keywords: GRK-1773

Estimation of the Knee Flexion-Extension Angle During Dynamic Sport Motions Using Body-worn Inertial Sensors - Jakob, Carolin; Kugler, Patrick; Hebenstreit, Felix; Reinfelder, Samuel; Jensen, Ulf; Schuldhaus, Dominik; Lochmann, Matthias; Eskofier, Björn (BodyNets 2013, 2013)

Estimation of the Source-Detector Alignment of Cone-Beam X-ray Systems using Collimator Edge Tracking - Luckner, Christoph; Mertelmeier, Thomas; Maier, Andreas; Ritschl, Ludwig (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.estima

Ethics and Good Practice in Computational Paralinguistics - Batliner, Anton; Hantke, Simone; Schuller, Bjorn (IEEE Transactions on Affective Computing, 2022)
With the advent of 'heavy Artificial Intelligence' - big data, deep learning, and ubiquitous use of the internet, ethical considerations are widely dealt with in public discussions and governmental bodies. Within Computational Paralinguistics with its manifold topics and possible applications (modelling of long-term, medium-term, and short-term traits and states such as personality, emotion, or speech pathology), we have not yet seen that many contributions. In this article, we try to set the scene by (1) giving a short overview of ethics and privacy, (2) describing the field of Computational Paralinguistics, its history and exemplary use cases, as well as (de-)anonymisation and peculiarities of speech and text data, and (3) proposing rules for good practice in the field, such as choosing the right performance measure, and accounting for representativity and interpretability.
CRIS-Team WoS Importer:2022-09-23

Evaluating the Feasibility of C-arm CT for Brain Perfusion Imaging: An in vitro Study - Ganguly, Arundhuti; Fieselmann, Andreas; Boese, Jan; Rohkohl, Christopher; Hornegger, Joachim; Fahrig, Rebecca (Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 2010)

Evaluating the Indoor Football Tracking Accuracy of a Radio-Based Real-Time Locating System - Seidl, Thomas; Witt, Nicolas; Poimann, Dino; Czyz, Titus; Franke, Norbert; Lochmann, Matthias; Völker, Matthias (Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS), 2016)
Nowadays, many tracking systems in football provide positional data of players but only a few systems provide reliable data of the ball. The tracking quality of many available systems suffers from high ball velocities up to 120km/h and from the occlusion of both the players and the ball. Radio-based local positioning systems use sensors integrated in the ball and located on the players’ back or near the shoes to avoid such issues. However, a qualitative evaluation of the tracking precision of radio-based systems is often not available and to the best of our knowledge there are actually no studies that deal with the positional accuracy of ball tracking. In this paper we close this gap and use the RedFIR radio-based locating system together with a ball shooting machine to repeatedly simulate realistic situations with different velocities in an indoor environment. We compare the derived positions from high speed camera footage to the positions provided by the RedFIR system by means of root mean square error (RMSE) and Bland-Altman analysis. We found an overall positional RMSE of 12.5cm for different ball velocities ranging from 45km/h to 61km/h. There was a systematic bias of 11.5cm between positions obtained by RedFIR and positions obtained by the high speed camera. Bland-Altman analysis showed 95% limits of agreement of [21.1cm, 1.9cm]. Taking the ball diameter of 22cm into account these results indicate that RedFIR is a valid tool for kinematic, tactical and time-motion analysis of ball movements in football.
Keywords: soccer; tracking; positioning; ball tracking; accuracy; radio-based

Evaluation and Assessment of Speech Intelligibility on Pathologic Voices Based Upon Acoustic Speaker Models - Bocklet, Tobias; Haderlein, Tino; Hönig, Florian Thomas; Rosanowski, Frank; Nöth, Elmar (Proceedings of the 3rd Advanced Voice Function Assessment International Workshop, 2009)

Evaluation of a Time-of-Flight based respiratory motion management system - Ulrich, Christian; Schaller, Christian; Penne, Jochen; Hornegger, Joachim (Bildverarbeitung für die Medizin, 2010)

Evaluation of Edge Orientation Histograms in Smile Detection - Timotius, Ivanna; Setyawan, Iwan (The 6th International Conference on Information Technology and Electrical Engineering (ICITEE 2014), 2014)
Smile detection received a enormous attention due to its famous application as a `smile shutter' in digital cameras. Edge Orientation Histograms (EOH) is one of the possible feature descriptors in a smile detector. This paper presents an evaluation of the use of Edge Orientation Histograms in a lip image based smile detector. The system built in this paper aims to discriminate lip images depicting a smile (including thin smile and broad smile) from lip images depicting non-smiling expressions. By dividing the lip images into 2 × 4 cells, and using 5° histogram bin size, we achieved 87.8% arithmetic means of accuracies. The experiments show that it is recommended not to use spatial binning that is too small. However, it is recommended to use fine orientation binning. Finally, it is recommended to use all orientation bins as features.
Keywords: smile detection; edge orientation histograms; estimated arithmetic means of accuracies

Evaluation of GOI Detectors in EGG Signals Assuming Different Models for the Pulse Length Variability - Ferrer Riesgo, Carlos A.; Rodriguez-Guillen, Reinier; Nöth, Elmar (PROGRESS IN ARTIFICIAL INTELLIGENCE AND PATTERN RECOGNITION, 2021)
CRIS-Team WoS Importer:2021-12-31

Evaluation of Interpolation Methods for Motion Compensated Tomographic Reconstruction for Cardiac Angiographic C-arm Data - Müller, Kerstin; Zheng, Yefeng; Lauritsch, Günter; Rohkohl, Christopher; Schwemmer, Chris; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim (Proceedings of the second international conference on image formation in x-ray computed tomography, 2012)
http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2012/Mueller12-EOI.pdf

Evaluation of interpolation methods for surface-based motion compensated tomographic reconstruction for cardiac angiographic C-arm data - Müller, Kerstin; Schwemmer, Chris; Hornegger, Joachim; Zheng, Yefeng; Wang, Yang; Lauritsch, Günter; Rohkohl, Christopher; Maier, Andreas; Schultz, Carl; Fahrig, Rebecca (Medical Physics, 2013)
Purpose: For interventional cardiac procedures, anatomical and functional information about the cardiac chambers is of major interest. With the technology of angiographic C-arm systems it is possible to reconstruct intraprocedural three-dimensional (3D) images from 2D rotational angiographic projection data (C-arm CT). However, 3D reconstruction of a dynamic object is a fundamental problem in C-arm CT reconstruction. The 2D projections are acquired over a scan time of several seconds, thus the projection data show different states of the heart. A standard FDK reconstruction algorithm would use all acquired data for a filtered backprojection and result in a motion-blurred image. In this approach, a motion compensated reconstruction algorithm requiring knowledge of the 3D heart motion is used. The motion is estimated from a previously presented 3D dynamic surface model. This dynamic surface model results in a sparse motion vector field (MVF) defined at control points. In order to perform a motion compensated reconstruction, a dense motion vector field is required. The dense MVF is generated by interpolation of the sparse MVF. Therefore, the influence of different motion interpolation methods on the reconstructed image quality is evaluated. Methods: Four different interpolation methods, thin-plate splines (TPS), Shepard's method, a smoothed weighting function, and a simple averaging, were evaluated. The reconstruction quality was measured on phantom data, a porcine model as well as on in vivo clinical data sets. As a quality index, the 2D overlap of the forward projected motion compensated reconstructed ventricle and the segmented 2D ventricle blood pool was quantitatively measured with the Dice similarity coefficient and the mean deviation between extracted ventricle contours. For the phantom data set, the normalized root mean square error (nRMSE) and the universal quality index (UQI) were also evaluated in 3D image space. Results: The quantitative evaluation of all experiments showed that TPS interpolation provided the best results. The quantitative results in the phantom experiments showed comparable nRMSE of ≈0.047 ± 0.004 for the TPS and Shepard's method. Only slightly inferior results for the smoothed weighting function and the linear approach were achieved. The UQI resulted in a value of ≈ 99% for all four interpolation methods. On clinical human data sets, the best results were clearly obtained with the TPS interpolation. The mean contour deviation between the TPS reconstruction and the standard FDK reconstruction improved in the three human cases by 1.52, 1.34, and 1.55 mm. The Dice coefficient showed less sensitivity with respect to variations in the ventricle boundary. Conclusions: In this work, the influence of different motion interpolation methods on left ventricle motion compensated tomographic reconstructions was investigated. The best quantitative reconstruction results of a phantom, a porcine, and human clinical data sets were achieved with the TPS approach. In general, the framework of motion estimation using a surface model and motion interpolation to a dense MVF provides the ability for tomographic reconstruction using a motion compensation technique. © 2013 American Association of Physicists in Medicine.

Evaluation of Spectrum Mismatching using Spectrum Binning Approach for Statistical Polychromatic Reconstruction in CT - Yang, Qiao; Wu, Meng; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.evalua_9

Evaluation of spectrum mismatching using spectrum binning for statistical polychromatic reconstruction in CT - Yang, Qiao; Wu, Meng; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Informatik aktuell, 2014)
In CT, the nonlinear attenuation characteristics of polychromatic X-rays cause beam hardening artifacts in the reconstructed images. Statistical algorithms can effectively correct beam hardening artifacts while providing the benefit of noise reduction. In practice, a big challenge for CT is the difficulty at acquiring accurate energy spectrum information, which hinders the efficiency of beam hardening correction approaches that require the spectrum as prior knowledge such as the statistical methods. In this paper, we used proposed energy spectrum binning approach for reducing prior knowledge from full spectrum to three energy bins to compare the results when applying parameters optimized for one spectrum to data measured using a different spectrum.
CRIS-Team Scopus Importer:2022-08-05

Evaluation of speech intelligibility for children with cleft lip and palate by means of automatic speech recognition - Schuster, Maria; Maier, Andreas; Haderlein, Tino; Nkenke, Emeka; Wohlleben, Ulrike; Rosanowski, Frank; Eysholdt, Ulrich; Nöth, Elmar (International Journal of Pediatric Otorhinolaryngology, 2006)

Evaluation of state-of-the-art hardware architectures for fast cone-beam CT reconstruction - Scherl, Holger; Kowarschik, Markus; Hofmann, Hannes; Keck, Benjamin; Hornegger, Joachim (Parallel Computing, 2012)
We present an evaluation of state-of-the-art computer hardware architectures for implementing the FDK method, which solves the 3-D image reconstruction task in cone-beam computed tomography (CT). The computational complexity of the FDK method prohibits its use for many clinical applications unless appropriate hardware acceleration is employed. Today's most powerful hardware architectures for high-performance computing applications are based on standard multi-core processors, off-the-shelf graphics boards, the Cell Broadband Engine Architecture (CBEA), or customized accelerator platforms (e.g., FPGA-based computer components). For each hardware platform under consideration, we describe a thoroughly optimized implementation of the most time-consuming parts of the FDK algorithm; the filtering step as well as the subsequent back-projection step. We further explain the required code transformations to parallelize the algorithm for the respective target architecture. We compare both the implementation complexity and the resulting performance of all architectures under consideration using the same two medical datasets which have been acquired using a standard C-arm device. Our optimized back-projection implementations achieve at least a speedup of 6.5 (CBEA, two processors), 22.0 (GPU, single board), and 35.8 (FPGA, 9 chips) compared to a standard workstation equipped with a quad-core processor. © 2011 Elsevier B.V. All rights reserved.

Evaluation of the Coarray Fortran Programming Model on the Example of a Lattice Boltzmann Code - Sembritzki, Klaus; Hager, Georg; Krammer, Bettina; Eitzinger, Jan; Wellein, Gerhard (PGAS12, 2012)
UnivIS-Import:2015-04-16:Pub.2012.tech.IMMD.phleir._evalu

Evaluation of the impact of view differentiation and backprojection weight in circle-plus-line cone-beam tomography - Hoppe, Stefan; Dennerlein, Frank; Lauritsch, Günter; Hornegger, Joachim; Noo, Frédéric (2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC, 2007)
Recently, we proposed a new view differentiation scheme for analytical cone-beam reconstruction formulae that demonstrated a strong robustness to changes in the data acquisition geometry and to coarse view sampling, unlike former differentiation schemes. We incorporated this new scheme into the Katsevich reconstruction formula for the circle-plus-line trajectory. We also implemented an alternative Katsevich formula for the same trajectory, where the view differentiation step was eliminated by using integration by parts. This work evaluates both formulae in terms of resolution performance, noise performance, visual image quality and computational effort. We also evaluate the impact of the z-sampling on the line segment. Experiments are presented from simulated cone-beam data. The experiments show that the view differentiation approach with the new view differentiation scheme achieves similar image quality as the integration-by-part approach while being at the same time much more efficient. © 2007 IEEE.

Evaluation of three analytical methods for reconstruction from cone-beam data on a short circular scan - Dennerlein, Frank; Noo, Frédéric; Hoppe, Stefan; Hornegger, Joachim; Lauritsch, Günter (2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC, 2007)
This article focuses on the problem of threedimensional image reconstruction from cone-beam data acquired along a partial circular scan (short-scan): We present a detailed comparative evaluation of three state-of-the-art analytical algorithms suggested to achieve image reconstruction in this short-scan geometry. Our evaluation involves quantitative studies, such as the estimation of the contrast-to-noise performance, of the achievable spatial resolution and of the cone-beam artifact behavior of these reconstruction algorithms. In addition to that, we also provide a visual assessment of image quality by evaluating reconstructions of the FORBILD head phantom and a disc phantom. The numerical results presented in this paper were obtained using computer-simulated cone-beam data, while focusing on non-truncated projection data and geometry parameters that are similar to those of real medical C-arm devices. © 2007 IEEE.

Evaluation of Tracheoesophageal Substitute Voices Using Prosodic Features - Haderlein, Tino; Nöth, Elmar; Schuster, Maria; Eysholdt, Ulrich; Rosanowski, Frank (Proc. Speech Prosody, 3rd International Conference, 2006)

EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control - Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Maier, Jennifer; Bergler, Christian; Kröger, Christine; Voigt, Jörn; Breininger, Katharina; Klopfleisch, Robert; Maier, Andreas (Scientific Reports, 2021)
In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.
CRIS-Team Scopus Importer:2021-04-09

Exact and efficient cone-beam reconstruction algorithm for a short-scan circle combined with various lines - Dennerlein, Frank; Katsevich, Alexander; Lauritsch, Günter; Hornegger, Joachim (Medical Imaging 2005 - Image Processing, 2005)
X-ray 3D rotational angiography based on C-arm systems has become a versatile and established tomographic imaging modality for high contrast objects in interventional environment. Improvements in data acquisition, e.g. by use of flat panel detectors, will enable C-arm systems to resolve even low-contrast details. However, further progress will be limited by the incompleteness of data acquisition on the conventional short-scan circular source trajectories. Cone artifacts, which result from that incompleteness, significantly degrade image quality by severe smearing and shading. To assure data completeness a combination of a partial circle with one or several line segments is investigated. A new and efficient reconstruction algorithm is deduced from a general inversion formula based on 3D Radon theory. The method is theoretically exact, possesses shift-invariant filtered backprojection (FBP) structure, and solves the long object problem. The algorithm is flexible in dealing with various circle and line configurations. The reconstruction method requires nothing more than the theoretically minimum length of scan trajectory. It consists of a conventional short-scan circle and a line segment approximately twice as long as the height of the region-of-interest. Geometrical deviations from the ideal source trajectory are considered in the implementation in order to handle data of real C-arm systems. Reconstruction results show excellent image quality free of cone artifacts. The proposed scan trajectory and reconstruction algorithm assure excellent image quality and allow low-contrast tomographic imaging with C-arm based cone-beam systems. The method can be implemented without any hardware modifications on systems commercially available today.

