Automatic Phonetization-based Statistical Linguistic Study of Standard Arabic - Sindran, Fadi; Mualla, Firas; Haderlein, Tino; Daqrouq, Khaled; Nöth, Elmar (International Journal of Computational Linguistics, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.automa_8

Automatic Plane Adjustment in Surgical Cone Beam CT-volumes - Martín Vicario, Celia; Kordon, Florian Johannes; Denzinger, Felix; Weiten, Markus; Thomas, Sarina; Kausch, Lisa; Franke, Jochen; Keil, Holger; Maier, Andreas; Kunze, Holger (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Automatic planning of atrial fibrillation ablation lines using landmark-constrained nonrigid registration - Koch, Martin; Brost, Alexander; Bourier, Felix; Hornegger, Joachim; Strobel, Norbert (Journal of Medical Imaging, 2014)
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.automa_7

Automatic Preparation of Standard Arabic Phonetically Rich Written Corpora with Different Linguistic Units - Sindran, Fadi; Mualla, Firas; Haderlein, Tino; Daqrouq, Khaled; Nöth, Elmar (Proc. Text, Speech and Dialogue; 20th International Conference, TSD 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_11
Keywords: Allophones; Diphones; Linguistic content; Phonetically rich; SA written corpora

Automatic Pronunciation Assessment of Non-native English Based on Phonological Analysis - Rios-Urrego, Cristian David; Escobar-Grisales, Daniel; Moreno-Acevedo, Santiago Andres; Pérez Toro, Paula Andrea; Nöth, Elmar; Orozco Arroyave, Juan Rafael (Springer Science and Business Media Deutschland GmbH, 2023)
Keywords: English; Pronunciation assessment; Phonological Analysis; Speech

Automatic Pronunciation Scoring of Words and Sentences Independent from the Non-Native's First Language - Cincarek, Tobias; Gruhn, Rainer; Hacker, Christian; Nöth, Elmar; Nakamura, Satoshi (Computer Speech and Language, 2009)

Automatic Quantification of Speech Intelligibility in Patients after Treatment for Oral Squamous Cell Carcinoma - Stelzle, Florian; Maier, Andreas; Nöth, Elmar; Bocklet, Tobias; Knipfer, Christian; Schuster, Maria; Neukam, Friedrich Wilhelm; Nkenke, Emeka (Journal of Maxillofacial and Oral Surgery, 2011)

Automatic Quantification of Speech Intelligibility of Adults with Oral Squamous Cell Carcinoma - Windrich, Martin; Maier, Andreas; Kohler, Regina; Nöth, Elmar; Nkenke, Emeka; Eysholdt, Ulrich; Schuster, Maria (Folia Phoniatrica Et Logopaedica, 2008)

Automatic Rating of Hoarseness by Text-based Cepstral and Prosodic Evaluation - Haderlein, Tino; Moers, Cornelia; Möbius, Bernd; Nöth, Elmar (Proc. Text, Speech and Dialogue; 15th International Conference, TSD 2012, 2012)

Automatic Recognition and Evaluation of Tracheoesophageal Speech - Haderlein, Tino; Steidl, Stefan; Nöth, Elmar; Rosanowski, Frank; Schuster, Maria (Proc. Text, Speech and Dialogue; 7th International Conference, TSD 2004, 2004)
Tracheoesophageal (TE) speech is a possibility to restore the ability to speak after laryngectomy, i.e. the removal of the larynx. TE speech often shows low audibility and intelligibility which also makes it a challenge to automatic speech recognition. We improved the recognition results by adapting a speech recognizer trained on normal, non-pathologic voices to single TE speakers by unsupervised HMM interpolation. In speech rehabilitation the patient’s voice quality has to be evaluated. As no objective classification means exists until now and an automation of this procedure is desirable we performed initial experiments for automatic evaluation of the intelligibility. We compared scoring results for TE speech from five experienced raters with the word accuracy from different types of speech recognizers. Correlation coefficients of about -0.8 are promising for future work.

Automatic Recognition of Parkinsons Disease Using Surface Electromyography During Standardized Gait Tests - Kugler, Patrick; Jaremenko, Christian; Schlachetzki, Johannes; Winkler, Jürgen; Klucken, Jochen; Eskofier, Björn (Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, 2013)
Diagnosis and severity staging of Parkinsons disease (PD) relies mainly on subjective clinical examination. To better monitor disease progression and therapy success in PD patients, new objective and rater independent parameters are required. Surface electromyography (EMG) during dynamic movements is one possible modality. However, EMG signals are often difficult to understand and interpret clinically. In this study pattern recognition was applied to find suitable parameters to differentiate PD patients from healthy controls. EMG signals were recorded from 5 patients with PD and 5 younger healthy controls, while performing a series of standardized gait tests. Wireless surface electrodes were placed bilaterally on tibialis anterior and gastrocnemius medialis and lateralis. Accelerometers were positioned on both heels and used for step segmentation. Statistical and frequency features were extracted and used to train a Support Vector Machine classifier. Sensitivity and specificity were high at 0.90 using leave-one-subject-out cross-validation. Feature selection revealed kurtosis and mean frequency as best features, with a significant difference in kurtosis (p=0.013). Evaluated on a bigger population, this could lead to objective diagnostic and staging tools for P

Automatic Removal of Externally Attached Fiducial Markers in Cone Beam C-arm CT - Berger, Martin; Forman, Christoph; Schwemmer, Chris; Choi, Jang-Hwan; Müller, Kerstin; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin 2014, 2014)
In computed tomography fiducial markers are frequently used to obtain accurate point correspondences for further processing. These markers typically cause metal artefacts, decreasing image quality of the subsequent reconstruction and are therefore often removed from the projection data. The placement of such markers is usually done on a surface, separating two materials, e.g. skin and air. Hence, a correct restoration of the occluded area is difficult. In this work six state-of-the-art interpolation techniques for the removal of high-density fiducial markers from cone-beam CT projection data are compared. We conducted a qualitative and quantitative evaluation for the removal of such markers and the ability to reconstruct the adjoining edge. Results indicate that an iterative spectral deconvolution is best suited for this application, showing promising results in terms of edge, as well as noise restoration.
UnivIS-Import:2015-07-08:Pub.2014.tech.IMMD.IMMD5.automa_5
Keywords: GRK-1773; Interpolation; C-arm CT; Metal Artifact

Automatic Robust Rule-Based Phonetization of Standard Arabic - Sindran, Fadi; Mualla, Firas; Bobzin, Katharina; Nöth, Elmar (Springer-verlag, 2015)
Phonetization is the process of encoding language sounds using phonetic symbols. It is used in many natural language processing tasks such as speech processing, speech synthesis, and computer-aided pronunciation assessment. A common phonetization approach is the use of letter-to-sound rules developed by linguists for the transcription form orthography to sound. In this paper, we address the problem of rule-based phonetization of standard Arabic. The paper contributions can be summarized as follows: 1) Discussing the transcription rules of standard Arabic which were used in literature on the phonemic and phonetic levels. 2) Important improvements of these rules were suggested and the resulting rules set was tested on large datasets. 3) We present a reliable automatic phonetic transcription of standard Arabic on five levels: phoneme, allophone, syllable, word, and sentence. An encoding which covers all sounds of standard Arabic is proposed and several pronunciation dictionaries were automatically generated. These dictionaries were manually verified yielding an accuracy of 100% with standard Arabic texts that do not contain dates, numbers, acronyms, abbreviations, and special symbols. They are available for research purposes along with the software package which performs the automatic transcription.
Keywords: Standard Arabic; Phonetic transcription; Pronunciation dictionaries; Transcription rules

Automatic Scoring of the Intelligibility in Patients with Cancer of the Oral Cavity - Maier, Andreas; Schuster, Maria; Batliner, Anton; Nöth, Elmar; Nkenke, Emeka (Interspeech 2007, 2007)

Automatic Segmentation of the Optic Radiation using DTI in Glaucoma Patients - El-Rafei, Ahmed Mohamed Ibrahim; Hornegger, Joachim; Engelhorn, Tobias; Dörfler, Arnd; Wärntges, Simone; Michelson, Georg (Computational Vision and Medical Image Processing - VipIMAGE 2009, 2009)

Automatic Segmentation of the Optic Radiation Using DTI in Healthy Subjects and Patients with Glaucoma - El-Rafei, Ahmed Mohamed Ibrahim; Engelhorn, Tobias; Wärntges, Simone; Dörfler, Arnd; Hornegger, Joachim; Michelson, Georg (Computational Vision and Medical Image Processing, 2011)

Automatic speech recognition for edentulous speakers with insufficient dentures - Bocklet, Tobias; Stelzle, Florian; Haderlein, Tino; Nöth, Elmar (Proceedings of the 9th International Conference on Advances in Quantitative Laryngology, Voice and Speech Research, 2010)

Automatic Speech Recognition Systems for the Evaluation of Voice and Speech Disorders in Head and Neck Cancer - Maier, Andreas; Haderlein, Tino; Stelzle, Florian; Nöth, Elmar; Nkenke, Emeka; Rosanowski, Frank; Schützenberger, Anne; Schuster, Maria (EURASIP Journal on Audio, Speech, and Music Processing, 2010)

Automatic Stuttering Recognition using Hidden Markov Models - Nöth, Elmar; Niemann, Heinrich; Haderlein, Tino; Decher, Michael; Eysholdt, Ulrich; Rosanowski, Frank; Wittenberg, Thomas (Proc. Int. Conf. on Spoken Language Processing, 2000)

Automatic sub-volume registration by probabilistic random search - Han, Jingfeng; Qiao, Min; Hornegger, Joachim; Kuwert, Torsten; Bautz, Werner; Römer, Wolfgang (Medical Imaging 2006: Image Processing, 2006)
Registration of an individual's image data set to an anatomical atlas provides valuable information to the physician. In many cases, the individual image data sets are partial data, which may be mapped to one part or one organ of the entire atlas data. Most of the existing intensity based image registration approaches are designed to align images of the entire view. When they are applied to the registration with partial data, a manual pre-registration is usually required. This paper proposes a fully automatic approach to the registration of the incomplete image data to an anatomical atlas. The spatial transformations are modelled as any parametric functions. The proposed method is built upon a random search mechanism, which allows to find the optimal transformation randomly and globally even when the initialization is not ideal. It works more reliably than the existing methods for the partial data registration because it successfully overcomes the local optimum problem. With appropriate similarity measures, this framework is applicable to both mono-modal and multi-modal registration problems with partial data. The contribution of this work is the description of the mathematical framework of the proposed algorithm and the implementation of the related software. The medical evaluation on the MRI data and the comparison of the proposed method with different existing registration methods show the feasibility and superiority of the proposed method.

Automatic thrombus detection in non-enhanced computed tomography images in patients with acute ischemic stroke - Löber, Patrick; Stimpel, Bernhard; Syben-Leisner, Christopher; Maier, Andreas; Ditt, Hendrik; Schramm, Peter; Raczkowski, Boy; Kemmling, Andre (Eurographics Proceedings 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_9

Automatic Unstained Cell Detection in Bright Field Microscopy - Mualla, Firas (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2016)
Bright field microscopy is preferred over other microscopic imaging modalities whenever ease of implementation and minimization of expenditure are main concerns. This simplicity in hardware comes at the cost of image quality yielding images of low contrast. While staining can be employed to improve the contrast, it may complicate the experimental setup and cause undesired side effects on the cells. In this thesis, we tackle the problem of automatic cell detection in bright field images of unstained cells. The research was done in context of the interdisciplinary research project COSIR. COSIR aimed at developing a novel microscopic hardware having the following feature: the device can be placed in an incubator so that cells can be cultivated and observed in a controlled environment. In order to cope with design difficulties and manufacturing costs, the bright field technique was chosen for implementing the hardware. The contributions of this work are briefly outlined in the text which follows. An automatic cell detection pipeline was developed based on supervised learning. It employs Scale Invariant Feature Transform (SIFT) keypoints, random forests, and agglomerative hierarchical clustering (AHC) in order to reliably detect cells. A keypoint classifier is first used to classify keypoints into cell and background. An intensity profile is extracted between each two nearby cell keypoints and a profile classifier is then utilized to classify the two keypoints whether they belong to the same cell (inner profile) or to different cells (cross profile). This two-classifiers approach was used in the literature. The proposed method, however, compares to the state-of-the-art as follows: 1) It yields high detection accuracy (at least 14% improvement compared to baseline bright field methods) in a fully-automatic manner with short runtime on the low-contrast bright field images. 2) Adaptation of standard features in literature from being pixel-based to adopting a keypoint-based extraction scheme: this scheme is sparse, scale-invariant, orientation-invariant, and feature parameters can be tailored in a meaningful way based on a relevant keypoint scale and orientation. 3) The pipeline is highly invariant with respect to illumination artifacts, noise, scale and orientation changes. 4) The probabilistic output of the profile classifier is used as input for an AHC step which improves detection accuracy. A novel linkage method was proposed which incorporates the information of SIFT keypoints into the linkage. This method was proved to be combinatorial, and thus, it can be computed efficiently in a recursive manner. Due to the substantial difference in contrast and visual appearance between suspended and adherent cells, the above-mentioned pipeline attains higher accuracy in separate learning of suspended and adherent cells compared to joint learning. Separate learning refers to the situation when training and testing are done either only on suspended cells or only on adherent cells. On the other hand, joint learning refers to training the algorithm to detect cells in images which contain both suspended and adherent cells. Since these two types of cells coexist in cell cultures with shades of gray between the two terminal cases, it is of practical importance to improve joint learning accuracy. We showed that this can be achieved using two types of phase-based features: 1) physical light phase obtained by solving the transport of intensity equation, 2) monogenic local phase obtained from a low-passed axial derivative image. In addition to the supervised cell detection discussed so far, a cell detection approach based on unsupervised learning was proposed. Technically speaking, supervised learning was utilized in this approach as well. However, instead of training the profile classifier using manually-labeled ground truth, a self-labeling algorithm was proposed with which ground truth labels can be automatically generated from cells and keypoints in the input image itself. The algorithm learns from extreme cases and applies the learned model on the intermediate ones. SIFT keypoints were successfully employed for unsupervised structure-of-interest measurements in cell images such as mean structure size and dominant curvature direction. Based on these measurements, it was possible to define the notion of extreme cases in a way which is independent from image resolution and cell type.<br
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.automa_78
Keywords: classification; clustering; computer vision; image processing; pattern recognition; random forests