Exergaming on Mobile Devices - Blank, Peter; Eskofier, Björn (10. Symposium der dvs Sportinformatik, 2014)
UnivIS-Import:2016-06-01:Pub.2014.tech.IMMD.IMMD5.exerga_6

Exhaustive Graph Cut-based Vasculature Reconstruction - Unberath, Mathias; Achenbach, Stephan; Fahrig, Rebecca; Maier, Andreas (Proceedings of the ISBI, 2016)
We propose an exhaustive extension to graph cut-based coronary artery reconstruction from multiple views of a rotational angiography sequence. The reconstruction is formulated as an energy minimization problem that is solved using the a-expansion algorithm. We enforce reprojection-based data consistency and completeness conditions on the reconstructed centerline. The proposed strategy omits the need for selection and manual refinement of a reference view. A phantom study is used to assess the performance in 2D and 3D. The average reprojection error decreased from 1.32 ± 0.99mm to 0.54 ± 0.02 mm. Moreover, an increase in Dice score from 0.50 ± 0.04 mm to 0.59 ± 0.00 mm was observed, indicating superior volumetric reconstruction quality. The results suggest that the proposed extension removes the susceptibility to reference frame selection and manual interaction, while increasing reconstruction quality.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.exhaus_2
Keywords: C-arm CT; Cardiac Dynamics; Cardiac Imaging; Epipolar Geometry; Graph Cut; Symmetrization

Experiences with Commercial Telephone-based Dialogue Systems - Nöth, Elmar; Horndasch, Axel; Gallwitz, Florian; Haas, Jürgen (it - Information Technology, 2004)
In this publication experiences with commercial spoken dialogue systems are discussed and guidelines for achieving high usability are pointed out. Different from most commercially deployed IVR (Interactive Voice Response) systems, the systems discussed in this paper belong to a new generation of real mixed-initiative spoken dialogue systems, i. e.,the user may take the initiative, using full sentences, at virtually any point in time during the dialogue. We use three commercially deployed systems as example applications: the automated switchboard of a large German company, the movie information system operated by Germany’s largest multiplex cinema, and a football Bundesliga information system operated by a German media compan
Keywords: Spoken dialogue systems; mixed-initiative spoken dialogue systems; IVR (Interactive Voice Response) systems

Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy - Thies, Mareike; Wagner, Fabian; Gu, Mingxuan; Mei, Siyuan; Huang, Yixing; Pechmann, Sabrina; Aust, Oliver; Weidner, Daniela; Neag, Georgiana; Uderhardt, Stefan; Schett, Georg; Christiansen, Silke; et al. (Informatik aktuell, 2024)
Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are characteristic of osteoporosis. The complexity of this method stems from the requirement for high-quality 3D reconstructions of the murine bones. However, respiratory motion and muscle relaxation lead to inconsistencies in the projection data which result in artifacts in uncompensated reconstructions. Motion compensation using epipolar consistency conditions (ECC) has previously shown good performance in clinical CT settings. Here, we explore whether such algorithms are suitable for correcting motion-corrupted XRM data. Different rigid motion patterns are simulated and the quality of the motion-compensated reconstructions is assessed. The method is able to restore microscopic features for out-of-plane motion, but artifacts remain for more realistic motion patterns including all six degrees of freedom of rigid motion. Therefore, ECC is valuable for the initial alignment of the projection data followed by further fine-tuning of motion parameters using a reconstruction-based method.
CRIS-Team Scopus Importer:2024-03-29

Exploring the Capabilities and Limitations of Large Language Models for Radiation Oncology Decision Support - Putz, Florian; Haderlein, Marlen; Lettmaier, Sebastian; Semrau, Sabine; Fietkau, Rainer; Huang, Yixing (International Journal of Radiation Oncology Biology Physics, 2024)
Keywords: Large language model; GPT-4; Radiation Oncology

Exploring the space between smoothed and non-smooth total variation for 3D iterative CT reconstruction - Haase, Viktor; Stierstorfer, K.; Hahn, K.; Schöndube, H.; Maier, Andreas; Noo, F. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
Because total variation (TV) is non-differentiable, iterative reconstruction using a TV penalty comes with technical difficulties. To avoid these, it is popular to use a smooth approximation of TV instead, which is defined using a single parameter, herein called δ, with the convention that the approximation is better when δ is smaller. To our knowledge, it is not known how well image reconstruction with this approximation can approach a converged non-smooth TV-regularized result. In this work, we study this particular question in the context of X-ray computed tomography (CT). Experimental results are reported with real CT data of a head phantom and supplemented with a theoretical analysis. To address our question, we make proficient use of a generalized iterative soft-thresholding algorithm that allows us to handle TV and its smooth approximation in the same framework. Our results support the following conclusions. First, images reconstructed using the smooth approximation of TV appears to smoothly converge towards the TV result as δ tends to zero. Second, the value of δdoes not need to be overly small to obtain a result that is essentially equivalent to TV, implying that numerical instabilities can be avoided. Last, though it is smooth, the convergence with δ is not particularly fast, as the mean absolute pixel difference decreases only as √δ in our experiments. Altogether, we conclude that the approximation is a theoretically valid way to approximate the non-smooth TV penalty for CT, opening the door to safe utilization of a wide variety of optimization algorithms.
CRIS-Team Scopus Importer:2019-07-12
Keywords: Computed tomography; GISTA; Huber function; Iterative reconstruction; Total variation

Exposing Digital Image Forgeries by Illumination Color Classification - Carvalho, Tiago; Riess, Christian; Angelopoulou, Elli; Pedrini, Helio; Rocha, Anderson (IEEE Transactions on Information Forensics and Security, 2013)

Extended ellipse-line-ellipse trajectory for long-object cone-beam imaging with a mounted C-arm system - Yu, Zhicong; Lauritsch, Günter; Dennerlein, Frank; Mao, Yanfei; Hornegger, Joachim; Noo, Frederic (Physics in Medicine and Biology, 2016)
Recent reports show that three-dimensional cone-beam (CB) imaging with a floor-mounted (or ceiling-mounted) C-arm system has become a valuable tool in interventional radiology. Currently, a circular short scan is used for data acquisition, which inevitably yields CB artifacts and a short coverage in the direction of the patient table. To overcome these two limitations, a more sophisticated data acquisition geometry is needed. This geometry should be complete in terms of Tuy's condition and should allow continuous scanning, while being compatible with the mechanical constraints of mounted C-arm systems. Additionally, the geometry should allow accurate image reconstruction from truncated data. One way to ensure such a feature is to adopt a trajectory that provides full R-line coverage within the field-of-view (FOV). An R-line is any segment of line that connects two points on a source trajectory, and the R-line coverage is the set of points that belong to an R-line. In this work, we propose a novel geometry called the extended ellipse-line-ellipse (ELE) for long-object imaging with a mounted C-arm system. This trajectory is built from modules consisting of two elliptical arcs connected by a line. We demonstrate that the extended ELE can be configured in many ways so that full R-line coverage is guaranteed. Both tight and relaxed parametric settings are presented. All results are supported by extensive mathematical proofs provided in appendices. Our findings make the extended ELE trajectory attractive for axially-extended FOV imaging in interventional radiology.
Keywords: R-line; long-object; C-arm; cone-beam; tomography; ELE

Extended stereopsis evaluation of professional and amateur soccer players and subjects without soccer background - Paulus, Jan; Tong, Jie; Hornegger, Joachim; Schmidt, Michael; Eskofier, Björn; Michelson, Georg (Frontiers in Psychology, 2014)
Stereopsis is one of several visual depth cues. It has been evaluated for athletes of different types of sports in the past. However, most studies do not cover the full range of stereopsis performance. Therefore, we propose computer-supported stereopsis tests that provide an extended assessment and analysis of stereopsis performance including stereo acuity and response times. By providing stationary and moving stimuli they cover static and dynamic stereopsis, respectively. The proposed stereopsis tests were used to compare professional and amateur soccer players with subjects without soccer background. The soccer players could not perform significantly (p <= 0.05) superior than the subjects without soccer background. However, the soccer players showed significantly (p <= 0.01) superior choice reaction times for monocular stimuli. The results are in congruence with previous findings in literature.
Keywords: stereopsis; soccer; stereo acuity; depth perception; visual performance

Extended Volume Image Reconstruction Using the Ellipse-Line-Ellipse Trajectory for a C-arm System - Yu, Zhicong; Noo, Frédéric; Lauritsch, Günter; Hornegger, Joachim (Proceedings of The 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013)
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.extend

Extending the Tracking Range for Prospective Motion Correction using a Single In-bore Camera and the Self-Encoded Marker - Forman, Christoph; Aksoy, Murat; Straka, Matus; Hornegger, Joachim; Bammer, Roland (Proceedings of the ISMRM Workshop on Current Concepts of Motion Correction for MRI & MRS, 2010)

Face recognition using Kernel Fisher's Discriminant Analysis and nearest neighbor - Setyawan, Iwan; Putra, Abraham F.; Timotius, Ivanna; Febrianto, Andreas A. (2011 6th International Conference on Telecommunication Systems, Services, and Applications (TSSA), 2011)
Face recognition is a technology that is achieving more and more prominence today. This technology is now found in various applications such as automatic photo tagging and identification of criminal suspects. While the task of recognizing faces is easy for humans, the task of teaching a computer to do so is very challenging. This paper presents a face recognition system based on the Kernel Fishers Discriminant Analysis (KFDA) and Nearest Neighbor (NN) algorithms. We use the KFDA algorithm as a feature extractor and the NN algorithm as a classifier. Our current implementation of the system has achieved a recognition success rate of more than 83%.

Face recognition using support vector machines and generalized discriminant analysis - Timotius, Ivanna; Linasari, The Christiani; Setyawan, Iwan; Febrianto, Andreas A. (2011 6th International Conference on Telecommunication Systems, Services, and Applications (TSSA), 2011)
Face recognition by machines has various important applications in our daily life. However, the task to teach machine to recognize face images has been a very challenging task. This paper presents face recognition by combining Generalized Discriminant Analysis (GDA) as a feature extractor and Support Vector Machines (SVM) as a classifier. Our experiment showed that the performance of combining these two methods as a face image classifier is better than by only using SVM. The accuracy of combined method is above 85%.
Keywords: Face Recognition; Generalized Discriminant Analysis; Support Vector Machines

Factorization of the reconstruction problem in circular cone-beam tomography and its use for stability analysis - Dennerlein, Frank; Noo, Frédéric; Hornegger, Joachim; Lauritsch, Günter (2006 IEEE Nuclear Science Symposium, Medical Imaging Conference and 15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors, Special Focus Workshops, NSS/MIC/RTSD, 2007)
In this article, we propose a novel factorization of the circular cone-beam (CB) reconstruction problem into a set of independent 2D inversion problems. This factorization is established in the context of modern two-step Hilbert reconstruction methods by combining the ideas of an empirically derived CB inversion approach with a firm and exact theory. We were able to accurately discretize these 2D inversion problems, which allows a detailed investigation of CB reconstruction by using the Singular Value Decomposition and also allows efficient iterative reconstruction approaches. The introduced theory is applied for preliminary studies of the stability of circular CB tomography assuming a short object. We analyzed, how the radius of the circular scan affects the stability and investigated the effect of an additional linear scan onto the condition of the problem. Numerical results are presented for a disc phantom. © 2006 IEEE.

Factors influencing relative speech intelligibility in patients with oral squamous cell carcinoma: a prospective study using automatic, computer-based speech analysis. - Stelzle, Florian; Knipfer, Christian; Schuster, M.; Bocklet, Tobias; Nöth, Elmar; Adler, Werner; Schempf, L.; Vieler, Peter; Riemann, Max; Neukam, Friedrich Wilhelm; Nkenke, Emeka (International journal of oral and maxillofacial surgery, 2013)
Oral squamous cell carcinoma (OSCC) and its treatment impair speech intelligibility by alteration of the vocal tract. The aim of this study was to identify the factors of oral cancer treatment that influence speech intelligibility by means of an automatic, standardized speech-recognition system. The study group comprised 71 patients (mean age 59.89, range 35-82 years) with OSCC ranging from stage T1 to T4 (TNM staging). Tumours were located on the tongue (n=23), lower alveolar crest (n=27), and floor of the mouth (n=21). Reconstruction was conducted through local tissue plasty or microvascular transplants. Adjuvant radiotherapy was performed in 49 patients. Speech intelligibility was evaluated before, and at 3, 6, and 12 months after tumour resection, and compared to that of a healthy control group (n=40). Postoperatively, significant influences on speech intelligibility were tumour localization (P=0.010) and resection volume (P=0.019). Additionally, adjuvant radiotherapy (P=0.049) influenced intelligibility at 3 months after surgery. At 6 months after surgery, influences were resection volume (P=0.028) and adjuvant radiotherapy (P=0.034). The influence of tumour localization (P=0.001) and adjuvant radiotherapy (P=0.022) persisted after 12 months. Tumour localization, resection volume, and radiotherapy are crucial factors for speech intelligibility. Radiotherapy significantly impaired word recognition rate (WR) values with a progression of the impairment for up to 12 months after surgery.

Failure and Risk Analysis based on Maintenance Reports of Machines Components in Manufacturing Industry - Thamm, Aleksandra; Herz, Matthias; Wiedemann, Markus; Thamm, Florian; Maier, Andreas (XIII. International Conference on the Theory of Machines and Mechanisms - TMM2020, 2021)
Manufacturing engineering is concerned with the application of engineering practices in manufacturing processes and production methods. Continuous optimization of processes, tools as well as machinery and equipment is the key to producing quality products with optimal capital investment. Hereby, maintenance plays a crucial role and allows manufacturers to optimize their processes. To enable improvements in this field it is required to analyze and evaluate the necessities for actions on component level. In this work we want to present a detailed retrospective analysis of more than 4.000 maintenance reports and analyze causes for assembly failures depending on the associated components. Furthermore, we extend the analysis and investigate the failure dependency with respect to the days elapsed from the date of maintenance request to resolving the issue. It turned out, not all cases with high failure rates are associated with subsequent higher risks considering the respective urgency related to each failure case. Furthermore, we discovered especially hydraulic and pneumatic components have a significant high to critical risk, as the labour safety and other components are threatened by their failure. Moreover we determined, that only 10% of all actions were planned maintenance and thus we propose based on the results of the risks analysis, how planned and preventive maintenance should be extended to reduce costs in this regard.<br
Keywords: Manufacturing; Failure Analysis; Risk Analysis; Spare Parts; Maintenance; Preventive Maintenance

Fan-beam filtered-backprojection reconstruction without backprojection weight - Dennerlein, Frank; Noo, Frédéric; Hornegger, Joachim; Lauritsch, Günter (Physics in Medicine and Biology, 2007)
In this paper, we address the problem of two-dimensional image reconstruction from fan-beam data acquired along a full 2π scan. Conventional approaches that follow the filtered-backprojection (FBP) structure require a weighted backprojection with the weight depending on the point to be reconstructed and also on the source position; this weight appears only in the case of divergent beam geometries. Compared to reconstruction from parallel-beam data, the backprojection weight implies an increase in computational effort and is also thought to have some negative impacts on noise properties of the reconstructed images. We demonstrate here that direct FBP reconstruction from full-scan fan-beam data is possible with no backprojection weight. Using computer-simulated, realistic fan-beam data, we compared our novel FBP formula with no backprojection weight to the use of an FBP formula based on equal weighting of all data. Comparisons in terms of signal-to-noise ratio, spatial resolution and computational efficiency are presented. These studies show that the formula we suggest yields images with a reduced noise level, at almost identical spatial resolution. This effect increases quickly with the distance from the center of the field of view, from 0% at the center to 20% less noise at 20 cm, and to 40% less noise at 25 cm. Furthermore, the suggested method is computationally less demanding and reduces computation time with a gain that was found to vary between 12% and 43% on the computers used for evaluation. © 2007 IOP Publishing Ltd.

Fan-beam Projection Image Acquisition using MRI - Syben-Leisner, Christopher; Stimpel, Bernhard; Leghissa, Martino; Dörfler, Arnd; Maier, Andreas (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fanbea_5

Fan-to-Parallel Beam Conversion: Deriving Neural Network Architectures Using Precision Learning - Syben-Leisner, Christopher; Stimpel, Bernhard; Lommen, Jonathan; Würfl, Tobias; Dörfler, Arnd; Maier, Andreas (Informatik aktuell, 2019)
CRIS-Team Scopus Importer:2019-05-14

Fast Adaptive Regularization for Perfusion Parameter Computation - Manhart, Michael; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Bildverarbeitung für die Medizin, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.fastad_9

Fast adaptive regularization for perfusion parameter computation: Tuning the Tikhonov regularization parameter to the SNR by regression - Manhart, Michael; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Informatik aktuell, 2015)
Computation of perfusion parameters by deconvolution from contrast-enhanced time-resolved CT or MR perfusion data sets is an illconditioned problem. Thus, adequate regularization and determination of corresponding regularization parameters is required. We present a novel method for Tikhonov regularization for perfusion imaging to locally adapt parameters to the SNR level by using a regression function. In an numerical evaluation our simple approach provided similar or even superior results compared to methods applying computationally more demanding L-curve analysis.
CRIS-Team Scopus Importer:2022-08-05

Fast and Efficient Limited Data Hyperspectral Remote Sensing Image Classification via GMM-Based Synthetic Samples - Davari, Amirabbas; Özkan, Hasan Can; Maier, Andreas; Rieß, Christian (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019)

Fast and Robust 3-D MRI Brain Structure Segmentation - Wels, Michael; Zheng, Yefeng; Carneiro, Gustavo; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin (Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2009), Part II, LNCS 5762, 2009)
We present a novel method for the automatic detection and segmentation of (sub-)cortical gray matter structures in 3-D magnetic resonance images of the human brain. Essentially, the method is a top-down segmentation approach based on the recently introduced concept of Marginal Space Learning (MSL). We show that MSL naturally decomposes the parameter space of anatomy shapes along decreasing levels of geometrical abstraction into subspaces of increasing dimensionality by exploiting parameter invariance. At each level of abstraction, i.e., in each subspace, we build strong discriminative models from annotated training data, and use these models to narrow the range of possible solutions until a final shape can be inferred. Contextual information is introduced into the system by representing candidate shape parameters with high-dimensional vectors of 3-D generalized Haar features and steerable features derived from the observed volume intensities. Our system allows us to detect and segment 8 (sub-)cortical gray matter structures in T1-weighted 3-D MR brain scans from a variety of different scanners in on average 13.9 sec., which is faster than most of the approaches in the literature. In order to ensure comparability of the achieved results and to validate robustness, we evaluate our method on two publicly available gold standard databases consisting of several T1-weighted 3-D brain MR scans from different scanners and sites. The proposed method achieves an accuracy better than most state-of-the-art approaches using standardized distance and overlap metrics. © 2009 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.fastan

Fast and Robust Features for Prosodic Classification - Buckow, Jan-Constantin; Warnke, Volker; Huber, Richard; Batliner, Anton; Nöth, Elmar; Niemann, Heinrich (Proc. of the Workshop on TEXT, SPEECH and DIALOG (TSD'99), 1999)

Fast Automatic Coil Selection for Radial Stack-of-stars GRE Imaging - Grimm, Robert; Forman, Christoph; Hutter, Jana; Kiefer, Berthold; Hornegger, Joachim; Block, Kai Tobias (Proceedings of International Society for Magnetic Resonance in Medicine, 2013)

Fast Automatic Segmentation of the Esophagus from 3D CT data using a Probabilistic Model - Feulner, Johannes; Zhou, S. Kevin; Seifert, Sascha; Hornegger, Joachim; Comaniciu, Dorin; Cavallaro, Alexander Josef (Springer-verlag, 2009)
Automated segmentation of the esophagus in CT images is of high value to radiologists for oncological examinations of the mediastinum. It can serve as a guideline and prevent confusion with pathological tissue. However, segmentation is a challenging problem due to low contrast and versatile appearance of the esophagus. In this paper, a two step method is proposed which first finds the approximate shape using a "detect and connect" approach. A classifier is trained to find short segments of the esophagus which are approximated by an elliptical model. Recently developed techniques in discriminative learning and pruning of the search space enable a rapid detection of possible esophagus segments. Prior shape knowledge of the complete esophagus is modeled using a Markov chain framework, which allows efficient inferrence of the approximate shape from the detected candidate segments. In a refinement step, the surface of the detected shape is non-rigidly deformed to better fit the organ boundaries. In contrast to previously proposed methods, no user interaction is required. It was evaluated on 117 datasets and achieves a mean segmentation error of 2.28mm with less than 9s computation time. © 2009 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.fastau

Fast Cross-Calibration Between MR Scanner and Optical System for Prospective Motion Correction - Aksoy, Murat; Forman, Christoph; Straka, Matus; Holdsworth, Samantha; Skare, Stefan; Hornegger, Joachim; Bammer, Roland (Proceedings of the 18th Annual Meeting of ISMRM & ESMRMB, 2010)

Fast Dynamic Reconstruction Algorithm with Joint Bilateral Filtering for Perfusion C-arm CT - Manhart, Michael; Kowarschik, Markus; Fieselmann, Andreas; Deuerling-Zheng, Yu; Hornegger, Joachim (NSS/MIC 2012 Conference Record, 2012)
Tissue perfusion measurement using C-arm angiographysystems is a novel technique with potential high benefitfor catheter-guided treatment of stroke in the interventionalsuite. However, perfusion C-arm CT (PCCT) is challenging:the slow C-arm rotation speed only allows measuring samplesof contrast time attenuation curves (TACs) every 5 - 6 s ifreconstruction algorithms for static data are used. Furthermore,the peaks of the tissue TACs typically lie in a range of 5- 30 HU, thus perfusion imaging is very sensitive to noise.We present a dynamic, iterative reconstruction (DIR) approachto reconstruct TACs described by a weighted sum of linearspline functions. The optimization problem is solved using anappropriate initialization and a Landweber-based optimizationstrategy with a modified backprojection step. To reduce noise anovel regularization technique based on Joint Bilateral Filtering(JBF) is introduced. The algorithm is evaluated using simulationdata created with a dynamic cylindrical phantom, a realisticdigital brain phantom and real measured data from an animalstudy with a canine stroke model. Results indicate that the DIRalgorithm qualitatively and quantitatively improves reconstructedTACs and perfusion maps compared to classical Feldkamp(FDK) reconstruction. For the brain phantom study the Pearsoncorrelation (PC) of the reconstructed cerebral blood flow (CBF)maps to the ground truth increased from 0.82 (FDK) to 0.87(DIR). For the canine study the PC of the CBF maps to coregisteredperfusion CT maps increased from 0.61 (FDK) to 0.73(DIR).