Automatic Vertebrae Segmentation in Fluoroscopic Images for Electrophysiology - Reiml, Sabrina; Kurzendorfer, Tanja; Toth, Daniel; Mountney, Peter; Steidl, Stefan; Brost, Alexander; 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.automa_4

Automatic Writer Identification in Medieval Papal Charters - Christlein, Vincent; Angelopoulou, Elli (Papsturkunden des frühen und hohen Mittelalters, 2015)
UnivIS-Import:2017-01-09:Pub.2015.tech.IMMD.IMMD5.automa_6

Automatische Bestimmung der Verständlichkeit expressiver Sprache - Haderlein, Tino; Nöth, Elmar; Döllinger, Michael (43. Jahrestagung der Deutschen Gesellschaft für Medizinische Physik, 2012)

Automatische Bewertung der Nasalitaet von Kindersprache - Maier, Andreas; Nöth, Elmar; Wohlleben, Ulrike; Eysholdt, Ulrich; Schuster, Maria (Aktuelle phoniatrische Aspekte, 2007)

Automatische internetbasierte Evaluation der Verständlichkeit - Maier, Andreas; Haderlein, Tino; Hacker, Christian; Nöth, Elmar; Rosanowski, Frank; Eysholdt, Ulrich; Schuster, Maria (Aktuelle phoniatrisch-pädaudiologische Aspekte 2006, 2006)

Automatische prosodische Stimmqualitätsbewertung aus Textaufnahmen bei großem Mikrofonabstand - Haderlein, Tino; Schützenberger, Anne; Döllinger, Michael; Nöth, Elmar (Aktuelle phoniatrisch-pädaudiologische Aspekte 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.automa_4
Keywords: Sprache

Automatische Qualitätsmessung von Retina-Fundusbildern - Paulus, Jan; Bock, Rüdiger; Meier, Jörg; Michelson, Georg; Hornegger, Joachim (Bildverarbeitung für die Medizin 2009 - Algorithmen, Systeme, Anwendungen, 2009)

Automatische Stimmanalyse nach Larynxteilresektion mithilfe akustischer Sprechermodellierung - Haderlein, Tino; Bocklet, Tobias; Hönig, Florian Thomas; Nöth, Elmar; Rosanowski, Frank (Aktuelle phoniatrisch-pädaudiologische Aspekte 2009, 2009)

Automatische Stottererkennung und Klassifikation mit Hilfe von Hidden-Markov- Modellen - Haderlein, Tino; Wittenberg, Thomas; Decher, Michael; Nöth, Elmar (Aktuelle phoniatrisch-pädaudiologische Aspekte 2000/2001, 2001)

Automatische Verständlichkeitsanalyse bei Kindern mit Lippen- und Lippenkieferspalten mittels Spracherkennung - Maier, Andreas; Nöth, Elmar; Eysholdt, Ulrich; Schuster, Maria (Aktuelle phoniatrisch-pädaudiologische Aspekte 2009, 2009)

Automatische Verständlichkeitsbewertung von Telefonaufnahmen Larynxteilresezierter mittels prosodischer Analyse - Haderlein, Tino; Maier, Andreas; Nöth, Elmar; Rosanowski, Frank; Eysholdt, Ulrich (Aktuelle phoniatrisch-pädaudiologische Aspekte 2010, 2010)

Automatisierung des Postlaryngektomie-Telefontests - Haderlein, Tino; Riedhammer, Korbinian Thomas; Maier, Andreas; Nöth, Elmar; Eysholdt, Ulrich; Rosanowski, Frank (HNO, 2009)

Automatisierung des Postlaryngektomie-Telefontests (PLTT) - Haderlein, Tino; Eysholdt, Ulrich; Riedhammer, Korbinian Thomas; Nöth, Elmar; Rosanowski, Frank (Aktuelle phoniatrisch-pädaudiologische Aspekte 2007, 2007)

Autonomous Approach and Landing for a Low-Cost Quadrotor Using Monocular Cameras - Deitsch, Sergiu; Gallwitz, Florian; Angelopoulou, Elli (Computer Vision - ECCV 2014 Workshops, 2014)
In this paper, we propose a monocular vision system for approach and landing using a low-cost micro aerial vehicle (MAV). The system enables an off-the-shelf Parrot AR.Drone 2.0 quadrotor MAV to autonomously detect a landpad, approach it, and land on it. Particularly, we exploit geometric properties of a circular landpad marker in order to estimate the exact flight distance between the quadrotor and the landing spot. We then employ monocular simultaneous localization and mapping (SLAM) to fly towards the landpad while accurately following a trajectory. Notably, our system does not require the landpad to be located directly underneath the MAV.<br
UnivIS-Import:2017-12-18:Pub.2014.tech.IMMD.IMMD5.autono_1
Keywords: Approach and landing; Ellipse detection; Conic sections; Pose estimation; MAV; PTAM; SLAM; GRK-1773

Auto-tuned Path-based Iterative Reconstruction (aPBIR) for X-ray Computed Tomography - Wu, Meng; Maier, Andreas; Xia, Yan; Fahrig, Rebecca (CT-Meeting 2016 Proceedings (The 4th International Meeting on Image Formation in X-Ray Computed Tomography), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.autotu_1

A variational approach to spatially dependent non-rigid registration - Jäger, Florian; Han, Jingfeng; Hornegger, Joachim; Kuwert, Torsten (Medical Imaging 2006: Image Processing, 2006)
In this paper we propose a new method for non-rigid registration of PET/CT datasets incorporating prior knowledge about the rigidity of regions within the PET volumes into the matching process. State-of-the-art medical image registration approaches usually assume that the whole image domain is associated with a homogeneous deformation property, thus bone structure and soft tissue have the same stiffness, for instance. This assumption, however, is invalid in the majority of cases. In many applications the deformation properties can be estimated automatically by a segmentation step, beforehand. The presented non-rigid registration method integrates knowledge about the tissue directly into the deformation field computation. For this reason, no additional post-processing steps, like filtering of the deformation field, are required. To integrate the tissue constraints the regularizer is replaced by a novel spatially dependent smoother. Dependent on the location within the image, the smoother is able to explicitly adjust the rigidity. Thus, different tissue classes can be treated in the registration process. To pass the stiffness coefficients to the algorithm an additional mask image is used. The registration results are illustrated on synthetic data first to give a good intuition about the effectiveness of the proposed method. Finally, we illustrate the improvement of the registration using real clinical data. It is shown that the mono-modal registration of PET images yields more reasonable results using a spatially dependent regularizer constraining the deformations of regions with high tracer concentration than using a normal curvature regularizer. Furthermore, the method is evaluated on multi-modal PET/CT registration problems.

A Variational Framework for Joint Image Registration, Denoising and Edge Detection - Han, Jingfeng; Berkels, Benjamin; Rumpf, Martin; Hornegger, Joachim; Droske, Marc; Fried, Michael; Scorzin, Jasmin; Schaller, Carlo (Bildverarbeitung für die Medizin 2006, 2006)

A Video Guided Solution for Screw Insertion in Orthopedic Plate Fixation - Magaraggia, Jessica; Kleinszig, Gerhard; Graumann, Rainer; Angelopoulou, Elli; Hornegger, Joachim (SPIE Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 2013)
In orthopedic and trauma surgery, metallic plates are used for reduction and fixation of bone fractures. In clinical practice, the intra-operative planning for screw fixation is usually based on fluoroscopic images. Screw fixation is then performed on a free-hand basis. As such, multiple attempts may be required in order to achieve an optimal positioning of the fixing screws. To help the physician insert the screws in accordance to the planned position, we propose a method for screw insertion guidance. Our approach uses a small video camera, rigidly placed on the drill, and a set of small markers that are rigidly fixed on a variable angle drill sleeve. In order to investigate the achievable accuracy of our setup, we simulate the estimation of the drill bit position under two different marker arrangements, planar and 3D, and different noise levels. Furthermore, we motivate our choices for marker design and position given the limited space available for marker positioning, the requirement for accurate position estimation of the drill bit and the illumination changes that could a affect the surgical site. We also describe our proposed marker detection and tracking pipeline. Our simulation results let us conclude that we can achieve an accuracy of 1 ° and 1mm in the estimation of angular orientation and tip position of the drill bit respectively, provided that we have accurate marker detection. © 2013 SPIE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.avideo_5
Keywords: Video-Guided Method; Marker Detection; Orthopedic and Trauma Surgery; Locking Screw Plates

A Virtual Environment Based Evaluating System for Goalkeepers Stereopsis Performance in Soccer - Paulus, Jan; Hornegger, Joachim; Schmidt, Michael; Eskofier, Björn; Michelson, Georg (Sportinformatik 2012, 2012)

A wearable real-time activity tracker - Jensen, Ulf; Leutheuser, Heike; Hofmann, Steffen; Schüpferling, Benno; Suttner, Gerald; Seiler, Kristin; Kornhuber, Johannes; Eskofier, Björn (Biomedical Engineering Letters, 2015)
Keywords: activity tracking; wearable computing; MET levels; accelerometers; mental health

A Wearable Sensor System for Sports and Fitness Applications - Blank, Peter; Kugler, Patrick; Schlarb, Heiko; Eskofier, Björn (Book of Abstracts of the 19th Annual Congress of the European College of Sport Science, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.aweara

A workflow for patient-individualized virtual angiogram generation based on CFD simulation - Endres, Jürgen; Kowarschik, Markus; Redel, Thomas; Sharma, Puneet; Mihalef, Viorel; Hornegger, Joachim; Dörfler, Arnd (Computational and Mathematical Methods in Medicine, 2012)

Axially Extended-Volume C-Arm CT Using a Reverse Helical Trajectory in the Interventional Room - Yu, Zhicong; Maier, Andreas; Lauritsch, Günter; Vogt, Florian; Schönborn, Manfred; Köhler, Christoph; Hornegger, Joachim; Noo, Frédéric (IEEE Transactions on Medical Imaging, 2015)
UnivIS-Import:2015-03-09:Pub.2015.tech.IMMD.IMMD5.axiall

Background Matters: A Correction Scheme for Dynamic Iterative CBCT with Limited Grid Size - Taubmann, Oliver; Lauritsch, Günter; Krings, Gregor; Maier, Andreas (Proceedings of Fully3D'17, 2017)
UnivIS-Import:2017-11-06:Pub.2017.tech.IMMD.IMMD5.backgr_1

Ball impact localization on table tennis rackets using piezo-electric sensors - Blank, Peter; Kautz, Thomas; Eskofier, Björn (Proceedings of the 2016 ACM International Symposium on Wearable Computers, 2016)
In this paper, we present an approach for a ball impact localization on table tennis rackets using piezo-electric sensors. Three sensors were used as vibration sensors at the outer edge of the racket. We analyzed ball impact vibration appearances and measured time differences of arriving wave fronts for each sensor pair. A study was conducted comprising six different racket-rubber combinations. Firstly, we calculated a time difference distribution model based on training sets of impact data constructed by a grid of measuring points. Secondly, this model was used to estimate the impact position of test data sets using three different methods. Best performance for each racket-rubber combination was achieved with a linear regression based method yielding a RMSE in x-direction (transversal racket direction) of 22.1 mm and a RMSE of 19.8 mm in y-direction (longitudinal racket direction). The measuring system is small enough to be completely invisibly integrated into table tennis rackets. This contribution is the first attempt to address a ball impact localization system for table tennis rackets that has the potential to be used in real-world training exercises and competitions to assist table tennis players during matches and to support the athletes' training progress.
Keywords: piezo-electric sensors; vibration measurement; localization; table tennis