Fast Epipolar Consistency without the Need for Pseudo Matrix Inverses - Preuhs, Alexander; Manhart, Michael; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.fastep

Fast GPU-based CT reconstruction using the Common Unified Device Architecture (CUDA) - Scherl, Holger; Keck, Benjamin; Kowarschik, Markus; Hornegger, Joachim (2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC, 2007)
The Common Unified Device Architecture (CUDA) is a fundamentally new programming approach making use of the unified shader design of the most current Graphics Processing Units (GPUs) from NVIDIA. The programming interface allows to implement an algorithm using standard C language and a few extensions without any knowledge about graphics programming using OpenGL, DirectX, and shading languages. We apply this revolutionary new technology to the FDK method, which solves the three-dimensional reconstruction task in cone-beam CT. The computational complexity of this algorithm prohibits its use for many medical applications without hardware acceleration. Today's GPUs with their high level of parallelism are cost-efficient processors for performing the FDK reconstruction according to medical requirements. In this paper, we present an innovative implementation of the most time-consuming parts of the FDK algorithm: filtering and back-projection. We also explain the required transformations to parallelize the algorithm for the CUDA architecture. Our implementation approach further allows to do an on-the-fly-reconstruction, which means that the reconstruction is completed right after the end of data acquisition. This enables us to present the reconstructed volume to the physician in real-time, immediately after the last projection image has been acquired by the scanning device. Finally, we compare our results to our highly optimized FDK implementation on the Cell Broadband Engine Architecture (CBEA), both with respect to reconstruction speed and implementation effort. © 2007 IEEE.

Fast Interpolation of Dense Motion Fields from Synthetic Phantoms - Maier, Andreas; Taubmann, Oliver; Wetzl, Jens; Wasza, Jakob; Forman, Christoph; Fischer, Peter; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin 2014, 2014)
Numerical phantoms are a common tool for the evaluation of registration and reconstruction algorithms. For applications concerning motion, dense deformation fields are of particular interest. Phantoms, however, are often described as surfaces and thus motion vectors can only be generated at these surfaces. In order to create dense motion fields, interpolation is required. A frequently used method for this purpose is the Parzen interpolator. However, with a high number of surface motion vectors and a high voxel count, its run time increases dramatically. In this paper, we investigate different methods to accelerate the creation of these motion fields using hierarchical sampling and the random ball cover. In the results, we show that a 643 volume can be sampled in less than one second with an error below 0.1 mm. Furthermore, we accelerate the interpolation of a 2563 dense deformation field to only 6.5 minutes using the proposed methods from days with previous methods.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.fastin_7
Keywords: interpolation; motion field; phantom

Fast iterative beam hardening correction based on frequency splitting in computed tomography - Yang, Qiao; Elter, Matthias; Schasiepen, Ingo; Maass, Nicole; Hornegger, Joachim (Medical Imaging 2013: Physics of Medical Imaging, 2013)
In computed tomography (CT), the nonlinear characteristics of beam hardening are due to the polychromaticity of X-rays, which severely degrade the CT image quality and diagnostic accuracy. The correction of beam hardening has been an active area since the early years of CT, and various techniques have been developed. State of- the-art works on multi-material beam hardening correction (BHC) are mainly based on segmenting datasets into different materials, and correcting the non-linearity iteratively. Those techniques are limited in correction effectiveness due to inaccurate segmentation. Furthermore, most of them are computationally intensive. In this study, we introduce a fast BHC scheme based on frequency splitting with the fact that beam hardening artifacts mainly contain in the low frequency components and take more iterations to be corrected in comparison with high frequency components. After low-pass filtering and correcting artifacts at down-sampled projections, an artifact reduced high resolution reconstruction will be obtained by incorporating the original edge information from the high frequency components. Evaluations in terms of correction accuracy and computational eficiency are performed using simulated and real CT datasets. In comparison to the BHC algorithm without frequency splitting, the proposed accelerated algorithm yields comparable results in correcting cupping and streak artifacts with tremendously reduced computational effort. We conclude that the presented framework can achieve a significant speedup while still obtaining excellent artifact reduction. This is a significant practical advantage for clinical as well as industrial CT. © 2013 SPIE.

Fast Pose Verification for High-Speed Radiation Therapy - Maier, Andreas; Westphal, Susanne; Geimer, Tobias; Maxim, Peter G.; King, Gregory; Schueler, Emil; Fahrig, Rebecca; Loo, Billy (Bildverarbeitung für die Medizin 2017: Algorithmen - Systeme - Anwendungen, 2017)
This paper discusses fast pose verification for radiation therapy on a new high-speed radiation therapy device. The PHASER system follows the idea of 4th generation CT imaging and allows fast 360° treatment using a steerable electron beam. Doing so, dose delivery is possible in few seconds. A major problem, however, is fast verification of the patient pose during treatment. In this paper, we suggest to use a projection-based approach that can be evaluated quickly and allows an accuracy below 1 mm as shown by our simulation study based on planning data from six 4D CT data sets.
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.fastpo

Fast Recursive Data-driven Multi-resolution Feature Extraction for Physiological Signal Classification - Hönig, Florian Thomas; Batliner, Anton; Nöth, Elmar (3rd Russian-Bavarian Conference on Biomedical Engineering, 2007)

Fast Sample Generation with Variational Bayesian for Limited Data Hyperspectral Image Classification - Davari, Amirabbas; Özkan, Hasan Can; Maier, Andreas; Riess, Christian (IEEE International Geoscience and Remote Sensing Symposium, 2018)

Fast simulation of x-ray projections of spline-based surfaces using an append buffer - Maier, Andreas; Hofmann, Hannes; Schwemmer, Chris; Hornegger, Joachim; Keil, Andreas; Fahrig, Rebecca (Physics in Medicine and Biology, 2012)
Many scientists in the field of x-ray imaging rely on the simulation of x-ray images. As the phantom models become more and more realistic, their projection requires high computational effort. Since x-ray images are based on transmission, many standard graphics acceleration algorithms cannot be applied to this task. However, if adapted properly, the simulation speed can be increased dramatically using state-of-the-art graphics hardware. A custom graphics pipeline that simulates transmission projections for tomographic reconstruction was implemented based on moving spline surface models. All steps from tessellation of the splines, projection onto the detector and drawing are implemented in OpenCL. We introduced a special append buffer for increased performance in order to store the intersections with the scene for every ray. Intersections are then sorted and resolved to materials. Lastly, an absorption model is evaluated to yield an absorption value for each projection pixel. Projection of a moving spline structure is fast and accurate. Projections of size 640×480 can be generated within 254ms. Reconstructions using the projections show errors below 1 HU with a sharp reconstruction kernel. Traditional GPU-based acceleration schemes are not suitable for our reconstruction task. Even in the absence of noise, they result in errors up to 9HU on average, although projection images appear to be correct under visual examination. Projections generated with our new method are suitable for the validation of novel CT reconstruction algorithms. For complex simulations, such as the evaluation of motion-compensated reconstruction algorithms, this kind of x-ray simulation will reduce the computation time dramatically. © 2012 Institute of Physics and Engineering in Medicine.

Fat/carbohydrate ratio but not energy density determines snack food intake and activates brain reward areas - Hoch, Tobias; Kreitz, Silke; Gaffling, Simone; Pischetsrieder, Monika; Heß, Andreas (Scientific Reports, 2015)
The snack food potato chips induces food intake in ad libitum fed rats, which is associated with modulation of the brain reward system and other circuits. Here, we show that food intake in satiated rats is triggered by an optimal fat/carbohydrate ratio. Like potato chips, an isocaloric fat/carbohydrate mixture influenced whole brain activity pattern of rats, affecting circuits related e.g. to reward/addiction, but the number of modulated areas and the extent of modulation was lower compared to the snack food itself.
UnivIS-Import:2015-07-08:Pub.2015.nat.dchph.llmch.fatcar

Fat Content and Fatty Acid Composition Quantification Using a 3D Stack-of-Radial Trajectory With Adaptive Gradient Calibration - Schneider, Manuel; Lugauer, Felix; Hoppe, Elisabeth; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.fatcon

FAUBOT: Purposeful Navigation of a Robot in a Simulated Environment - Danner, Daniel; Kaufhold, Caroline; Kranz, Peter; Müller, Rainer; Pfaller, Sven; Riess, Christian; Angelopoulou, Elli (28th IEEE International Real-Time Systems Symposium (RTSS 2007), 2007)

FAU IISAH Corpus - A German Speech Database Consisting of Human-Machine and Human-Human Interaction Acquired by Close-Talking and Far-Distance Microphones - Spiegl, Werner; Riedhammer, Korbinian Thomas; Steidl, Stefan; Nöth, Elmar (Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10), 2010)

FDK-type reconstruction algorithms for the reverse helical trajectory - Yu, Zhicong; Noo, Frédéric; Dennerlein, Frank; Lauritsch, Günter; Hornegger, Joachim (2011 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2011, 2012)
For emergency cases in the interventional room, 3D long-object cone-beam(CB) imaging using a C-arm system could save valuable time and reduce risks to the patient by avoiding the traditionally-used CT scan, and thus could potentially be a crucial tool for patient health. To accomplish such a task, the reverse helix is an attractive trajectory, however theoretically-exact and stable (TES) reconstruction with a reverse helix is challenging. Two TES solutions are available, but both of them come with a heavy computational load and some issues in terms of image quality. This work proposes three new approximate reconstruction algorithms for the reverse helix that are stable and efficient, and thus practical. Though not exact, reconstruction results obtained from all three methods appear acceptable. © 2011 IEEE.

Feasibility of intraoperative assessment of safe surgical margins during laryngectomy with confocal laser endomicroscopy: A pilot study - Sievert, Matti; Oetter, Nicolai; Aubreville, Marc; Stelzle, Florian; Maier, Andreas; Eckstein, Markus; Mantsopoulos, Konstantinos; Gostian, Antoniu Oreste; Mueller, Sarina K.; Koch, Michael; Agaimy, Abbas; Iro, Heinrich; et al. (Auris Nasus Larynx, 2021)
Objective: This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with Confocal Laser Endomicroscopy (CLE) during planned partial or total laryngectomy. Methods: Eight patients with confirmed larynx squamous cell carcinoma (SCC) and planned partial or total laryngectomy were included in this study in March 2020. Two head and neck surgeons and one pathologist were asked to classify carcinoma or healthy epithelium in a sample of 94 representative sequences (5.640 images), blinded to the histological results (H&E staining). Results: Healthy mucosa areas showed epithelium with cells of uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of SCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 80.1%, 72.3%, 87.9%, 85.7%, and 76.1%, respectively. A distinct transition between healthy appearing tissue and suspicious lesions could also be detected. Conclusion: CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the larynx for evaluation and demarcation of cancer. If validated in further studies, CLE could eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin.
CRIS-Team Scopus Importer:2021-01-29
Keywords: Confocal laser endomicroscopy; Laryngeal cancer; Safe surgical margins

Feasibility of Motion Compensation using Inertial Measurement in C-arm CT - Maier, Jennifer; Aichert, André; Mehringer, Wolfgang; Bier, Bastian; Eskofier, Björn; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Bonaretti, Serena; Maier, Andreas (Conference Record of the 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2018)

Feasibility Study: Free-Breathing 3-D CINE Imaging with Respiratory Gating Based on Pilot Tone Navigation - Wetzl, Jens; Schroeder, Lea; Forman, Christoph; Lugauer, Felix; Rehner, Robert; Fenchel, Matthias; Maier, Andreas; Hornegger, Joachim; Speier, Peter (Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.feasib

Feature Constrained Non-rigid Image Registration - Han, Jingfeng; Hornegger, Joachim; Kuwert, Torsten; Bautz, Werner; Römer, Wolfgang (Frontiers in Simulation, 2005)

Feature Loss After Denoising of SPECT Projection Data using a U-Net - Reymann, Maximilian; Massanes, Francesc; Ritt, Philipp; Cachovan, Michal; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas (Feature Loss After Denoising of SPECT Projection Data using a U-Net, 2020)
It has been long known that denoising of SPECT projection data from multi channel collimators harbors the danger that features are filtered away as distinction between noise and signal in the 2D projection image does not consider the tomographic information (see Vija, Yahil et al.). We investigate if an approach using Neural Networks can overcome the past issues, by training a U-Net on Monte Carlo Simulations of the XCAT phantom. Although error metrics indicate improvement of signal statistics, we observe a loss of image features after the reconstruction. We show that when using OSEM reconstruction without denoising in projection domain, these features can be preserved.<br
Keywords: SPECT; Denoising; Monte Carlo Simulation; XCAT Phantom; SIMIND; U-Net

Feature representation of pathophysiology of parkinsonian dysarthria - Rueda, Alice; Vasquez Correa, Juan; Rios-Urrego, Cristian David; Orozco-Arroyave, Juan Rafael; Krishnan, Sridhar; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019)
This paper focuses on selecting features that can best represent the pathophysiology of Parkinson's disease (PD) dysarthria. PD dysarthria has often been the subject of feature selection and classification experiments, but rarely have the selected features been attempted to be matched to the pathophysiology of PD dysarthria. PD dysarthria manifests through changes in control of a person's speech production muscles and affects respiration, articulation, resonance, and laryngeal properties, resulting in speech characteristics such as short phrases separated by pauses, reduced speed for non-repetitive syllables or supernormal speed of repetitive syllables, reduced resonance, irregular vowel generation, etc. Articulation, phonation, diadochokinesis (DDK) rhythm, and Empirical Mode Decomposition (EMD) features were extracted from the DDK and sustained /a/ recordings of the Spanish GITA Corpus. These recordings were captured from 50 healthy (HC) and 50 PD subjects. A two-stage filter-wrapper feature selection process was applied to reduce the number of features from 3,534 to 15. These 15 features mainly represent the instability of the voice and rhythm. SVM, Random Forest and Naive Bayes were used to test the discriminative power of the selected features. The results showed that these sustained /a/ and /pa-ta-ka/ stability features could successfully discriminate PD from HC with 70% accuracy.
Created from Fastlane, Scopus look-up
Keywords: DDK; Dimension reduction; Discriminative feature selection; Feature analysis; Parkinson's speech

Feature space visualization with spatial similarity maps for pathological speech data - Klumpp, Philipp; Vasquez Correa, Juan; Haderlein, Tino; Nöth, Elmar (Proc. Interspeech 2019, 2019)
The feature vectors of a data set encode information about relations between speaker groups, clusters and outliers. Based on the assumption that these relations are conserved within the spatial properties of feature vectors, we introduce similarity maps to visualize consistencies and deviations in magnitude and orientation between two feature vectors. We also present an iterative approach to find subspaces of a high-dimensional feature space that encode information about predefined speaker clusters. The methods were evaluated with two different data sets, one from chronically hoarse speakers and a second one from Parkinson’s Disease patients and a healthy control group. The results showed that similarity maps provide a decent visualization of speaker groups and the spatial properties of their respective feature vectors. With the iterative optimization, it was possible to find features that show pronounced spatial differences between predefined clusters.<br
Created from Fastlane, Scopus look-up
Keywords: Feature analysis; Feature space reduction; Feature space visualization

Feedback-Training mit tragbaren Sensor-Netzwerken - Kugler, Patrick; Jensen, Ulf; Eskofier, Björn; Hornegger, Joachim (INFORMATIK 2010 Service Science - Neue Perspektiven für die Informatik - Band 1, 2010)
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD5.feedba
Keywords: feedback training; inertial sensors; digital sports

Feedback training with portable sensor networks - Kugler, Patrick; Jensen, Ulf; Eskofier, Björn; Hornegger, Joachim (40th Jahrestagung der Gesellschaft fur Informatik e.V. (GI): Service Science - Neue Perspektiven fur die Informatik, INFORMATIK 2010, 2010)

Few-shot Unsupervised Domain Adaptation for Multi-modal Cardiac Image Segmentation - Gu, Mingxuan; Vesal, Sulaiman; Kosti, Ronak Vijaypal; Maier, Andreas (Informatik aktuell, 2022)
CRIS-Team Scopus Importer:2022-05-06