Band-Pass Filter Design by Segmentation in Frequency Domain for Detection of Epithelial Cells in Endomicroscope Images - Bier, Bastian; Mualla, Firas; Steidl, Stefan; Bohr, Christopher; Neumann, Helmut; Maier, Andreas; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015 - Algorithmen, Systeme, Anwendungen, 2015)
Voice hoarseness can have various reasons, one of them is a change of the vocal fold mucus. This change can be examined with micro endoscopes. Cell detection in these images is a difficult task, due to bad image quality, caused by noise and illumination variations. In previous works, it was observed that the repetitive pattern of the cell walls cause an elliptical shape in the Fourier domain [1, 2]. A manual segmentation and back transformation of this shape results in filtered images, where the cell detection is much easier [3]. The goal of this work is to automatically segment the elliptical shape in Fourier domain. Two different approaches are developed to get a suitable band-pass filter: a thresholding and an active contour method. After the band-pass filter is applied, the achieved results are superior to the manual segmentation case.
UnivIS-Import:2018-09-11:Pub.2015.tech.IMMD.IMMD5.bandpa_5
Keywords: medizinische Bildverarbeitung; Zelldetektion

Bayesian Vision: From Intensity Marginals to Mutual Information and Entropic Object Recognition - Hornegger, Joachim; Paulus, Dietrich (1997)
UnivIS-Import:2016-06-27:Pub.1997.tech.IMMD.IMMD5.bayesi

Beam Hardering Correction with an Iterative Scheme Using Exact Backward Projector and a Polychromatic Forward Projector - Bock, Rüdiger; Hoppe, Stefan; Scherl, Holger; Hornegger, Joachim (Bildverarbeitung für die Medizin 2007, 2007)

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

Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for AI-assisted medical education and decision making in radiation oncology - Huang, Yixing; Gomaa, Ahmed; Semrau, Sabine; Haderlein, Marlen; Lettmaier, Sebastian; Weissmann, Thomas; Grigo, Johanna; Ben Tkhayat, Hassen; Frey, Benjamin; Gaipl, Udo; Distel, Luitpold; Maier, Andreas; et al. (Frontiers in Oncology, 2023)
Keywords: Large language model; Radiotherapy; Natural language processing; Artificial intelligence; Gray Zone; Clinical decision support (CDS)

Benchmarking Super-Resolution Algorithms on Real Data - Köhler, Thomas; Bätz, Michel; Naderi Boldaji, Farzad; Kaup, André; Maier, Andreas; Riess, Christian (2017)
Over the past decades, various super-resolution (SR) techniques have been developed to enhance the spatial resolution of digital images. Despite the great number of methodical contributions, there is still a lack of comparative validations of SR under practical conditions, as capturing real ground truth data is a challenging task. Therefore, current studies are either evaluated 1) on simulated data or 2) on real data without a pixel-wise ground truth. To facilitate comprehensive studies, this paper introduces the publicly available Super-Resolution Erlangen (SupER) database that includes real low-resolution images along with high-resolution ground truth data. Our database comprises image sequences with more than 20k images captured from 14 scenes under various types of motions and photometric conditions. The datasets cover four spatial resolution levels using camera hardware binning. With this database, we benchmark 15 single-image and multi-frame SR algorithms. Our experiments quantitatively analyze SR accuracy and robustness under realistic conditions including independent object and camera motion or photometric variation

Bias Correction for Respiration Detection in Radial 3D Gradient-Echo Imaging - Grimm, Robert; Block, Kai Tobias; Kiefer, Berthold; Hornegger, Joachim (Proceedings of International Society for Magnetic Resonance in Medicine, 2011)

Bidirectional Alignment of Glottal Pulse Length Sequences for the Evaluation of Pitch Detection Algorithms - Ferrer, Carlos A.; Guillén, Reinier Rodríguez; Nöth, Elmar (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
This paper describes a problem in a reported Dynamic Time Warping (DTW) alignment procedure to compare the reference and detected glottal pulse length sequences, oriented to compare the evaluation of Pitch Detection Algorithms (PDAs) in pathological voices. The problem in the existing alignment method tends to overestimate the failure of the PDA, by aligning only the detected to the reference sequence. A solution is presented, which performs a bidirectional alignment reducing the differences present in the definitive comparison. The proposal is evaluated in both synthetic and real voice signals, by running three well-known PDAs, and the magnitude of the error reduction along with comments on the possible factors influencing its value, are given. The alignment variant introduced in this paper allows to perform a fairer comparison of the PDAs performances.
CRIS-Team Scopus Importer:2019-12-10
Keywords: Alignment; Dynamic Time Warping; Jitter; Pitch Detection Algorithms

Binarization Driven Blind Deconvolution for Document Image Restoration - Köhler, Thomas; Maier, Andreas; Christlein, Vincent (Pattern Recognition, 2015)
UnivIS-Import:2015-11-17:Pub.2015.tech.IMMD.IMMD5.binari_4

Binary Tomography by Iterating Linear Programs - Weber, Stefan; Schnörr, Christoph; Schüle, Thomas; Hornegger, Joachim (2005)

Binary tomography by iterating linear programs from noisy projections - Weber, Stefan; Schüle, Thomas; Hornegger, Joachim; Schnörr, Christoph (Springer-verlag, 2004)
In this paper we improve the behavior of a reconstruction algorithm for binary tomography in the presence of noise. This algorithm which has recently been published is derived from a primal-dual subgradient method leading to a sequence of linear programs. The objective function contains a smoothness prior that favors spatially homogeneous solutions and a concave functional gradually enforcing binary solutions. We complement the objective function with a term to cope with noisy projections and evaluate its performance. © Springer-Verlag 2004.