Fiducial-Free 2D/3D Registration for Robot-Assisted Femoroplasty - Gao, Cong; Farvardin, Amirhossein; Grupp, Robert; Bakhtiarinejad, Mahsan; Ma, Liuhong; Thies, Mareike; Unberath, Mathias; Taylor, Russell; Armand, Mehran (IEEE Transactions on Medical Robotics and Bionics, 2020)
Femoroplasty is a proposed alternative therapeutic method for preventing osteoporotic hip fractures in the elderly. Previously developed navigation system for femoroplasty required the attachment of an external X-ray fiducial to the femur. We propose a fiducial-free 2D/3D registration pipeline using fluoroscopic images for robot-assisted femoroplasty. Intraoperative fluoroscopic images are taken from multiple views to perform registration of the femur and drilling/injection device. The proposed method was tested through comprehensive simulation and cadaveric studies. Performance was evaluated on the registration error of the femur and the drilling/injection device. In simulations, the proposed approach achieved a mean accuracy of 1.26±0.74 mm for the relative planned injection entry point; 0.63 ± 0.21◦ and 0.17 ± 0.19◦ for the femur injection path direction and device guide direction, respectively. In the cadaver studies, a mean error of 2.64±1.10 mm was achieved between the planned entry point and the device guide tip. The biomechanical analysis showed that even with a 4 mm translational deviation from the optimal injection path, the yield load prior to fracture increased by 40.7%. This result suggests that the fiducial-less 2D/3D registration is sufficiently accurate to guide robot assisted femoroplasty.<br
Keywords: 2D/3D registration; femur registration; X-ray navigation; robot-assisted femoroplasty

Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery - Fan, Fuxin; Kreher, Björn W.; Keil, Holger; Maier, Andreas; Huang, Yixing (Medical Physics, 2022)
Purpose: Fiducial markers are commonly used in navigation assisted minimally invasive spine surgery and they help transfer image coordinates into real world coordinates. In practice, these markers might be located outside the field-of-view (FOV) of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries, due to the limited detector sizes. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. Methods: In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method). For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific learning strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. Results: The two methods are evaluated on simulated and real data. The direct method achieves 100% detection rates with maximal 2-pixel difference on simulated data with normal truncation and simulated data with severe noise, but fails to detect all the markers in extremely severe truncation case. The recovery method detects all the markers successfully with maximal 1 pixel difference on all simulated data sets. For real data, both methods achieve 100% marker detection rates with mean registration error below 0.2 mm. Conclusions: Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with task-specific learning has high robustness and generalizability on various data sets. The task-specific learning is able to reconstruct structures of interest outside the FOV from severely truncated data, which has the potential to empower CBCT systems with new applications.<br
Keywords: minimally invasive spine surgery; marker detection; marker recovery; task-specific learning; truncation correction

Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks - Aubreville, Marc; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas (Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, 2019)
Most tumor grading systems for human as for veterinary histopathology are based upon the absolute count of mitotic figures in a certain reference area of a histology slide. Since time for prognostication is limited in a diagnostic setting, the pathologist will oftentimes almost arbitrarily choose a certain field of interest assumed to have the highest mitotic activity. However, as mitotic figures are commonly very sparse on the slide and often have a patchy distribution, this poses a sampling problem which is known to be able to influence the tumor prognostication. On the other hand, automatic detection of mitotic figures can’t yet be considered reliable enough for clinical application. In order to aid the work of the human expert and at the same time reduce variance in tumor grading, it is beneficial to assess the whole slide image (WSI) for the highest mitotic activity and use this as a reference region for human counting. For this task, we compare two methods for region of interest proposal, both based on convolutional neural networks (CNN). For both approaches, the CNN performs a segmentation of the WSI to assess mitotic activity. The first method performs a segmentation of mitotic cells at the original image resolution, while the second approach performs a segmentation operation at a significantly reduced resolution, cutting down on processing complexity. We evaluate the approach using a dataset of 32 completely annotated whole slide images of canine mast cell tumors, where 22 were used for training of the network and 10 for test. Our results indicate that, while the overall correlation to the ground truth mitotic activity is considerably higher (0.936 vs. 0.829) for the approach based upon the fine resolution network, the field of interest choices are only marginally better. Both approaches propose fields of interest that contain a mitotic count in the upper quartile of respective slides.<br
Keywords: Mitotic Figure; Cell Segmentation; Digital Histopathology; Tumor Grading

Field of View Extension in Computed Tomography Using Deep Learning Prior - Huang, Yixing; Gao, Lei; Preuhs, Alexander; Maier, Andreas (Bildverarbeitung für die Medizin: Algorithmen – Systeme – Anwendungen, 2020)
In computed tomography (CT), data truncation is a common problem. Images reconstructed by the standard filtered back-projection algorithm from truncated data suffer from cupping artifacts inside the field-of-view (FOV), while anatomical structures are severely distorted or missing outside the FOV. Deep learning, particularly the U-Net, has been applied to extend the FOV as a post-processing method. Since image-to-image prediction neglects the data fidelity to measured projection data, incorrect structures, even inside the FOV, might be reconstructed by such an approach. Therefore, generating reconstructed images directly from a post-processing neural network is inadequate. In this work, we propose a data consistent reconstruction method, which utilizes deep learning reconstruction as prior for extrapolating truncated projections and a conventional iterative reconstruction to constrain the reconstruction consistent to measured raw data. Its efficacy is demonstrated in our study, achieving small average root-mean-square error of 24 HU inside the FOV and a high structure similarity index of 0.993 for the whole body area on a test patient's CT data.
Keywords: Deep learning; data truncation; FOV extension; data consistency

Filled Pauses in Spontaneous Speech - Batliner, Anton; Kießling, Andreas; Burger, Stefanie; Nöth, Elmar (Proc. of the 13th Intl. Congress of Phonetic Sciences, 1995)

FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales - Bergler, Christian; Gebhard, Alexander; Towers, Jared R.; Butyrev, Leonid; Sutton, Gary J.; Shaw, Tasli J. H.; Maier, Andreas; Nöth, Elmar (Scientific Reports, 2021)
Biometric identification techniques such as photo-identification require an array of unique natural markings to identify individuals. From 1975 to present, Bigg’s killer whales have been photo-identified along the west coast of North America, resulting in one of the largest and longest-running cetacean photo-identification datasets. However, data maintenance and analysis are extremely time and resource consuming. This study transfers the procedure of killer whale image identification into a fully automated, multi-stage, deep learning framework, entitled FIN-PRINT. It is composed of multiple sequentially ordered sub-components. FIN-PRINT is trained and evaluated on a dataset collected over an 8-year period (2011–2018) in the coastal waters off western North America, including 121,000 human-annotated identification images of Bigg’s killer whales. At first, object detection is performed to identify unique killer whale markings, resulting in 94.4% recall, 94.1% precision, and 93.4% mean-average-precision (mAP). Second, all previously identified natural killer whale markings are extracted. The third step introduces a data enhancement mechanism by filtering between valid and invalid markings from previous processing levels, achieving 92.8% recall, 97.5%, precision, and 95.2% accuracy. The fourth and final step involves multi-class individual recognition. When evaluated on the network test set, it achieved an accuracy of 92.5% with 97.2% top-3 unweighted accuracy (TUA) for the 100 most commonly photo-identified killer whales. Additionally, the method achieved an accuracy of 84.5% and a TUA of 92.9% when applied to the entire 2018 image collection of the 100 most common killer whales. The source code of FIN-PRINT can be adapted to other species and will be publicly available.<br

First steps towards initial registration for electrophysiology procedures - Brost, Alexander; Bourier, Felix; Yatziv, Liron; Koch, Martin; Hornegger, Joachim; Strobel, Norbert; Kurzidim, Klaus (Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 2011)
Atrial fibrillation is the most common heart arrhythmia and a leading cause of stroke. The treatment option of choice is radio-frequency catheter ablation, which is performed in electrophysiology labs using C-Arm X-ray systems for navigation and guidance. The goal is to electrically isolate the pulmonary vein-left atrial junction thereby rendering myocardial fibers responsible for induction and maintenance of AF inactive. The use of overlay images for fluoroscopic guidance may improve the quality of the ablation procedure, and can reduce procedure time. Overlay images, acquired using CT, MRI, or C-arm CT, can add soft-tissue information, otherwise not visible under X-ray. MRI can be used to image a wide variety of anatomical details without ionizing radiation. In this paper, we present a method to register a 3-D MRI volume to 2-D biplane X-ray images using the coronary sinus. Current approaches require registration of the overlay images to the fluoroscopic images to be performed after the trans-septal puncture, when contast agent can be administered. We present a new approach for registration to align overlay images before the trans-septal puncture. To this end, we manually extract the coronary sinus from pre-operative MRI and register it to a multi-electorde catheter placed in the coronary sinus. © 2011 SPIE.

Flat panel detector angiographic CT for stent-assisted coil embolization of broad-based cerebral aneurysms - Richter, Gregor; Engelhorn, Tobias; Struffert, Tobias; Dölken, Marc; Ganslandt, Oliver; Hornegger, Joachim; Kalender, Willi Alfred; Dörfler, Arnd (American Journal of Neuroradiology, 2007)
BACKGROUND AND PURPOSE: The purpose of this work was to evaluate angiographic CT (ACT) in the combined application of a self-expanding neurovascular stent and detachable platinum coils in the management of broad-based and fusiform intracranial aneurysms. MATERIALS AND METHODS: Eleven patients harboring wide-necked intracranial aneurysms were treated with a flexible self-expanding neurovascular stent and subsequent aneurysm embolization with platinum microcoils. ACT was performed after the interventional procedure to analyze stent position and the relationship of coils to the stent. Postprocessing included multiplanar reconstructions (MPRs) and maximum intensity projections (MIPs). ACT volume datasets were postprocessed for soft tissue visualization. RESULTS: Accurate stent placement with subsequent coil occlusion of the aneurysms was feasible in all of the patients. Similar to nonsubtracted digital subtraction angiography (DSA) images, radiopaque platinum stent markers showed excellent visibility in ACT as well. The stent struts themselves, hardly visible in nonsubtracted DSA, were visible in MPRs and MIPs of ACT in all of the patients. In aneurysms larger than 10 mm in diameter, accurate stent assessment at the level of the coils was limited due to beam hardening artifacts. Postprocedural ACT in all of the patients did not reveal any evidence of procedure-related intracranial hemorrhage. CONCLUSION: ACT provides cross-sectional, 3D visualization of endovascular stents otherwise hardly visible with plain fluoroscopy. ACT enables us to accurately determine stent position, which may be helpful in complex stent-assisted aneurysm coiling procedures. However, in aneurysms larger than 10 mm in diameter, beam hardening artifacts caused by the endoaneurysmal coil package impair visibility of the stent. Further data are necessary to evaluate the usefulness of ACT in stent-assisted aneurysm coiling.

Flexible Log File Parsing Using Hidden Markov Models - Kuhnert, Nadine; Maier, Andreas (2019)
We aim to model unknown file processing. 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 by focusing only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Second, 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 processing. 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, applying the model and the resulting parsing rule to a different system with slightly different log file patterns, we achieve an accuracy over 99%.<br
Keywords: Hidden Markov Models; Parameter Extraction; Parsing; Text Mining; Information Retrieval

FlexParser—The adaptive log file parser for continuous results in a changing world - Kuhnert, Nadine; Maier, Andreas (Journal of Software: Evolution and Process, 2022)
Any modern system writes events into files, called log files. Those contain crucial information which are subject to various analyses. Examples range from cybersecurity, intrusion detection over usage analyses to trouble shooting. Before data analysis is possible, desired information needs to be extracted first out of the semi-structured log messages. State-of-the-art event parsing often assumes static log events. However, any modern system is updated consistently and with updates also log file structures can change. We call those changes “mutation” and study parsing performance for different mutation cases. Latest research discovers mutations using anomaly detection post mortem, however, does not cover actual continuous parsing. Thus, we propose a novel and flexible parser, called FlexParser, which can extract desired values despite gradual changes in the log messages. It implies basic text preprocessing followed by a supervised Deep Learning method. We train a stateful LSTM on parsing one event per data set. Statefulness enforces the model to learn log message structures across several examples. Our model was tested on seven different, publicly available log file data sets and various kinds of mutations. Exhibiting an average F1-Score of 0.98, it outperforms other Deep Learning methods as well as state-of-the-art unsupervised parsers<br
Keywords: deep learning; flexible parsing; log parser; LSTM; system log

ForestNet – Automatic Design of Sparse Multilayer Perceptron Network Architectures Using Ensembles of Randomized Trees - Rodriguez Salas, Dalia; Ravikumar, Nishant; Seuret, Mathias; Maier, Andreas (Pattern Recognition, 2020)
In this paper, we introduce a mechanism for designing the architecture of a Sparse Multi-Layer Perceptron network, for classification, called ForestNet. Networks built using our approach are capable of handling high-dimensional data and learning representations of both visual and non-visual data. The proposed approach first builds an ensemble of randomized trees in order to gather information on the hierarchy of features and their separability among the classes. Subsequently, such information is used to design the architecture of a sparse network, for a specific data set and application. The number of neurons is automatically adapted to the dataset. The proposed approach was evaluated using two non-visual and two visual datasets. For each dataset, 4 ensembles of randomized trees with different sizes were built. In turn, per ensemble, a sparse network architecture was designed using our approach and a fully connected network with same architecture was also constructed. The sparse networks defined using our approach consistently outperformed their respective tree ensembles, achieving statistically significant improvements in classification accuracy. While we do not beat state-of-art results with our network size and the lack of data augmentation techniques, our method exhibits very promising results, as the sparse networks performed similarly to their fully connected counterparts with a reduction of more than 98% of connections in the visual tasks.<br
Keywords: Multilayer Perceptron; Random Forest; Randomized Trees; Sparse Neural Networks; Network Architecture

Formbasierte Segmentierung des Bronchialbaumes - Jäger, Florian; Hornegger, Joachim; Hahn, Eckhart Georg (Bildverarbeitung für die Medizin 2005, 2005)

Fourier-based Reduction of Directed Streak Arifacts in Cone-Beam CT - Gawellek, Julia; Bier, Bastian; Gold, Garry; Maier, Andreas (Bildverarbeitung für die Medizin 2018, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.fourie

Fourier Consistency-Based Motion Estimation in Rotational Angiography - Unberath, Mathias; Berger, Martin; Aichert, André; Maier, Andreas (Proc. Bildverarbeitung für die Medizin (BVM) 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fourie

Frangi-Net: A Neural Network Approach to Vessel Segmentation - Fu, Weilin; Breininger, Katharina; Schaffert, Roman; Ravikumar, Nishant; Würfl, Tobias; Fujimoto, James G.; Moult, Eric M.; Maier, Andreas (BildVerarbeitung für die Medizin (BVM) 2018, 2018)
In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network (“Frangi-Net”), and illustrate that the Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show that, as a neural network, Frangi-Net is trainable. We evaluate the proposed method on a set of 45 high resolution fundus images. After fine-tuning, we observe both qualitative and quantitative improvements in the segmentation quality compared to the original Frangi measure, with an increase up to 17% in F1 scor
Keywords: Vessel segmentation; Precision learning; Frangi-Net; Fundus imaging

Free-breathing fat and R2∗ quantification in the liver using a stack-of-stars multi-echo acquisition with respiratory-resolved model-based reconstruction - Schneider, Manuel; Benkert, Thomas; Solomon, Eddy; Nickel, Dominik; Fenchel, Matthias; Kiefer, Berthold; Maier, Andreas; Chandarana, Hersh; Block, Kai Tobias (Magnetic Resonance in Medicine, 2020)
Purpose: To develop a free-breathing hepatic fat and (Formula presented.) quantification method by extending a previously described stack-of-stars model-based fat-water separation technique with additional modeling of the transverse relaxation rate (Formula presented.) . Methods: The proposed technique combines motion-robust radial sampling using a stack-of-stars bipolar multi-echo 3D GRE acquisition with iterative model-based fat-water separation. Parallel-Imaging and Compressed-Sensing principles are incorporated through modeling of the coil-sensitivity profiles and enforcement of total-variation (TV) sparsity on estimated water, fat, and (Formula presented.) parameter maps. Water and fat signals are used to estimate the confounder-corrected proton-density fat fraction (PDFF). Two strategies for handling respiratory motion are described: motion-averaged and motion-resolved reconstruction. Both techniques were evaluated in patients (n = 14) undergoing a hepatobiliary research protocol at 3T. PDFF and (Formula presented.) parameter maps were compared to a breath-holding Cartesian reference approach. Results: Linear regression analyses demonstrated strong (r > 0.96) and significant (P ≪.01) correlations between radial and Cartesian PDFF measurements for both the motion-averaged reconstruction (slope: 0.90; intercept: 0.07%) and the motion-resolved reconstruction (slope: 0.90; intercept: 0.11%). The motion-averaged technique overestimated hepatic (Formula presented.) values (slope: 0.35; intercept: 30.2 1/s) compared to the Cartesian reference. However, performing a respiratory-resolved reconstruction led to better (Formula presented.) value consistency (slope: 0.77; intercept: 7.5 1/s). Conclusions: The proposed techniques are promising alternatives to conventional Cartesian imaging for fat and (Formula presented.) quantification in patients with limited breath-holding capabilities. For accurate (Formula presented.) estimation, respiratory-resolved reconstruction should be used.
CRIS-Team Scopus Importer:2020-04-28
Keywords: compressed sensing; free-breathing fat/ R2∗ quantification; multi-echo 3D stack-of-stars GRE; nonalcoholic fatty liver disease; radial sampling; respiratory motion-resolved reconstruction

Free-Breathing, Self-Navigated and Dynamic 3-D Multi-Contrast Cardiac CINE Imaging Using Cartesian Sampling and Compressed Sensing - Hoppe, Elisabeth; Wetzl, Jens; Forman, Christoph; Koerzdoerfer, Gregor; Schneider, Manuel; Speier, Peter; Schmidt, Michaela; Maier, Andreas (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition), 2019)
We present a free-breathing multi-contrast 3-D cardiac CINE acquisition and reconstruction technique based on Compressed Sensing. Inversion pulses were repeatedly applied during a continuous acquisition to sample contrast- and cardiac-resolved 3-D data, while a self-navigation method was applied for respiratory gating. Validation was performed in a phantom, showing recovery curves and T1* maps in good correlation to known T1 values for the phantom as well as a MOLLI reference measurement. Feasibility for in-vivo application was demonstrated in a healthy voluntee
Keywords: Magnetic Resonance Imaging; Cardiac CINE Magnetic Resonance Imaging; Multicontrast Imaging; Compressed Sensing

Free-Breathing Self-Navigated Isotropic 3-D CINE Imaging of the Whole Heart using Adaptive Triggering and Retrospective Gating - Wetzl, Jens; Lugauer, Felix; Kroeker, Randall; Schmidt, Michaela; Maier, Andreas; Forman, Christoph (Proceedings of the 25th Annual Meeting of the ISMRM, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.freebr_8

Free-Breathing, Self-Navigated Isotropic 3-D CINE Imaging of the Whole Heart Using Cartesian Sampling - Wetzl, Jens; Lugauer, Felix; Schmidt, Michaela; Maier, Andreas; Hornegger, Joachim; Forman, Christoph (Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.freebr

Free-Breathing Whole-Heart Coronary MRA: Motion Compensation Integrated into 3D Cartesian Compressed Sensing Reconstruction - Forman, Christoph; Grimm, Robert; Hutter, Jana; Maier, Andreas; Hornegger, Joachim; Zenge, Michael O. (MICCAI 2013, Part II, LNCS 8150, 2013)

Free-Breathing Whole-Heart Coronary MRI: An Image-Based Motion Compensation Integrated Into Compressed-Sensing Reconstruction - Forman, Christoph; Grimm, Robert; Hutter, Jana; Wasza, Jakob; Kraus, Martin; Hornegger, Joachim; Zenge, Michael O. (Proceedings of the 21st Annual Meeting of ISMRM, 2013)
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.freebr_2

Frist experimental results on long-object imaging using a reverse helical trajectory with a C-arm system - Yu, Zhicong; Maier, Andreas; Schönborn, Manfred; Vogt, Florian; Köhler, Christoph; Lauritsch, Günter; Hornegger, Joachim; Noo, Frédéric (Proceedings of The second international conference on image formation in x-ray computed tomography, 2012)

From discourse to pathology: Automatic identification of Parkinson's disease patients via morphological measures across three languages - Eyigoz, Elif; Courson, Melody; Sedeño, Lucas; Rogg, Katharina; Orozco-Arroyave, Juan Rafael; Nöth, Elmar; Skodda, Sabine; Trujillo, Natalia; Rodríguez, Mabel; Rusz, Jan; Muñoz, Edinson; Cardona, Juan F.; et al. (Cortex, 2020)
Embodied cognition research on Parkinson's disease (PD) points to disruptions of frontostriatal language functions as sensitive targets for clinical assessment. However, no existing approach has been tested for crosslinguistic validity, let alone by combining naturalistic tasks with machine-learning tools. To address these issues, we conducted the first classifier-based examination of morphological processing (a core frontostriatal function) in spontaneous monologues from PD patients across three typologically different languages. The study comprised 330 participants, encompassing speakers of Spanish (61 patients, 57 matched controls), German (88 patients, 88 matched controls), and Czech (20 patients, 16 matched controls). All subjects described the activities they perform during a regular day, and their monologues were automatically coded via morphological tagging, a computerized method that labels each word with a part-of-speech tag (e.g., noun, verb) and specific morphological tags (e.g., person, gender, number, tense). The ensuing data were subjected to machine-learning analyses to assess whether differential morphological patterns could classify between patients and controls and reflect the former's degree of motor impairment. Results showed robust classification rates, with over 80% of patients being discriminated from controls in each language separately. Moreover, the most discriminative morphological features were associated with the patients' motor compromise (as indicated by Pearson r correlations between predicted and collected motor impairment scores that ranged from moderate to moderate-to-strong across languages). Taken together, our results suggest that morphological patterning, an embodied frontostriatal domain, may be distinctively affected in PD across languages and even under ecological testing conditions.
CRIS-Team Scopus Importer:2020-10-09
Keywords: Automated speech analysis; Cross-linguistic validity; Linguistic assessments; Morphology; Parkinson's disease

From Emotion to Interaction - Lessons from Real Human-Machine-Dialogues - Batliner, Anton; Hacker, Christian; Steidl, Stefan; Nöth, Elmar; Haas, Jürgen (Affective Dialogue Systems, Proceedings of a Tutorial and Research Workshop, 2004)
The monitoring of emotional user states can help to assess the progress of human-machine-communication. If we look at specific databases, however, we are faced with several problems: users behave differently, even within one and the same setting, and some phenomena are sparse; thus it is not possible to model and classify them reliably. We exemplify these difficulties on the basis of SympaFly, a database with dialogues between users and a fully automatic speech dialogue telephone system for flight reservation and booking, and discuss possible remedies.

Frühzeitige Registrierung eines 3D-Overlays des linken Atriums während linksatrialen Ablationen mittels Koronarsinuskatheter - Bourier, Felix; Schneider, Hans-Jürgen; Heißenhuber, Frank; Ganslmeier, Patrycja; Fischer, Robert; Brost, Alexander; Koch, Martin; Strobel, Norbert; Hornegger, Joachim; Kurzidim, Klaus (77. Jahrestagung, 2011)

Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps - Sanders, James Chester; Ritt, Philipp; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas (IEEE Transactions on Medical Imaging, 2016)
We propose a data-driven method for extracting a respiratory surrogate signal from SPECT list-mode data. The approach is based on dimensionality reduction with Laplacian Eigenmaps. By setting a scale parameter adaptively and adding a series of post-processing steps to correct polarity and normalization between projections, we enable fully-automatic operation and deliver a respiratory surrogate signal for the entire SPECT acquisition. We validated the method using 67 patient scans from three acquisition types (myocardial perfusion, liver shunt diagnostic, lung inhalation/perfusion) and an Anzai pressure belt as a gold standard. The proposed method achieved a mean correlation against the Anzai of 0.81 ± 0.17 (median 0.89). In a subsequent analysis, we characterize the performance of the method with respect to count rates and describe a predictor for identifying scans with insufficient statistics. To the best of our knowledge, this is the first large validation of a data-driven respiratory signal extraction method published thus far for SPECT, and our results compare well with those reported in the literature for such techniques applied to other modalities such as MR and PET.
EVALuna2:13863

Fully Automated Knowledge-Based Segmentation of the Caudate Nuclei in 3-D MRI - Wels, Michael; Huber, Martin; Hornegger, Joachim (3D Segmentation in the Clinic - A Grand Challenge MICCAI 2007 Workshop Proceedings, 2007)

Fully automated segmentation of multiple sclerosis lesions in multispectral MRI - Wels, Michael; Huber, M.; Hornegger, Joachim (Pattern Recognition and Image Analysis, 2008)
This paper addresses segmentation of multiple sclerosis lesions in multispectral 3-D brain MRI data. For this purpose, we propose a novel fully automated segmentation framework based on probabilistic boosting trees, which is a recently introduced strategy for supervised learning. By using the context of a voxel to be classified and its transformation to an overcomplete set of Haar-like features, it is possible to capture class specific characteristics despite the well-known drawbacks of MR imaging. By successively selecting and combining the most discriminative features during ensemble boosting within a tree structure, the overall procedure is able to learn a discriminative model for voxel classification in terms of posterior probabilities. The final segmentation is obtained after refining the preliminary result by stochastic relaxation and a standard level set approach. A quantitative evaluation within a leave-one-out validation shows the applicability of the proposed method. © 2008 Pleiades Publishing, Ltd.
UnivIS-Import:2015-04-16:Pub.2008.tech.IMMD.IMMD5.fullya

Fully Automatic Assessment of Speech of Children with Cleft Lip and Palate - Maier, Andreas; Nöth, Elmar; Batliner, Anton; Nkenke, Emeka; Schuster, Maria (Informatica, 2006)

Fully-automatic CT data preparation for interventional X-ray skin dose simulation - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Stimpel, Bernhard; Syben, Christopher; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik Aktuell Bildverarbeitung für die Medizin 2020 Algorithmen - Systeme - Anwendungen, 2020)
Recently, deep learning (DL) found its way to interventional X-ray skin dose estimation. While its performance was found to be acceptable, even more accurate results could be achieved if more data sets were available for training. One possibility is to turn to computed tomography (CT) data sets. Typically, computed tomography (CT) scans can be mapped to tissue labels and mass densities to obtain training data. However, care has to be taken to make sure that the different clinical settings are properly accounted for. First, the interventional environment is characterized by wide variety of table setups that are significantly different from the typical patient tables used in conventional CT. This cannot be ignored, since tables play a crucial role in sound skin dose estimation in an interventional setup, e.,g., when the X-ray source is directly underneath a patient (posterior-anterior view). Second, due to interpolation errors, most CT scans do not facilitate a clean segmentation of the skin border. As a solution to these problems, we applied connected component labeling (CCL) and Canny edge detection to (a) robustly separate the patient from the table and (b) to identify the outermost skin layer. Our results show that these extensions enable fully-automatic, generalized pre-processing of CT scans for further simulation of both skin dose and corresponding X-ray projection
Keywords: X-ray simulation; Computed tomography; Data preparation

Fully Automatic Head Motion Correction for Interventional C-arm Systems using Fiducial Markers - Müller, Kerstin; Berger, Martin; Choi, Jang-Hwan; Datta, Sanjit; Gehrisch, Sonja; Moore, Teri; Marks, Michael P; Maier, Andreas; Fahrig, Rebecca (Proceedings of the 13th Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.fullya_7

Fully Automatic Maximum Intensity Projections of Regions of Interest in Magnetic Resonance Angiograms - Seeger, Christoph; Brost, Alexander; Dobre, Mircea; Fischbein, Nancy; Han, Zhaoying; Maclaren, Julian; Vos, Sjoerd; Hornegger, Joachim; Bammer, Roland (Proceedings of the 21st Annual meeting of the ISMRM, 2013)

Fully Automatic Segmentation of Anatomy and Scar from LGE-MRI - Kurzendorfer, Tanja (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2018)
The leading cause of death worldwide are cardiovascular diseases. In addition, the number of patients suffering from heart failure is rising. The underlying cause of heart failure is often a myocardial infarction. For diagnosis in clinical routine, cardiac magnetic resonance imaging is used, as it provides information about morphology, blood flow, perfusion, and tissue characterization. In more detail, the analysis of the tissue viability is very important for diagnosis, procedure planning, and guidance, i.e., for implantation of a bi-ventricular pacemaker. The clinical gold standard for the viability assessment is 2-D late gadolinium enhanced magnetic resonance imaging (LGE-MRI). In the last years, the imaging quality continuously improved and LGE-MRI was extended to a 3-D whole heart scan. This scan guarantees an accurate quantification of the myocardium to the extent of myocardial scarring. The main challenge arises in the accurate segmentation and analysis of such images. In this work, novel methods for the segmentation of the LGE-MRI data sets, both 2-D and 3-D, are proposed. One important goal is the direct segmentation of the LGE-MRI and the independence of an anatomical scan to avoid errors from the anatomical scan contour propagation. For the 2-D LGE-MRI segmentation, the short axis stack of the left ventricle (LV) is used. First, the blood pool is detected and a rough outline is maintained by a morphological active contours without edges approach. Afterwards, the endocardial and epicardial boundary is estimated by either a filter or learning based method in combination with a minimal cost path search in polar space. For the endocardial contour refinement, an additional scar exclusion step is added. For the 3-D LGE-MRI, the LV is detected within the whole heart scan. In the next step, the short axis view is estimated using principal component analysis. For the endocardial and epicardial boundary estimation also a filter based or learning based approach can be applied in combination with dynamic programming in polar space. Furthermore, because of the high resolution also the papillary muscles are segmented. In addition to the fully automatic LV segmentation approaches, a generic semi- automatic method based on Hermite radial basis function interpolation is introduced in combination with a smart brush. Effective interactions with less number of equations accelerate the performance and therefore, a real-time and an intuitive, interactive segmentation of 3-D objects is supported effectively. After the segmentation of the left ventricle’s myocardium, the scar tissue is quantified. In this thesis, three approaches are investigated. The full-width-at-half-max algorithm and the x-standard deviation methods are implemented in a fully automatic manner. Furthermore, a texture based scar classification algorithm is introduced. Subsequently, the scar tissue can be visualized, either in 3-D as a surface mesh or in 2-D projected onto the 16 segment bull’s eye plot of the American Heart Association. However, for precise procedure planning and guidance, the information about the scar transmurality is very important. Hence, a novel scar layer visualization is introduced. Therefore, the scar tissue is divided into three layers depending on the location of the scar within the myocardium. With this novel visualization, an easy distinction between endocardial, mid-myocardial, or epicardial scar is possible. The scar layers can also be visualized in 3-D as surface meshes or in 2-D projected onto the 16 segment bull’s eye plot.<br
Keywords: Late-Gadolinium Enhanced Magnetic Resonance Imaging; Left Ventricle; Segmentation

Fully automatic segmentation of left ventricular anatomy in 3-DLGE-MRI - Kurzendorfer, Tanja; Forman, Christoph; Schmidt, Michaela; Tillmanns, Christoph; Maier, Andreas; Brost, Alexander (Computerized Medical Imaging and Graphics, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fullya_0

Fully Automatic Segmentation of Papillary Muscles in 3-D LGE-MRI - Kurzendorfer, Tanja; Brost, Alexander; Forman, Christoph; Schmidt, Michaela; Tillmanns, Christoph; Maier, Andreas (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.fullya_7

Fusing Motion Estimates for CBCT Reconstruction - Bier, Bastian; Unberath, Mathias; Geimer, Tobias; Ravikumar, Nishant; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.fusing

Fuzzy C-means Clustering For Retinal Layer Segmentation On High Resolution OCT Images - Mayer, Markus Anton; Tornow, Ralf-Peter; Hornegger, Joachim; Kruse, Friedrich (Analysis of Biomedical Signals and Images, Proceedings of the Biosignal 2008 International Eurasip Conference, 2008)

GAN-based Augmentation of Mammograms to Improve Breast Lesion Detection - El-Ghoussani, Amir; Rodriguez Salas, Dalia; Seuret, Mathias; Maier, Andreas (Informatik aktuell, 2022)
CRIS-Team Scopus Importer:2022-05-06

Gastrointestinal surgery 2025 - Changes emerging with information science - Hornegger, Joachim; Höller, Kurt Emmerich; Hempel, Sarah-Marie (Endoskopie heute, 2011)
Gastrointestinal surgery is influenced by developments in information science. In particular, it is expected that information science will serve as a basis for NOTES procedures. In this contribution we provide an overview over the expected progress and associated problems. High innovation speed in technology and the continuously increasing complexity of technical systems open up great opportunities, but imply also problems that have to be assessed actively by physicians and surgeons. © Georg Thieme Verlag KG Stuttgart New York.

Gender-dependent GMM-UBM for Tracking Parkinson's Disease Progression from Speech - Arias Vergara, Tomás; Vasquez Correa, Juan; Orozco-Arroyave, Juan Rafael; Vargas-Bonilla, Jesus Francisco; Haderlein, Tino; Nöth, Elmar (Speech Communication - 12. ITG Fachtagung Sprachkommunikation, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.gender
Keywords: Sprache

Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning - Escobar-Grisales, Daniel; Vasquez Correa, Juan; Orozco Arroyave, Juan Rafael (Communications in Computer and Information Science, 2021)
The interest in demographic information retrieval based on text data has increased in the research community because applications have shown success in different sectors such as security, marketing, heath-care, and others. Recognition and identification of demographic traits such as gender, age, location, or personality based on text data can help to improve different marketing strategies. For instance it makes it possible to segment and to personalize offers, thus products and services are exposed to the group of greatest interest. This type of technology has been discussed widely in documents from social media. However, the methods have been poorly studied in data with a more formal structure, where there is no access to emoticons, mentions, and other linguistic phenomena that are only present in social media. This paper proposes the use of recurrent and convolutional neural networks, and a transfer learning strategy for gender recognition in documents that are written in informal and formal languages. Models are tested in two different databases consisting of Tweets and call-center conversations. Accuracies of up to 75% are achieved for both databases. The results also indicate that it is possible to transfer the knowledge from a system trained on a specific type of expressions or idioms such as those typically used in social media into a more formal type of text data, where the amount of data is more scarce and its structure is completely different.
CRIS-Team Scopus Importer:2021-10-22
Keywords: Convolutional neural networks; Demographic information retrieval; Gender recognition; Recurrent neural networks; Transfer learning

Generation of Personalized Computational Phantoms Using Only Patient Metadata - Zhong, Xia; Strobel, Norbert; Sanders, James Chester; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.genera_4

Generic Features for Biosignal Classification - Jensen, Ulf; Ring, Matthias; Eskofier, Björn (Sportinformatik 2012: 9. Symposium der Sektion Sportinformatik der Deutschen Vereinigung für Sportwissenschaft, 2012)

Genetic programming for expert systems - Sickel, Konrad; Hornegger, Joachim (2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 2010)
Genetic programming is the usage of the paradigm of survival of the fittest in scientific computing. It is applied to evolve solutions to problems where dependencies between multiple input factors are unknown. In this paper we propose and evaluate the application of a specifically adapted genetic programming framework to optimize the rule base of an expert system. The expert system controls a computer-aided-design software and targets the automation of a manufacturing process. The used steady state genetic programming framework introduces some variations on the selection and evolution operators normally used in genetic programming. In particular: size enforcing mutation, dynamic fitness calculation and size constraint ranking. The genetic programming system is evaluated with real data and led to an improved expert system performance of about 22 percent. © 2010 IEEE.