Binary Tomography by Iterating Linear Programs from Noisy Projections - Weber, Stefan; Schüle, Thomas; Hornegger, Joachim; Schnörr, Christoph (Proceedings of International Workshop on Combinatorial Image Analysis (IWCIA), 2004)
In this paper we improve the behavior of a reconstruction algorithm for binary tomography in the presence of noise. This algorithm which has recently been published is derived from a primal-dual subgradient method leading to a sequence of linear programs. The objective function contains a smoothness prior that favors spatially homogeneous solutions and a concave functional gradually enforcing binary solutions. We complement the objective function with a term to cope with noisy projections and evaluate its performance. © Springer-Verlag 2004.
UnivIS-Import:2015-04-16:Pub.2004.tech.IMMD.IMMD5.binary
Keywords: Discrete Tomography; Combinatorial Optimization; Linear

Biometric and Mobile Gait Analysis for Early Detection and Therapy Monitoring in Parkinson's Disease - Barth, Jens; Klucken, Jochen; Kugler, Patrick; Kammerer, Thomas; Steidl, Ralph; Winkler, Jürgen; Hornegger, Joachim; Eskofier, Björn (Engineering in Medicine and Biology Society,EMBC, 2011 Annual International Conference of the IEEE, 2011)
Parkinson's disease (PD) is the most frequent neurodegenerative movement disorder. Early diagnosis and effective therapy monitoring is an important prerequisite to treat patients and reduce health care costs. Objective and non-invasive assessment strategies are an urgent need in order to achieve this goal. In this study we apply a mobile, lightweight and easy applicable sensor based gait analysis system to measure gait patterns in PD and to distinguish mild and severe impairment of gait. Examinations of 16 healthy controls, 14 PD patients in an early stage, and 13 PD patients in an intermediate stage were included. Subjects performed standardized gait tests while wearing sport shoes equipped with inertial sensors (gyroscopes and accelerometers). Signals were recorded wirelessly, features were extracted, and distinct subpopulations classified using different classification algorithms. The presented system is able to classify patients and controls (for early diagnosis) with a sensitivity of 88% and a specificity of 86%. In addition it is possible to distinguish mild from severe gait impairment (for therapy monitoring) with 100% sensitivity and 100% specificity. This system may be able to objectively classify PD gait patterns providing important and complementary information for patients, caregivers and therapists. © 2011 IEEE.

Bit-Accurate Simulation of Convolution-Based Filtering on Reconfigurable Hardware - Scherl, Holger; Kowarschik, Markus; Hornegger, Joachim (Frontiers in Simulation, 2005)

Blood flow quantification using optical flow methods in a body fitted coordinate system - Maday, Peter; Brosig, Richard; Endres, Jürgen; Kowarschik, Markus; Navab, Nassir (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2014)
In this paper a blood flow quantification method that is based on a physically motivated dense 2D flow estimation algorithm is outlined. It yields accurate time varying volumetric flow rate measurements based on digital subtraction angiography (DSA) image sequences, with robustness to significant inter-frame displacements. Time varying volumetric flow rates are estimated for individual non-branching vascular segments based on the estimated 2D flow fields and a 3D vessel segmentation from a 3D Rotational Angiography (3DRA) acquisition. The novelty of the approach lies in the use of a vessel aligned coordinate system for the problem formulation. The coordinate functions are generated using the Schwarz-Christoffel1(SC) map that yields a solution with coordinate lines aligned with the vessel boundaries. The use of vessel aligned coordinates enables the easy and accurate handling of boundary conditions in the irregular domain of a vessel lumen while only requiring slight modifications to the used finite difference approach. Unlike traditional coarse to fine methods we use an anisotropic scaling strategy that enables the estimation of flows with larger inter frame displacements. The evaluation of our method is based on highly realistic synthetic DSA datasets for a number of cases. Ground truth volumetric flow rate values are compared against the measurements and a high degree of fidelity is observed. Performance measures are obtained with varying flow velocities and acquisition rates. © 2014 SPIE.
CRIS-Team Scopus Importer:2023-03-08

Boosting based object detection using a geometric model - Quast, Katharina; Seeger, Christoph; Trivedi, Mohan; Kaup, André (2011 18th IEEE International Conference on Image Processing, ICIP 2011, 2011)
In this paper we present a new method for automatic object detection in images and video sequences. As a classifier the popular AdaBoost algorithm is used, that combines several weak classifiers into one strong classifier. To create a detector based on this classifier, the weak classifiers are set into relation during boosting by using a geometric model. All votes of the weak detectors are evaluated in a voting space. The voting space allows a detection with combinations of different object features. We trained and tested the proposed method with SIFT and kAS features and combinations of these. The learned detector is then used to localize objects in images and video sequences. The performance of the algorithm is examined based on selected image data. © 2011 IEE
Keywords: boosting; object detection; Object recognition

Boosting Methods for Automatic Segmentation of Focal Liver Lesions - Militzer, Arne (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)
Over the past decades, huge progress has been made in treatment of cancer, decreasing fatality rates despite a growing number of cases. Technical achievements had a big share in this development. With modern image acquisition techniques, most types of tumors can be made visible. Automatic processing of these images to support diagnosis and therapy, on the other hand, is still very basic. Marking lesions for volume measurements, intervention planning or tracking over time requires a lot of manual interaction, which is both tedious and error prone. The work at hand therefore aims at providing tools for the automatic segmentation of liver lesions. A system is presented that receives a contrast enhanced CT image of the liver as input and, after several preprocessing steps, decides for each image voxel inside the liver whether it belongs to a tumor or not. That way, tumors are not only detected in the image but also precisely delineated in three dimensions. For the decision step, which is the main target of this thesis, we adopted the recently proposed Probabilistic Boosting Tree. In an offline learning phase, this classifier is trained using a number of example images. After training, it can process new and previously unseen images. Such automatic segmentation systems are particularly valuable when it comes to monitoring tumors of a patient over a longer period of time. Therefore, we propose a method for learning a prior model to improve segmentation accuracy for such follow-up examinations. It is learned from a number of series of CT images, where each series contains images of one patient. Two different ways of incorporating the model into the segmentation system are investigated. When acquiring an image of a patient, the system can use the model to calculate a patient specific lesion prior from images of the same patient acquired earlier and thus guide the segmentation in the current image. The validity of this approach is shown in a set of experiments on clinical images. When comparing the points of 90% sensitivity in these experiments, incorporating the prior improved the precision of the segmentation from 82.7% to 91.9%. This corresponds to a reduction of the number of false positive voxels per true positive voxel by 57.8%. Finally, we address the issue of long processing times of classification based segmentation systems. During training, the Probabilistic Boosting Tree builds up a hierarchy of AdaBoost classifiers. In order to speed up classification during application phase, we modify this hierarchy so that simpler and thus faster AdaBoost classifiers are used in higher levels. To this end, we introduce a cost term into AdaBoost training that trades off discriminative power and computational complexity during feature selection. That way the optimization process can be guided to build less complex classifiers for higher levels of the tree and more complex and thus stronger ones for deeper levels. Results of an experimental evaluation on clinical images are presented, which show that this mechanism can reduce the overall cost during application phase by up to 76% without degrading classification accuracy. It is also shown that this mechanism could be used to optimize arbitrary secondary conditions during AdaBoost training.<br
UnivIS-Import:2016-07-26:Pub.2014.tech.IMMD.IMMD5.boosti
Keywords: Bildsegmentierung; Lebertumor; Maschinelles Lernen; Boosting; Computertomographie