Geometric Accuracy of 3-D X-Ray Image-Based Localization from Two C-Arm Views - Brost, Alexander; Strobel, Norbert; Yatziv, Liron; Gilson, Wesley D.; Meyer, Bernhard; Hornegger, Joachim; Lewin, Jonathan; Wacker, Frank (MICCAI 2009 Workshop on Geometric Accuracy in Image Guided Interventions, 2009)

Geometric Adjustment of X-ray Tomosynthesis - Grulich, Tobias; Holub, Wolfgang; Haßler, Ulf; Aichert, André; Maier, Andreas (Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.geomet

Geometrical jitter correction in computed tomography - Maass, Nicole; Dennerlein, Frank; Aichert, André; Maier, Andreas (Proceedings of the third international conference on image formation in X-ray computed tomography, 2014)
UnivIS-Import:2018-09-06:Pub.2014.tech.IMMD.IMMD5.geomet_0

Geometric calibration of the circle-plus-arc trajectory - Hoppe, Stefan; Noo, Frédéric; Dennerlein, Frank; Lauritsch, Günter; Hornegger, Joachim (Physics in Medicine and Biology, 2007)
In this paper, a novel geometric calibration method for C-arm cone-beam scanners is presented which allows the calibration of the circle-plus-arc trajectory. The main idea is the separation of the trajectory into two circular segments (circle segment and arc segment) which are calibrated independently. This separation makes it possible to reuse a calibration phantom which has been successfully applied in clinical environments to calibrate numerous routinely used C-arm systems. For each trajectory segment, the phantom is placed in an optimal position. The two calibration results are then combined by computing the transformation the phantom underwent between the independent calibration runs. This combination can be done in a post-processing step by using standard linear algebra. The method is not limited to circle-plus-arc trajectories and works for any calibration procedure in which the phantom has a preferred orientation with respect to a trajectory segment. Results are presented for both simulated as well as real data acquired with a C-arm system. We also present the first image reconstruction results for the circle-plus-arc trajectory using real C-arm data. © 2007 IOP Publishing Ltd.

Geometric primitive refinement for structured light cameras - Fürsattel, Peter; Placht, Simon; Maier, Andreas; Riess, Christian (Machine Vision and Applications, 2018)
Keywords: Geometric primitives; Structured light; Range imaging

Gesture recognition with a Time-Of-Flight camera - Kollorz, Eva Nicole Karin; Penne, Jochen; Hornegger, Joachim; Barke, Alexander (International Journal of Intelligent Systems Technologies and Applications, 2008)

Glaucoma classification based on histogram analysis of diffusion tensor imaging measures in the optic radiation - El-Rafei, Ahmed Mohamed Ibrahim; Engelhorn, Tobias; Wärntges, Simone; Dörfler, Arnd; Hornegger, Joachim; Michelson, Georg (Springer-verlag, 2011)
Glaucoma is associated with axonal degeneration of the optic nerve leading to visual impairment. This impairment can progress to a complete vision loss. The transsynaptic disease spread in glaucoma extends the degeneration process to different parts of the visual pathway. Most of glaucoma diagnosis focuses on the eye analysis, especially in the retina. In this work, we propose a system to classify glaucoma based on visual pathway analysis. The system utilizes diffusion tensor imaging to identify the optic radiation. Diffusion tensor-derived indices describing the underlying fiber structure as well as the main diffusion direction are used to characterize the optic radiation. Features are extracted from the histograms of these parameters in regions of interest defined on the optic radiation. A support vector machine classifier is used to rank the extracted features according to their discrimination ability between glaucoma patients and healthy subjects. The seven highest ranked features are used as inputs to a logistic regression classifier. The system is applied to two age-matched groups of 39 glaucoma subjects and 27 normal controls. The evaluation is performed using a 10-fold cross validation scheme. A classification accuracy of 81.8% is achieved with an area under the ROC curve of 0.85. The performance of the system is competitive to retina based classification systems. However, this work presents a new direction in detecting glaucoma using visual pathway analysis. This analysis is complementary to eye examinations and can result in improvements in glaucoma diagnosis, detection, and treatment. © 2011 Springer-Verlag.

Glaucoma classification based on visual pathway analysis using diffusion tensor imaging - El-Rafei, Ahmed Mohamed Ibrahim; Engelhorn, Tobias; Wärntges, Simone; Dörfler, Arnd; Hornegger, Joachim; Michelson, Georg (Magnetic Resonance Imaging, 2013)
Most of the existing methods for diagnosing glaucoma analyze the eye with a main focus on the retina, despite the transsynaptic nature of the fiber degeneration caused by glaucoma. Thus, they ignore a significant part of the visual system represented by the visual pathway in the brain. The advances in neuroimaging, especially diffusion tensor imaging (DTI), enable the identification and characterization of white matter fibers. In this work, we propose a system based on DTI analysis of the visual pathway fibers in the optic radiation for detecting and discriminating different glaucoma entities. The optic radiation is identified semi-automatically. DTI provides information about the fiber orientation as well as a set of derived parameters describing the degree of diffusion anisotropy and diffusivity. Features for each DTI derived measure are extracted from a specified region of interest on the optic radiation. The features are grouped into three sets: Histogram, co-occurrence matrices, and Laws features. For feature selection, the features are ranked using a support vector machine classifier. The highest ranked features are used for classification. A support vector machine classifier is used for classification in a 10-fold cross validation setup. The system is applied to three age-matched subjects' categories containing 27 healthy, 39 primary open angle glaucoma (POAG), and 18 normal tension glaucoma (NTG) subjects. The discrimination accuracy between healthy and glaucoma (POAG and NTG) subjects is 94.1% with an area under the ROC of 0.97. Classification accuracy of 92.4% is obtained for the normal and the POAG groups while it increased to 100% in case of healthy and NTG groups. In addition, the system could differentiate between glaucoma types (POAG and NTG) with an accuracy of 98.3%. A complementary analysis was performed to estimate the selection bias in the obtained accuracy. The bias ranged from 10% to 20% depending on the group pair under consideration. The classification results indicate the high performance of the system compared to retina-based glaucoma detection systems. The proposed approach utilizes visual pathway analysis rather than the conventional eye analysis which presents a new trend in glaucoma detection. Analyzing the entire visual system could provide significant information that can improve the glaucoma examination flow and treatment. (c) 2013 Elsevier Inc. All rights reserved.
Keywords: Diffusion tensor imaging; Glaucoma; Optic radiation; Visual pathway; Classification; Texture features

Glaucoma risk index: Automated glaucoma detection from color fundus images - Bock, Rüdiger; Meier, Jörg; Nyul, Laszlo G.; Hornegger, Joachim; Michelson, Georg (Medical Image Analysis, 2010)
Glaucoma as a neurodegeneration of the optic nerve is one of the most common causes of blindness. Because revitalization of the degenerated nerve fibers of the optic nerve is impossible early detection of the disease is essential. This can be supported by a robust and automated mass-screening. We propose a novel automated glaucoma detection system that operates on inexpensive to acquire and widely used digital color fundus images. After a glaucoma specific preprocessing, different generic feature types are compressed by an appearance-based dimension reduction technique. Subsequently, a probabilistic two-stage classification scheme combines these features types to extract the novel Glaucoma Risk Index (GRI) that shows a reasonable glaucoma detection performance. On a sample set of 575 fundus images a classification accuracy of 80% has been achieved in a 5-fold cross-validation setup. The GRI gains a competitive area under ROC (AUC) of 88% compared to the established topography-based glaucoma probability score of scanning laser tomography with AUC of 87%. The proposed color fundus image-based GRI achieves a competitive and reliable detection performance on a low-priced modality by the statistical analysis of entire images of the optic nerve head. © 2009 Elsevier B.V.

Glottal Flow Patterns Analyses for Parkinson's Disease Detection: Acoustic and Nonlinear Approaches - Belalcazar-Bolaños, Elkyn; Orozco Arroyave, Juan Rafael; Vargas-Bonilla, Jesus Francisco; Haderlein, Tino; Nöth, Elmar (Proc. Text, Speech and Dialogue; 19th International Conference, TSD 2016, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.glotta
Keywords: Sprache

GPU-accelerated SART reconstruction using the CUDA programming environment - Keck, Benjamin; Hofmann, Hannes; Scherl, Hermann; Kowarschik, Markus; Hornegger, Joachim (Medical Imaging 2009: Physics of Medical Imaging, 2009)
The Common Unified Device Architecture (CUDA) introduced in 2007 by NVIDIA is a recent programming model making use of the unified shader design of the most recent graphics processing units (GPUs). The programming interface allows algorithm implementation using standard C language along with a few extensions without any knowledge about graphics programming using OpenGL, DirectX, and shading languages. We apply this novel technology to the Simultaneous Algebraic Reconstruction Technique (SART), which is an advanced iterative image reconstruction method in cone-beam CT. So far, the computational complexity of this algorithm has prohibited its use in most medical applications. However, since today's GPUs provide a high level of parallelism and are highly cost-efficient processors, they are predestinated for performing the iterative reconstruction according to medical requirements. In this paper we present an efficient implementation of the most time-consuming parts of the iterative reconstruction algorithm: forward- and back-projection. We also explain the required strategy to parallelize the algorithm for the CUDA 1.1 and CUDA 2.0 architecture. Furthermore, our implementation introduces an acceleration technique for the reconstruction compared to a standard SART implementation on the GPU using CUDA. Thus, we present an implementation that can be used in a time-critical clinical environment. Finally, we compare our results to the current applications on multi-core workstations, with respect to both reconstruction speed and (dis-)advantages. Our implementation exhibits a speed-up of more than 64 compared to a state-of-the-art CPU using hardware-accelerated texture interpolation. © 2009 SPIE.

GPU Accelerated Time-of-Flight Super-Resolution for Image-Guided Surgery - Wetzl, Jens; Taubmann, Oliver; Haase, Sven; Köhler, Thomas; Kraus, Martin; Hornegger, Joachim (Bildverarbeitung für die Medizin, 2013)

Gradient-Based Differential Approach for 3-D Motion Compensation in Interventional 2-D/3-D Image Fusion - Wang, Jian; Borsdorf, Anja; Heigl, Benno; Köhler, Thomas; Hornegger, Joachim (International Conference on 3D Vision, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.gradie_6

Gradient-Delay-Compensated Radial MRI for Fat Content and Fatty Acid Composition Quantification - Schneider, Manuel; Lugauer, Felix; Hoppe, Elisabeth; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the ISMRM Workshop on Quantitative Body Imaging, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.gradie_1

Graph-based procedural abstraction - Dreweke, Alexander; Wörlein, Marc; Fischer, Ingrid; Schell, Dominic; Meinl, Thorsten; Philippsen, Michael (International Symposium on Code Generation and Optimization (CGO'07), 2007)
Procedural abstraction (PA) extracts duplicate code segments into a newly created method and hence reduces code size. For embedded micro computers the amount of memory is still limited so code reduction is an important issue. This paper presents a novel approach to PA, that is especially targeted towards embedded systems. Earlier approaches of PA are blind with respect to code reordering, i.e., two code segments with the same semantic effect but with different instruction orders were not detected as candidates for PA. Instead of instruction sequences, in our approach the data flow graphs of basic blocks are considered. Compared to known PA techniques more than twice the number of instructions can be saved on a set of binaries, by detecting frequently appearing graph fragments with a graph mining tool based on the well known gSpan algorithm. The detection and extraction of graph fragments is not as straight forward as extracting sequential code fragments. NP-complete graph operations and special rules to decide which parts can be abstracted are needed. However, this effort pays off as smaller sizes significantly reduce costs on mass-produced embedded systems. © 2007 IEEE.

Graphical explanations for large semantic networks - Prechtel, Reinhard; Niemann, Heinrich; Nöth, Elmar; Sagerer, Gerhard (Proc. COMPUGRAPHICS'91, 1991)

Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Calcium Scoring - Durlak, Felix; Wels, Michael; Schwemmer, Chris; Sühling, Michael; Steidl, Stefan; Maier, Andreas (Machine Learning in Medical Imaging, 2017)
UnivIS-Import:2017-11-06:Pub.2017.tech.IMMD.IMMD5.growin

Guided Image Super-Resolution: A New Technique for Photogeometric Super-Resolution in Hybrid 3-D Range Imaging - Ghesu, Florin-Cristian; Köhler, Thomas; Haase, Sven; Hornegger, Joachim (Pattern Recognition, 2014)
In this paper, we augment multi-frame super-resolution with the concept of guided filtering for simultaneous upsampling of 3-D range data and complementary photometric information in hybrid range imaging. Our guided super-resolution algorithm is formulated as joint maximum a-posteriori estimation to reconstruct high-resolution range and photometric data. In order to exploit local correlations between both modalities, guided filtering is employed for regularization of the proposed joint energy function. For fast and robust image reconstruction, we employ iteratively re-weighted least square minimization embedded into a cyclic coordinate descent scheme. The proposed method was evaluated on synthetic datasets and real range data acquired with Microsoft’s Kinect. Our experimental evaluation demonstrates that our approach outperforms state-of-the-art range super-resolution algorithms while it also provides super-resolved photometric data.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.guided_65

Guided Noise Reduction for Spectral CT with Energy-Selective Photon Counting Detectors - Manhart, Michael; Fahrig, Rebecca; Hornegger, Joachim; Dörfler, Arnd; Maier, Andreas (Proceedings of the Third CT Meeting, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.guided_6

Guided Noise Reduction with Streak Removal for High Speed Flat Detector CT Perfusion - Manhart, Michael; Aichert, André; Kowarschik, Markus; Deuerling-Zheng, Yu; Struffert, Tobias; Dörfler, Arnd; Maier, Andreas; Hornegger, Joachim (Proceedings NSS/MIC 2013, 2013)
Tissue perfusion measurement using C-arm angiography systems capable of CT-like imaging (flat detector CT (FD-CT)) is a novel technique with high potential benefit for catheter-guided treatment of stroke in the interventional suite. New high speed protocols (HSP) with increased C-arm rotation speed enable fast acquisitions of FD-CT volumes and allow for sampling the contrast flow with improved temporal resolution. However, the peak contrast attenuation values of brain tissue typically lie in a range of 5-30 HU. Thus perfusion imaging is very sensitive to noise. Recently we introduced the FDK-JBF denoising technique based on Feldkamp (FDK) reconstruction followed by denoising in volume space using joint bilateral filtering (JBF). In the evaluation FDK-JBF achieved comparable results to algebraic techniques, but is computationally less costly. Yet the angular sampling of the projection data in the HSP is coarse, which leads to streak artifacts in the reconstructed volumes. Mask volumes are subtracted from the contrast agent enhanced (bolus) volumes and the streak artifacts are subtracted out if the patient does not move during the acquisition. However, in case of motion the streak artifacts will not be identical in the mask and bolus volumes. We show that these streaks can lead to severe artifacts in the perfusion maps and describe a novel technique for streak removal (SR), which is based on streak detection by using time-contrast curve analysis.We evaluated the FDK-SR-JBF algorithm in a phantom and a patient study and show that noise and streaks can be reduced within a short computation time. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.guided_5
Keywords: Flat detector CT; noise reduction; Perfusion imaging; stroke treatment

Guiding transseptal puncture by 3D-overlay of the left atrium and ascending aorta by a new softwareprototype - Bourier, Felix; Brost, Alexander; Koch, Martin; Kleinöder, Andreas; Kiraly, Attila P.; Hornegger, Joachim; Strobel, Norbert; Zorger, Niels; Schneider, Hans-Jürgen; Kurzidim, Klaus (ESC Congress, 2011)

Handbuch für NeuroGraph Version 3.4 - Wilke, Peter (1994)

Handbuch für NeuroGraph Version 3.6 - Wilke, Peter (1996)

Handheld ultrahigh speed swept source optical coherence tomography instrument using a MEMS scanning mirror - Lu, Chen D.; Kraus, Martin; Potsaid, Benjamin; Liu, Jonathan J.; Choi, WooJhon; Jayaraman, Vijaysekhar; Cable, Alex E.; Hornegger, Joachim; Duker, Jay S.; Fujimoto, James G. (Biomedical Optics Express, 2014)
We developed an ultrahigh speed, handheld swept source optical coherence tomography (SS-OCT) ophthalmic instrument using a 2D MEMS mirror. A vertical cavity surface-emitting laser (VCSEL) operating at 1060 nm center wavelength yielded a 350 kHz axial scan rate and 10 μm axial resolution in tissue. The long coherence length of the VCSEL enabled a 3.08 mm imaging range with minimal sensitivity roll-off in tissue. Two different designs with identical optical components were tested to evaluate handheld OCT ergonomics. An iris camera aided in alignment of the OCT beam through the pupil and a manual fixation light selected the imaging region on the retina. Volumetric and high definition scans were obtained from 5 undilated normal subjects. Volumetric OCT data was acquired by scanning the 2.4 mm diameter 2D MEMS mirror sinusoidally in the fast direction and linearly in the orthogonal slow direction. A second volumetric sinusoidal scan was obtained in the orthogonal direction and the two volumes were processed with a software algorithm to generate a merged motion-corrected volume. Motion-corrected standard 6 × 6 mm2 and wide field 10 × 10 mm2 volumetric OCT data were generated using two volumetric scans, each obtained in 1.4 seconds. High definition 10 mm and 6 mm B-scans were obtained by averaging and registering 25 B-scans obtained over the same position in 0.57 seconds. One of the advantages of volumetric OCT data is the generation of en face OCT images with arbitrary cross sectional B-scans registered to fundus features. This technology should enable screening applications to identify early retinal disease, before irreversible vision impairment or loss occurs. Handheld OCT technology also promises to enable applications in a wide range of settings outside of the traditional ophthalmology or optometry clinics including pediatrics, intraoperative, primary care, developing countries, and military medicine. © 2013 Optical Society of America.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.handhe

Handling multiple materials for exposure of digital forgeries using 2-D lighting environments - Riess, Christian; Unberath, Mathias; Naderi Boldaji, Farzad; Pfaller, Sven; Stamminger, Marc; Angelopoulou, Elli (Multimedia Tools and Applications, 2017)

Handwritten Text Recognition Error Rate Reduction in Historical Documents using Naive Transcribers - Christlein, Vincent; Nicolaou, Anguelos; Schlauwitz, Thorsten; Späth, Sabrina; Herbers, Klaus; Maier, Andreas (2018)
Handwritten text recognition (HTR) is a difficult research problem. In particular for historical documents, this task is hard as handwriting style, orthography, and text quality pose significant challenges. Creation of a single multi-purpose HTR system seems to be out of reach for current state-of-the-art systems. Therefore, we are interested in fast creation of specialized HTR systems for a particular set of historical documents. Still manual annotation by historical experts is expensive and can often not be applied at a large scale. Instead, we use the transcripts of naive transcribers that may still contain a significant amount of errors. In this paper, we propose to fuse the recognized word-chain with naive transcribers that can be obtained in a cost-effective way. For the actual fusion, we rely on a word-level approach, the so-called Recognizer Output Voting Error Reduction (ROVER). Results indicate that we are able to reduce the Word Error Rate (WER) of an HTR system trained with only few pages from 2.6 % to 19.2% with two additional transcribers with 25.1% and 27.1% WER each. This performance is already close to current state-of-the-art systems trained with significantly more dat
Keywords: handwritten text recognition; line fusion; naive transcribers; Nürnberg; Briefbücher; 15. Jahrhundert

Hardware Failure Prediction on Imbalanced Times Series Data: Generation of Artificial Data Using Gaussian Process and Applying LSTMFCN to Predict Broken Hardware - Rücker, Nadine; Pflueger, Lea; Maier, Andreas (Journal of Digital Imaging, 2021)
Magnetic resonance imaging (MRI) systems and their continuous, failure-free operation is crucial for high-quality diagnostics and seamless workflows. One important hardware component is coils as they detect the magnetic signal. Before every MRI scan, several image features are captured which represent the used coil’s condition. These image features recorded over time are used to train machine learning models for classification of coils into normal and broken coils for faster and easier maintenance. The state-of-the-art techniques for classification of time series involve different kinds of neural networks. We leveraged sequential data and trained three models, long short-term memory (LSTM), fully convolutional network (FCN), and the combination of those called LSTMFCN as reported by Karim et al. (IEEE access 6:1662–1669, 2017). We found LSTMFCN to combine the benefits of LSTM and FCN. Thus, we achieved the highest F1-score of 87.45% and the highest accuracy of 99.35% using LSTMFCN. Furthermore, we tackled the high data imbalance of only 2.1% data collected from broken coils by training a Gaussian process (GP) regressor and adding predicted sequences as artificial samples to our broken labelled data. Adding 40 synthetic samples increased the classification results of LSTMFCN to an F1-score of 92.30% and accuracy of 99.83%. Thus, MRI head/neck coils can be classified normal or broken by training a LSTMFCN on image features, successfully. Augmenting the data using GP-generated samples can improve the performance even further.
CRIS-Team Scopus Importer:2021-01-15
Keywords: Gaussian process regression; Hardware failure prediction; Machine learning; Time series classification

Hardware-unabhängige Beschleunigung von Medizinischer Bildverarbeitung mit OpenCL - Siegl, Christian; Hofmann, Hannes; Keck, Benjamin; Prümmer, Marcus; Hornegger, Joachim (Bildverarbeitung für die Medizin 2011, 2011)

HDPA: historical document processing and analysis framework - Lenc, Ladislav; Martínek, Jiří; Král, Pavel; Nicolao, Anguelos; Christlein, Vincent (Evolving Systems, 2021)
Nowadays, the accessibility of digitized historical documents is extremely important to facilitate fast and efficient retrieval of historical information and knowledge extraction from such data. To provide such functionality, it is necessary to convert document images into plain text using optical character recognition (OCR). Many OCR related methods and tools have been proposed, however, they are often too complicated for a standard user, some important parts are missing or they are not available in free versions. Therefore, this paper describes a complex and flexible web framework for historical document manipulation and analysis with the main focus on OCR. The framework contains eight modules to facilitate three main tasks: image pre-processing and segmentation, creation of data for OCR model training and the OCR itself. This framework is freely available for non commercial purposes. We have experimentally evaluated this framework on real data and we have shown that this system is efficient and can save human labour in the process of annotated data preparation. Moreover, we have reached state-of-the-art OCR results.<br
Keywords: CNN; Document analysis; Framework; Historical documents; LSTM; Neural networks; OCR

HealthStack: Providing an IoT Middleware for Malleable QoS Service Stacking for Hospital 4.0 Operating Rooms - Facco Rodrigues, Vinicius; Righi, Rodrigo R.; Andre da Costa, Cristiano; Stoffel Antunes, Rodolfo; Bazo, Rodrigo; Reis, Eduardo S.; Seewald, Lucas A.; S., Luis G.; Eskofier, Björn (IEEE Internet of Things, 2022)
Healthcare 4.0 is a new concept that originates from the evolution of hospitals due to technological advances in medical activities. Nowadays, more and more doctors and healthcare administrators require real-time data analysis obtained from sensors and surgery monitoring. Using real-time information could make the difference between death and life in such settings. Therefore, quality of service (QoS) is essential in this context because, without it, the results of the applications become unreliable. Given the background, this article proposes HealthStack, a sensor middleware model for operating room facilities. HealthStack aims at improving delay and jitter from applications and, at the same time, reducing resource consumption. Our scientific contributions are twofold: a middleware for operating rooms with automatic QoS support for real-time data transmission; and a QoS strategy based on artificial neurons to select middleware components with critical performance. We developed a prototype that uses depth cameras and ultra-wideband (UWB) real-time location systems (RTLS) to monitor workflow during surgery. The evaluation demonstrates that the strategy improves the average jitter experienced by application by 92.3%. The results also reveal a reduction of network and CPU consumption by up to 61.8% and 8.3%.
CRIS-Team Scopus Importer:2022-04-01
Keywords: Biomedical monitoring; distributed systems; healthcare.; Internet of Things; Middleware; Monitoring; Quality of service; Real-time systems; Sensors; sensors; Surgery

Heart Rate Variability During Physical Exercise - Leutheuser, Heike; Eskofier, Björn (KOPS Institutional Repository University of Konstanz, 2012)

Helfen "Fallen" bei verrauschten Daten? - Spracherkennung mit TRAPs - Maier, Andreas; Hacker, Christian; Steidl, Stefan; Nöth, Elmar (Fortschritte der Akustik: Plenarvorträge und Fachbeiträge der 31. Deutschen Jahrestagung für Akustik DAGA 2005, München, 2005)

Helical Dark-field Fiber Reconstruction - Felsner, Lina; Syben-Leisner, Christopher; Maier, Andreas; Riess, Christian (International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2021)

Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables - Martindale, Christine; Sprager, Sebastijan; Eskofier, Björn (Sensors, 2019)
Activity monitoring using wearables is becoming ubiquitous, although accurate cycle level analysis, such as step-counting and gait analysis, are limited by a lack of realistic and labeled datasets. The effort required to obtain and annotate such datasets is massive, therefore we propose a smart annotation pipeline which reduces the number of events needing manual adjustment to 14%. For scenarios dominated by walking, this annotation effort is as low as 8%. The pipeline consists of three smart annotation approaches, namely edge detection of the pressure data, local cyclicity estimation, and iteratively trained hierarchical hidden Markov models. Using this pipeline, we have collected and labeled a dataset with over 150,000 labeled cycles, each with 2 phases, from 80 subjects, which we have made publicly available. The dataset consists of 12 different task-driven activities, 10 of which are cyclic. These activities include not only straight and steady-state motions, but also transitions, different ranges of bouts, and changing directions. Each participant wore 5 synchronized inertial measurement units (IMUs) on the wrists, shoes, and in a pocket, as well as pressure insoles and video. We believe that this dataset and smart annotation pipeline are a good basis for creating a benchmark dataset for validation of other semi- and unsupervised algorithms.<br
Keywords: activity recognition; benchmark database; gait analysis; inertial measurement unit; gait phases; cyclic activities; home monitoring; smart annotation; semi-supervised learning

Hierarchical, Multi-Sensor Based Classification of Daily Life Activities: Comparison with State-of-the-Art Algorithms Using a Benchmark Dataset - Leutheuser, Heike; Schuldhaus, Dominik; Eskofier, Björn (Plos One, 2013)
Insufficient physical activity is the 4th leading risk factor for mortality. Methods for assessing the individual daily life activity (DLA) are of major interest in order to monitor the current health status and to provide feedback about the individual quality of life. The conventional assessment of DLAs with self-reports induces problems like reliability, validity, and sensitivity. The assessment of DLAs with small and light-weight wearable sensors (e.g. inertial measurement units) provides a reliable and objective method. State-of-the-art human physical activity classification systems differ in e.g. the number and kind of sensors, the performed activities, and the sampling rate. Hence, it is difficult to compare newly proposed classification algorithms to existing approaches in literature and no commonly used dataset exists. We generated a publicly available benchmark dataset for the classification of DLAs. Inertial data were recorded with four sensor nodes, each consisting of a triaxial accelerometer and a triaxial gyroscope, placed on wrist, hip, chest, and ankle. Further, we developed a novel, hierarchical, multi-sensor based classification system for the distinction of a large set of DLAs. Our hierarchical classification system reached an overall mean classification rate of 89.6% and was diligently compared to existing state-of-the-art algorithms using our benchmark dataset. For future research, the dataset can be used in the evaluation process of new classification algorithms and could speed up the process of getting the best performing and most appropriate DLA classification system.

High-Density Object Removal from Projection Images using Low-Frequency-Based Object Masking - Schwemmer, Chris; Prümmer, Marcus; Daum, Volker; Hornegger, Joachim (Bildverarbeitung für die Medizin 2010 - Algorithmen - Systeme - Anwendungen, 2010)

High-Energy X-Ray Talbot-Lau Radiography of a Human Knee - Horn, Florian; Gelse, Kolja; Jabari, Samir; Hauke, Christian; Käppler, Sebastian; Ludwig, Veronika; Meyer, Pascal; Michel, Thilo; Mohr, Jürgen; Pelzer, Georg; Rieger, Jens; Riess, Christian; et al. (Physics in Medicine and Biology, 2017)
Keywords: dark-field; high-energy; chondrocalcinosis; x-ray imaging; phase-contrast; TalbotLau imaging

Highly Efficient Self-Repairing Slippery Liquid-Infused Surface with Promising Anti-Icing and Anti-Fouling Performance - Rao, Qingqing; Lu, Yanye; Song, Lina; Hou, Yang; Zhan, Xiaoli; Zhang, Qinghua (ACS Applied Materials and Interfaces, 2021)
Smart slippery liquid-infused porous surfaces (SLIPSs) have aroused remarkable attention owing to tremendous application foreground in biomedical instruments and industry. However, challenges still remain in fabricating durable SLIPSs. In this work, a fast and highly efficient self-repairing slippery surface (SPU-60M) was fabricated based on a polyurethane membrane and silicone oil. By introducing a great quantity of reversible disulfide bonds into the polymer backbone and hydrogen bonds in the polymer interchain, this SLIPS material could be quickly repaired in 15 min with 97.8% healing efficiency. Moreover, the self-healing efficiency could be maintained at 42.75% after the 10th cutting-healing cycle. Notably, SPU-60M showed excellent self-repairing ability not only in an ambient environment but also in an underwater environment and at ultralow temperatures. Besides, the icing delay time (DT) of SPU-60M could be prolonged to 1182 s at -15 °C, and the ice adhesion strength was only 10.33 kPa at -30 °C. In addition, SPU-60M had excellent anti-fouling performance with BSA adsorption of 2.41 μg/cm2 and Escherichia coli CFU counts of 41 × 104. These findings provide a facile way to design highly efficient self-repairing SLIPSs with multifunctionality.
CRIS-Team Scopus Importer:2021-09-10
Keywords: anti-fouling; anti-icing; self-cleaning; self-repairing; SLIPS

Highly undersampled peripheral Time-of-Flight magnetic resonance angiography: optimized data acquisition and iterative image reconstruction - Hutter, Jana; Grimm, Robert; Forman, Christoph; Hornegger, Joachim; Schmitt, Peter (Magnetic Resonance Materials in Physics Biology and Medicine, 2015)
Object: The aim of this study was to investigate the acceleration of peripheral Time-of-Flight magnetic resonance angiography using Compressed Sensing and parallel magnetic resonance imaging (MRI) while preserving image quality and vascular contrast.
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.highly
Keywords: Compressed Sensing; Iterative reconstruction; Non-contrast-enhanced MRA; Peripheral angiography

Highly undersampled time resolved phase-contrast MRA with flow-adapted compressed sensing reconstruction - Hutter, Jana; Greiser, Andreas; Grimm, Robert; Forman, Christoph; Hornegger, Joachim; Schmitt, Peter (Proceedings of the 21st Annual meeting of the ISMRM, 2013)
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.highly

High Performance GPU-based Preprocessing for Time-of-Flight Imaging in Medical Applications - Wasza, Jakob; Bauer, Sebastian; Hornegger, Joachim (Bildverarbeitung für die Medizin 2011, 2011)

High Performance Iterative X-Ray CT with Application in 3-D Mammography and Interventional C-arm Imaging Systems - Keck, Benjamin (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)
Medical image reconstruction is a key component for a broad range of medical imaging technologies. For classical Computed Tomography systems the amount of measured signals per second increased exponentially over the last four decades, whereas the computational complexity of the majority of utilized algorithms has not changed significantly. A major interest and challenge is to provide optimal image quality at the fewest patient dose possible. One solution and active research field towards solving that problem, are iterative reconstruction methods. Their complexity is a multiple compared to the classical analytical methods which were used in nearly all commercially available systems. In this thesis the application of graphics cards in the field of iterative medical image reconstruction is investigated. The major contributions are the demonstrated fast implementations for off-the-shelf hardware as well as the motivation of graphics card usage in upcoming generations of medical systems. The first realization describes the implementation of a commonly used analytical cone-beam reconstruction method for C-arm CT, before covering iterative reconstruction methods. Both analytical as well as iterative reconstruction methods share the compute-intensive back-projection step. In addition iterative reconstruction methods require a forward-projection step with similarly high computational cost. The introduced Compute Unified Device Architecture (CUDA) builds the basis for the presented GPU implementation of both steps. Different realization schemes are presented by combining both steps and applying minor modifications. The implementations of the SART, SIRT as well as OS-SIRT illustrate the realization of algebraic reconstruction methods. Further, a realization for the more advanced statistical reconstruction methods is described, introducing a GPU accelerated implementation of a maximum likelihood reconstruction using a concave objective function. The achieved reconstruction performance is based on different detailed optimizations and exploitation of various technical features. In addition the performance results are evaluated for different hardware platforms - like the CPU - and for the proposed algorithms. The results implicate that for all presented reconstruction methods a significant speedup compared to a CPU realization is achieved. In example, we achieve at least a speedup factor of 10 for the presented OS-SIRT comparing a NVIDIA QuadroFX 5600 graphics card with a workstation equipped with two Intel Xeon Quad-Core E5410 processors. This is additionally supported by the comparison of the presented implementations to the CUDA alternative OpenCL underpinning the performance lead of GPUs using CUDA. A further contribution of this thesis is the exemplary clinical application of the pro- posed algorithms to two different modalities: C-arm CT and 3-D mammography. These applications demonstrate the potential and importance of GPU accelerated iterative medical image reconstruction. This thesis is concluded with a summary and an outlook on the future of GPU accelerated medical imaging processing.<br
UnivIS-Import:2016-07-26:Pub.2014.tech.IMMD.IMMD5.highpe
Keywords: CUDA; GPGPU; GPU; SART; SIRT; computed tomography; graphics cards; iterative reconstruction; maximum likelihood

High-Precision Protein-Tracking With Interferometric Scattering Microscopy - Taylor, Richard W.; Holler, Cornelia; Mahmoodabadi, Reza Gholami; Küppers, Michelle; Mirzaalian Dastjerdi, Houman; Zaburdaev, Vasily; Schambony, Alexandra; Sandoghdar, Vahid (Frontiers in Cell and Developmental Biology, 2020)
The mobility of proteins and lipids within the cell, sculpted oftentimes by the organization of the membrane, reveals a great wealth of information on the function and interaction of these molecules as well as the membrane itself. Single particle tracking has proven to be a vital tool to study the mobility of individual molecules and unravel details of their behavior. Interferometric scattering (iSCAT) microscopy is an emerging technique well-suited for visualizing the diffusion of gold nanoparticle-labeled membrane proteins to a spatial and temporal resolution beyond the means of traditional fluorescent labels. We discuss the applicability of interferometric single particle tracking (iSPT) microscopy to investigate the minutia in the motion of a protein through measurements visualizing the mobility of the epidermal growth factor receptor in various biological scenarios on the live cell.
CRIS-Team Scopus Importer:2020-11-27
Keywords: epidermal growth factor receptor; interferometric scattering microscopy; iSCAT; iSPT; live cell imaging; membrane organization; single particle tracking

High-Resolution 3D Whole-Heart Coronary MRA: A Study on the Combination of Data Acquisition in Multiple Breath-Holds and 1D Residual Respiratory Motion Compensation - Forman, Christoph; Piccini, Davide; Grimm, Robert; Hutter, Jana; Hornegger, Joachim; Zenge, Michael O. (Magnetic Resonance Materials in Physics Biology and Medicine, 2014)
Object: To study a scan protocol for coronary magnetic resonance angiography based on multiple breath-holds featuring 1D motion compensation and to compare the resulting image quality to a navigator-gated free-breathing acquisition. Image reconstruction was performed using L1 regularized iterative SENSE. Materials and methods: The effects of respiratory motion on the Cartesian sampling scheme were minimized by performing data acquisition in multiple breath-holds. During the scan, repetitive readouts through a k-space center were used to detect and correct the respiratory displacement of the heart by exploiting the self-navigation principle in image reconstruction. In vivo experiments were performed in nine healthy volunteers and the resulting image quality was compared to a navigator-gated reference in terms of vessel length and sharpness. Results: Acquisition in breath-hold is an effective method to reduce the scan time by more than 30 % compared to the navigator-gated reference. Although an equivalent mean image quality with respect to the reference was achieved with the proposed method, the 1D motion compensation did not work equally well in all cases. Conclusion: In general, the image quality scaled with the robustness of the motion compensation. Nevertheless, the featured setup provides a positive basis for future extension with more advanced motion compensation methods. © 2014 ESMRMB.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.highre
Keywords: Compressed sensing; Coronary magnetic resonance angiography; Multiple breath-holds; Respiratory motion compensation