Boosting of Prosodic and Pronunciation Features to Detect Mispronunciations of Non-Native Children - Hacker, Christian; Cincarek, Tobias; Maier, Andreas; Heßler, Andre; Nöth, Elmar (ICASSP, 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings, 2007)

Brain Parenchyma and Vessel Separation in 3D Digital Subtraction Angiography Images - Endres, Jürgen; Rohkohl, Christopher; Royalty, Kevin; Schafer, Sebastian; Maier, Andreas; Dörfler, Arnd; Kowarschik, Markus (Bildverarbeitung für die Medizin: Algorithmen-Systeme-Anwendungen., 2017)
UnivIS-Import:2017-03-24:Pub.2017.tech.IMMD.IMMD5.brainp_0

Breast density assessment using wavelet features on mammograms - Schebesch, Frank; Unberath, Mathias; Andersen, Ingwer; Maier, Andreas (Informatik aktuell, 2017)
Breast density differs from almost entirely fatty to extremely dense tissue composition. In mammography screenings, physicians are often supported by computer-aided detection and diagnosis systems (CAD) whose detection rate is affected by the density of the breast. An automatic pre-assessment of breast density would enable a specific analysis adapted to each density class. Digital mammograms from the INbreast database [1] are decomposed into Haar-Wavelet components and several levels are used for classification. A random forest classifier is applied on the averaged Wavelet components for four class densities which yields an accuracy of 64.53% in CC-view and 51.22% in MLO-view. The 3-class problem with a combined class of medium densities yields an accuracy of 73.89% in CC-view and 67.80% in MLO-view.
CRIS-Team Scopus Importer:2022-06-05

Bridge to Real Data: Empirical Multiple Material Calibration for Learning-Based Material Decomposition - Lu, Yanye; Berger, Martin; Manhart, Michael; Choi, Jang-Hwan; Hoheisel, Martin; Kowarschik, Markus; Fahrig, Rebecca; Ren, Qiushi; Hornegger, Joachim; Maier, Andreas (Proceedings of the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
In this study, we proposed an empirical multi-material calibration pipeline for learning-based material decomposition. We used realistic short scan CT data from a general metric phantom using a Siemens C-arm system, and built the corresponding numeric phantom data in a software framework. After that we applied registration approaches for matching the simulated data to the acquired data, which generates prior knowledge for the following material decomposition process, as well as the ground truth for quantitative evaluations. According to the preliminary decomposition results, we successfully decomposed the inserted phantom plugs of different materials using learning-based material decomposition process, which indicates that the proposed approach is valid for learning-based material decomposition.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.bridge_9

Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data - Köhler, Thomas; Bätz, Michel; Naderi Boldaji, Farzad; Kaup, André; Maier, Andreas; Riess, Christian (2018)

Bundle Adjustment for Marker-Based Rigid MR/X-Ray Registration - Fischer, Peter; Pohl, Thomas; Brost, Alexander; Hornegger, Joachim (2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2012)

CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling - Denzinger, Felix; Wels, Michael; Taubmann, Oliver; Gülsün, Mehmet A.; Schöbinger, Max; Andre, Florian; Buss, Sebastian J.; Goerich, Johannes; Sühling, Michael; Maier, Andreas; Breininger, Katharina (Medical Imaging with Deep Learning, 2022)

CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling - Denzinger, Felix; Wels, Michael; Taubmann, Oliver; Gülsün, Mehmet A.; Schöbinger, Max; Andre, Florian; Buss, Sebastian J.; Goerich, Johannes; Sühling, Michael; Maier, Andreas; Breininger, Katharina (PMLR, 2022)
With coronary artery disease (CAD) persisting to be one of the leading causes of death worldwide, interest in supporting physicians with algorithms to speed up and improve diagnosis is high. In clinical practice, the severeness of CAD is often assessed with a coronary CT angiography (CCTA) scan and manually graded with the CAD-Reporting and Data System (CAD-RADS) score. The clinical questions this score assesses are whether patients have CAD or not (rule-out) and whether they have severe CAD or not (hold-out). In this work, we reach new state-of-the-art performance for automatic CAD-RADS scoring. We propose using severity-based label encoding, test time augmentation (TTA) and model ensembling for a task-specific deep learning architecture. Furthermore, we introduce a novel task-and model-specific, heuristic coronary segment labeling, which subdivides coronary trees into consistent parts across patients. It is fast, robust, and easy to implement. We were able to raise the previously reported area under the receiver operating characteristic curve (AUC) from 0.914 to 0.942 in the rule-out and from 0.921 to 0.950 in the hold-out task respectively.<br

Calibration-free beam hardening reduction in x-ray CBCT using the epipolar consistency condition and physical constraints. - Würfl, Tobias; Hoffmann, Mathis; Aichert, André; Maass, Nicole; Dennerlein, Frank; Maier, Andreas (Medical Physics, 2019)
The beam hardening effect is a typical source of artifacts in x-ray cone beam computed tomography (CBCT). It causes streaks in reconstructions and corrupted Hounsfield units toward the center of objects, widely known as cupping artifacts. We present a novel efficient projection data-based method for reduction of beam-hardening artifacts and incorporate physical constraints on the shape of the compensation functions. The method is calibration-free and requires no additional knowledge of the scanning setup. The mathematical model of the beam hardening effect caused by a single material is analyzed. We show that the effect of beam hardening on the resulting functions on the line integral measurements are monotonous and concave functions of the ideal data. This holds irrespective of any limiting assumptions on the energy dependency of the material, the detector response or properties of the x-ray source. A regression model for the beam hardening effect respecting these theoretical restrictions is proposed. Subsequently, we present an efficient method to estimate the parameters of this model directly in projection domain using an epipolar consistency condition. Computational efficiency is achieved by exploiting the linearity of an intermediate function in the formulation of our optimization problem. algorithm is effective even in challenging measured data scenarios with additional sources of inconsistency. The combination of mathematical consistency condition and a compensation model that is based on the properties of x-ray physics enables us to improve image quality of measured data retrospectively and to decrease the need for calibration in a data-driven manner. BACKGROUND PURPOSE METHOD RESULTS CONCLUSIONS