High Resolution Iterative CT Reconstruction using Graphics Hardware - Keck, Benjamin; Hofmann, Hannes; Scherl, Holger; Kowarschik, Markus; Hornegger, Joachim (2009 IEEE Nuclear Science Symposium Conference Record, 2009)

High-speed slot-scanning radiography using small-angle tomosynthesis: Investigation of spatial resolution - Luckner, Christoph; Herbst, Magdalena; Weber, Thomas; Beister, Marcel; Ritschl, Ludwig; Kappler, Steffen; Maier, Andreas (Medical Physics, 2019)
Purpose This paper studies spatial resolution that is achievable with a fast slot-scanning tomosynthesis approach for orthopedic examinations. Hereby, we use parallel scanning motion implemented in a twin robotic x-ray system. Methods We have measured and analyzed the modulation transfer function (MTF) for various combinations of scanning speed, x-ray tube voltage, pulse length, the nominal focal spot size as well as source-to-object distances. Moreover, we present a theoretical model which describes the system in terms of the MTF. The system was equipped with newly developed linear trajectory prototypes for slot scanning. The acquired images form the basis for a small-angle tomosynthesis reconstruction. In total, three different scanning speeds (27, 14, 8 cm/s), pulse lengths (1, 2, 4 ms), tube voltages (80, 100, 120 kV), two nominal focal spot sizes (0.6, 1.0), and three source-to-object distances (950, 1050, 1150 mm) were investigated. To determine the resolution capabilities, we measured the MTF for the given parameter space. The results were then used to design a filter that yields a desired resolution in the reconstructed image. In addition, we also measured the noise power spectrum (NPS) to show the influence of the aforementioned filters on the noise distribution. Results We have shown that the presented model is in good agreement with the performed measurements. Scanning speed and pulse width have an impact on the MTF in the scanning direction. Up to a travel distance of 0.3 mm during an x-ray pulse, an isotropic resolution can be achieved. Longer pulse width or higher scanning speed cause anisotropic resolution. Moreover, it is shown that none of the investigated parameters have an influence on the MTF perpendicular to the scanning direction (slot direction). The 10% MTF value ranges between 9 and 18 lp/cm in the scanning direction and about 18 lp/cm in slot direction. Tube voltage, nominal focal spot size as well as the source-to-object distance showed no major impact on the system MTF. In terms of the anisotropic resolution capabilities, we have shown that limiting the resolution in the slot direction to obtain isotropic resolution is possible yet at the cost of an inhomogeneous noise pattern. However, maintaining the resolution in slot direction will provide a better edge response and a more homogeneous noise texture at the cost of an inhomogeneous image resolution. Conclusion We have demonstrated the feasibility of the slot-scanning technique using a twin-robotic x-ray system. Even the fastest scanning mode (27 cm/s) yields image resolution on a level that is sufficient for typical orthopedic examinations in terms of musculoskeletal (MSK) measurements. Moreover, it could be shown that the application of specifically designed target MTFs on two-dimensional x-ray images is feasible.
CRIS-Team WoS Importer:2019-11-05

Hilbert spectrum analysis for automatic detection and evaluation of Parkinson's speech - Karan, Biswajit; Sahu, Sitanshu Sekhar; Orozco Arroyave, Juan Rafael; Mahto, Kartik (Biomedical Signal Processing and Control, 2020)
Parkinson's disease (PD) is a progressive neurological disorder that mainly affects people in old age. Abnormality in the speech signals has been reported as a biomarker to detect PD. This study explores the use of Hilbert spectrum (HS) based features to model voice impairments in people affected by PD. The instantaneous energy deviation cepstral coefficient (IEDCC) is proposed. Statistical analyses show that the proposed feature is an effective and relevant biomarker for PD detection and evaluation of the dysarthria level in speech affected by PD. The capability of the proposed features to differentiate between PD and healthy people is evaluated upon five sustained vowels and ten isolated words from the standard PC-GITA database. The average accuracy of the proposed approach ranges from 82 % to 90 % with vowels, whereas for words the average accuracy ranges between 80 % and 91 %. Besides PD detection, the dysarthria level is evaluated according to the m-FDA scale. Spearman's correlation coefficients (ρ) are computed between the estimated m-FDA values and the original scores. Correlations of up to 0.75 are obtained with vowel/o/, while 0.77 is the highest correlation obtained with the word/reina/. The developed models are further validated with a separate and independent dataset. The classification accuracy in these additional recordings ranges between 50 % and 80 % with vowels and from 50 % to 82 % with words. The promising results obtained on the additional test set indicate that the proposed method is suitable to perform the automatic detection of PD speakers in real-world conditions.
CRIS-Team Scopus Importer:2020-06-30
Keywords: Dysarthria; EMD; Hilbert spectrum; IEDCC; Intrinsic mode function; m-FDA scale; Parkinson's disease

Home Assistance System for Elderly People - Ott, Stefan; Spiegl, Werner; Soutschek, Stefan; Maier, Andreas; Steidl, Stefan; Nöth, Elmar (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

How Big is Big Enough?: A Large-Scale Histological Dataset of Mitotic Figures - Bertram, Christof A.; Aubreville, Marc; Marzahl, Christian; Maier, Andreas; Klopfleisch, Robert (Bildverarbeitung für die Medizin 2020, 2020)
Quantification of mitotic figures (MF) within the tumor areas of highest mi- totic density is the most important prognostic parameter for outcome assessment of many tumor types. However, high intra- and inter-rater variability results from difficulties in individual MF identification and region of interest (ROI) se- lection due to uneven MF distribution. Deep learning-based algorithms for MF detection and ROI selection are very promising methods to overcome these lim- itations. As of today, few datasets of human mammary carcinoma are available. They provide labels only in small image sections of the whole slide image (WSI) and include up to 1,552 MF annotations. Our research group has developed a large-scale, open access dataset with annotations for MF in 32 cases of canine cutaneous mast cell tumors. Entire WSI were completely labeled by two pathologists resulting in 44,800 MF anno- tations. Of those, 5.5% were initially missed by expert WSI screening and added through a deep learning-based pipeline for identification of potential candidates. For algorithmic validation, we used a two-stage approach (RetinaNet followed by cell classificator), which yielded a F1 score of 0.820. Through the algorith- m-aided completion of the dataset we were able to increase the F1 score by 3.4 percentage points. Influence of the size of the dataset was assessed by stepwise reduction of the number of WSI and size (in high power fields, HPF) of the image sections used for training. With the number of included images, the F1 score moderately increased (3 WSI: 0.772; 6 WSI: 0.804; 12 WSI: 0.817; 21 WSI: 0.820). The size of the tumor area in training (ROI selected by an expert) had significant effects on the F1 score (5 HPF: 0.583; 10 HPF: 0.676; 50 HPF: 0.770; complete WSI: 0.820), which was determined in entire WSI of the test set. We emphasize the benefit of appropriate dataset size and complete WSI labeling.
Keywords: mitotic figures; tumor diagnostics; open data; big data; canine cutaneous mast cell tumor

How Many Labellers? Modelling Inter-Labeller Agreement and System Performance for the Automatic Assessment of Non-Native Prosody - Hönig, Florian Thomas; Batliner, Anton; Weilhammer, Karl; Nöth, Elmar (Proceedings of the INTERSPEECH 2010 Satellite Workshop on Second Language Studies: Acquisition, Learning, Education and Technology (L2WS)/Speech and Language Technology in Education (SLaTE), 2010)

How Many Labellers Revisited - Naives, Experts, and Real Experts - Hönig, Florian Thomas; Batliner, Anton; Nöth, Elmar (Proceedings of the ISCA Special Interest Group on Speech and Language Technology in Education, 2011)

How Many Speakers, How Many Texts - The Automatic Assessment of Non-Native Prosody - Hönig, Florian Thomas; Batliner, Anton; Nöth, Elmar (Workshop on Speech and Language Technology in Education, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.howman_9

How to add word classes to the Kaldi speech recognition toolkit - Horndasch, Axel; Kaufhold, Caroline; Nöth, Elmar (Text, Speech, and Dialogue. TSD 2016, 2016)
The paper explains and illustrates how the concept of word classes can be added to the widely used open-source speech recognition toolkit Kaldi. The suggested extensions to existing Kaldi recipes are limited to the word-level grammar (G) and the pronunciation lexicon (L) models. The implementation to modify the weighted finite state transducers employed in Kaldi makes use of the OpenFST library. In experiments on small and mid-sized corpora with vocabulary sizes of 1.5 K and 5.5 K respectively a slight improvement of the word error rate is observed when the approach is tested with (hand-crafted) word classes. Furthermore it is shown that the introduction of sub-word unit models for open word classes can help to robustly detect and classify out-of-vocabulary words without impairing word recognition accurac
Keywords: OOV detection and classification; Kaldi speech recognition toolkit; Word classes

How we learned to stop worrying about content and love the metadata - Gloe, Thomas; Kirchner, Matthias; Riess, Christian (2013)

How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies - Tayebi Arasteh, Soroosh; Monajem, Mehrpad; Christlein, Vincent; Heinrich, Philipp; Nicolao, Anguelos; Boldaji, Hamidreza Naderi; Lotfinia, Mahshad; Evert, Stephanie (2021 IEEE 15th International Conference on Semantic Computing (ICSC), 2021)
Twitter sentiment analysis, which often focuses on predicting the polarity of tweets, has attracted increasing attention over the last years, in particular with the rise of deep learning (DL). In this paper, we propose a new task: predicting the predominant sentiment among (first-order) replies to a given tweet. Therefore, we created RETwEET, a large dataset of tweets and replies manually annotated with sentiment labels. As a strong baseline, we propose a two-stage DL-based method: first, we create automatically labeled training data by applying a standard sentiment classifier to tweet replies and aggregating its predictions for each original tweet; our rationale is that individual errors made by the classifier are likely to cancel out in the aggregation step. Second, we use the automatically labeled data for supervised training of a neural network to predict reply sentiment from the original tweets. The resulting classifier is evaluated on the new ReTweeT dataset, showing promising results, especially considering that it has been trained without any manually labeled data. Both the dataset and the baseline implementation are publicly available.<br
Keywords: Deep Learning; Sentiment Analysis; Tweet Reply Prediction; Natural Language Processing

Human Authentication Implemented for Mobile Applications Based on ECG-Data Acquired from Sensorized Garments - Tantinger, Daniel; Zrenner, Markus; Lang, Nadine; Leutheuser, Heike; Eskofier, Björn; Weigand, Christian; Struck, Matthias (Computing in Cardiology 2015, 2015)

Human beings live longer self-determined Fit4Life - Hornegger, Joachim; Nöth, Elmar; Kornhuber, Johannes (2008)

Human Machine Interface for Elderly People - Kollorz, Eva Nicole Karin; Penne, Jochen; Hornegger, Joachim; Kornhuber, Johannes (Tagungsband zum 1. deutschen AAL-Kongress, 2008)

Hybrid prospective and retrospective head motion correction to mitigate cross-calibration errors - Aksoy, Murat; Forman, Christoph; Straka, Matus; Cukur, Tolga; Hornegger, Joachim; Bammer, Roland (Magnetic Resonance in Medicine, 2012)
Utilization of external motion tracking devices is an emerging technology in head motion correction for MRI. However, cross-calibration between the reference frames of the external tracking device and the MRI scanner can be tedious and remains a challenge in practical applications. In this study, we present two hybrid methods, both of which combine prospective, optical-based motion correction with retrospective entropy-based autofocusing to remove residual motion artifacts. Our results revealed that in the presence of cross-calibration errors between the optical tracking device and the MR scanner, application of retrospective correction on prospectively corrected data significantly improves image quality. As a result of this hybrid prospective and retrospective motion correction approach, the requirement for a high-quality calibration scan can be significantly relaxed, even to the extent that it is possible to perform external prospective motion tracking without any prior cross-calibration step if a crude approximation of cross-calibration matrix exists. Moreover, the motion tracking system, which is used to reduce the dimensionality of the autofocusing problem, benefits the retrospective approach at the same time. Copyright © 2011 Wiley Periodicals, Inc.

Hybrid Prospective & Retrospective Head Motion Correction System to Mitigate Cross-Calibration Errors - Aksoy, Murat; Forman, Christoph; Holdsworth, Samantha; Skare, Stefan; Hornegger, Joachim; Bammer, Roland (Proceedings of the 18th Annual Meeting of ISMRM & ESMRMB, 2010)

Hybrid RGB/Time-of-Flight Sensors in Minimally Invasive Surgery - Haase, Sven (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2016)
Nowadays, minimally invasive surgery is an essential part of medical interventions. In a typical clinical workflow, procedures are planned preoperatively with 3-dimensional (3-D) computed tomography (CT) data and guided intraoperatively by 2-dimensional (2-D) video data. However, accurate preoperative data acquired for diagnose and operation planning is often not feasible to deliver valid information for orientation and decisions within the intervention due to issues like organ movements and deformations. Therefore, innovative interventional tools are required to aid the surgeon and improve safety and speed for minimally invasive procedures. Augmenting 2-D color information with 3-D range data allows to use an additional dimension for developing novel surgical assistance systems. Here, Time-of-Flight (ToF) is a promising low-cost and real-time capable technique that exploits reflected near-infrared light to estimate the radial distances of points in a dense manner. This thesis covers the entire implementation pipeline of this new technology into a clinical setup, starting from calibration to data preprocessing up to medical applications. The first part of this work covers a novel automatic calibration scheme for hybrid data acquisition based on barcodes as recognizable feature points. The common checkerboard pattern is overlaid by a marker that includes unique 2-D barcodes. The prior knowledge about the barcode locations allows to detect only valid feature points for the calibration process. Based on detected feature points seen from different points of view a sensor data fusion for the complementary modalities is estimated. The proposed framework achieved subpixel reprojection errors and barcode identification rates above 90% for both the ToF and the RGB sensor. As range data of low-cost ToF sensors is typically error-prone due to different issues, e.g. specular reflections and low signal-to-noise ratio (SNR), preprocessing is a mandatory step after acquiring photometric and geometric information in a common setup. This work proposes the novel concept of hybrid preprocessing to exploit the benefits of one sensor to compensate for weaknesses of the other sensor. Here, we extended established preprocessing concepts to handle hybrid image data. In particular, we propose a nonlocal means filter that takes an entire sequence of hybrid image data into account to improve the mean absolute error of range data by 20%. A different concept estimates a high-resolution range image by means of super-resolution techniques that takes advantage of geometric displacements by the optical system. This technique improved the mean absolute error only by 12% but improved the spatial resolution simultaneously. In oder to tackle the issue of specular highlights that cause invalid range data, we propose a multi-view scheme for highlight correction. We replace invalid range data at a specific viewpoint with valid data of another viewpoint. This reduced the mean absolute error by 33% compared to a basic interpolation. Finally, this thesis introduces three novel medical applications that benefit from hybrid 3-D data. First, a collision avoidance module is introduced that exploits range data to ensure a safety margin for endoscopes within a narrow operation side. Second, an endoscopic tool localization framework is described that exploits hybrid range data to improve tool localization and segmentation. Third, a data fusion framework is proposed to extend the narrow field of view and reconstruct the entire situs. This work shows that hybrid image data of ToF and RGB sensors allows to improve image based assistance systems with more reliable and intuitive data for better guidance within a minimally invasive intervention.<br
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.hybrid
Keywords: ToF-Kamera; Endoskopie; Minimal-invasive Chirurgie

Hybrid Training Data for Historical Text OCR - Martínek, Jiří; Lenc, Ladislav; Král, Pavel; Nikolaou, Angelos; Christlein, Vincent (2019 International Conference on Document Analysis and Recognition (ICDAR), 2019)
Current optical character recognition (OCR) systems commonly make use of recurrent neural networks (RNN) that process whole text lines. Such systems avoid the task of character segmentation necessary for character-based approaches. A disadvantage of this approach is a need of a large amount of annotated data. This can be solved by sing generated synthetic data instead of costly manually annotated ones. Unfortunately, such data is often not suitable for historical documents particularly for quality reasons. This work presents a hybrid approach for generating annotated data for OCR at a low cost. We first collect a small dataset of isolated characters from historical document images. Then, we generate historical looking text lines from the generated characters. Another contribution lies in the design and implementation of an OCR system based on a convolutional-LSTM network. We first pre-train this system on hybrid data. Afterwards, the network is fine-tuned with real printed text lines. We demonstrate that this training strategy is efficient for obtaining state-of-the-art results. We also show that the score of the proposed system is comparable or even better in comparison to several state-of-the-art system

Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings - Davari, Amirabbas; Sakaltras, Nikolaos; Haeberle, Armin; Vesal, Sulaiman; Christlein, Vincent; Maier, Andreas; Riess, Christian (International Conference on Image Processing (ICIP), 2018)
Old master drawings were mostly created step by step in several layers using different materials. To art historians and restorers, examination of these layers brings various insights into the artistic work process and helps to answer questions about the object, its attribution and its authenticity. However, these layers typically overlap and are oftentimes difficult to differentiate with the unaided eye. For example, a common layer combination is red chalk under ink. In this work, we propose an image processing pipeline that operates on hyperspectral images to separate such layers. In particular, we propose to use two descriptors in hyperspectral historical document analysis, namely hyper-hue and extended multi-attribute profile (EMAP). We show that hyperspectral images enable better layer separation than RGB images, and that spectral focus stacking is an important preprocessing step towards that goal. Our comparative results with other features underline the efficacy of the three proposed improvements.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.hyperh_9
Keywords: EMAP; Hyper-Hue; Layer Separation; Old Master Drawing; Spectral Focus Stacking

Hypernasality in Speech of Children with Cleft Lip and Palate: Automatic Evaluation - Maier, Andreas; Schuster, Maria; Haderlein, Tino; Nöth, Elmar (Proceedings of the 8th International Seminar on Speech Production 2008, 2008)

ICA-based Reduction of Electromyogenic Artifacts in EEG Data: Comparison With and Without EMG Data - Gabsteiger, Florian; Leutheuser, Heike; Reis, Pedro; Lochmann, Matthias; Eskofier, Björn (IEEE EMBC 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.icabas