Calibration of a Camera-Based Guidance Solution for Orthopedic and Trauma Surgery - Magaraggia, Jessica; Egli, Adrian; Kleinszig, Gerhard; Graumann, Rainer; Angelopoulou, Elli; Hornegger, Joachim (Bildverarbeitung für die Medizin 2013, 2013)
In orthopedic and trauma surgery, fracture reduction usually requires the use of metallic plates and their fixation by means of screws. The employment of guidance solutions during surgical procedures has become of great importance during the last decades. Our guidance solution exploits a small video camera placed directly on the instrument, for example a drill, and a set of small markers placed around the location where the drilling needs to be performed. A calibration step is required in order to determine the relative position of the instrument tip and axis w.r.t the coordinate system of the video camera. In this paper we describe a calibration method for our guidance solution. This calibration method exploits optical markers and a calibration plate whose geometry is known. Moreover, we show how we can exploit directly the image acquired by the video camera during the calibration in order to define an error measure to estimate the accuracy of the calibration. With this method, we achieved respectively an accuracy of 0.23mm and 3.40° in the estimation of the instrument tip position and of the orientation of the instrument axis.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.calibr_8
Keywords: Instrument calibration; Orthopedic surgery; Image-guidance

Calibration of the circle-plus-arc trajectory - Hoppe, Stefan; Noo, Frédéric; Dennerlein, Frank; Lauritsch, Günter; Hornegger, Joachim (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 paper, a novel calibration method for C-arm cone-beam (CB) scanners is presented which allows the calibration of the circle-plus-arc trajectory. The circle-plus-arc trajectory has been investigated recently for exact image reconstruction and is especially well suited for C-arm systems. The main idea is the separation of the trajectory into two segments (circle segment and arc segment) which are calibrated independently. For each trajectory segment, a calibration phantom is placed in an optimal way. The calibration results are then combined by computing the transformation the phantom experienced in-between the independent runs. This combination can be done in a postprocessing step by using the Singular Value Decomposition (SVD). The method works for any calibration procedure in which the phantom has a favored orientation with respect to a trajectory segment. Results are presented for both, simulated as well as real data acquired with a Siemens AXIOM Arris C-arm system. © 2006 IEEE.

Caller: Computer Assisted Language Learning from Erlangen - Pronunciation Training and More - Hacker, Christian; Maier, Andreas; Heßler, Andre; Guthunz, Ute; Nöth, Elmar (Proc. Int. Conf. Interactive Computer Aided Learning (ICL), 2007)

Can you Understand him? Let's Look at his Word Accuracy - Automatic Evaluation of Tracheoesophageal Speech - Schuster, Maria; Nöth, Elmar; Haderlein, Tino; Steidl, Stefan; Batliner, Anton; Rosanowski, Frank (Proceedings of ICASSP 2005 - International Conference on Acoustics, Speech, and Signal Processing, 2005)
Tracheoesophageal (TE) speech is a possibility to restore the ability to speak after laryngectomy. TE speech often shows low intelligibility. An objective means to determine and quantify the intelligibility does not exist until now and an automation of this procedure is desirable. We used a speech recognizer trained on normal, non–pathologic voices. We compared intelligibility scores for TE speech from five experienced raters with the word accuracy (WA) of our speech recognizer. A correlation coefficient of -.84 shows that WA can be a good indicator of intelligibility for pathologic voices. An outlook for future work is presented.

Cardiac C-arm CT: 4D non-model based heart motion estimation and its application - Prümmer, Marcus; Fahrig, Rebecca; Wigström, Lars; Boese, Jan; Lauritsch, Günter; Strobel, Norbert; Hornegger, Joachim (Medical Imaging 2007: Physics of Medical Imaging, 2007)
The combination of real-time fluoroscopy and 3D cardiac imaging on the same C-arm system is a promising technique that might improve therapy planning, guiding, and monitoring in the interventional suite. In principal, to reconstruct a 3D image of the beating heart at a particular cardiac phase, a complete set of X-ray projection data representing that phase is required. One approximate approach is the retrospectively ECG-gated FDK reconstruction (RG-FDK). From the acquired data set of N multiple C-arm sweeps, those projection images which are acquired closest in time to the desired cardiac phase are retrospectively selected. However, this approach uses only 1/N of the obtained data. Our goal is to utilize data from other cardiac phases as well. In order to minimize blurring and motion artifacts, cardiac motion has to be compensated for, which can be achieved using a temporally dependent spatial 3D warping of the filtered-backprojections. In this work we investigate the computation of the 4D heart motion based on prior reconstructions of several cardiac phases using RG-FDK. A 4D motion estimation framework is presented using standard fast non-rigid registration. A smooth 4D motion vector field (MVF) represents the relative deformation compared to a reference cardiac phase. A 4D deformation regridding by adaptive supersampling allows selecting any reference phase independently of the set of phases used in the RG-FDK for a motion corrected reconstruction. Initial promising results from in vivo experiments are shown. The subjects individual 4D cardiac MVF could be computed from only three RG-FDK image volumes. In addition, all acquired projection data were motion corrected and subsequently used for image reconstruction to improve the signal-to-noise ratio compared to RG-FDK.

Cardiac c-arm CT: A unified framework for motion estimation and dynamic CT - Prümmer, Marcus; Hornegger, Joachim; Lauritsch, Günter; Wigstroem, Lars; Girard-Hughes, Erin; Fahrig, Rebecca (IEEE Transactions on Medical Imaging, 2009)
Generating 3-D images of the heart during interventional procedures is a significant challenge. In addition to real time fluoroscopy, angiographic C-arm systems can also now be used to generate 3-D/4-D CT images on the same system. One protocol for cardiac CT uses ECG triggered multisweep scans. A 3-D volume of the heart at a particular cardiac phase is then reconstructed by applying Feldkamp (FDK) reconstruction to the projection images with retrospective ECG gating. In this work we introduce a unified framework for heart motion estimation and dynamic cone-beam reconstruction using motion corrections. The benefits of motion correction are 1) increased temporal and spatial resolution by removing cardiac motion which may still exist in the ECG gated data sets and 2) increased signal-to-noise ratio (SNR) by using more projection data than is used in standard ECG gated methods. Three signal-enhanced reconstruction methods are introduced that make use of all of the acquired projection data to generate a 3-D reconstruction of the desired cardiac phase. The first averages all motion corrected back-projections; the second and third perform a weighted averaging according to 1) intensity variations and 2) temporal distance relative to a time resolved and motion corrected reference FDK reconstruction. In a comparison study seven methods are compared: nongated FDK, ECG-gated FDK, ECG-gated, and motion corrected FDK, the three signal-enhanced approaches, and temporally aligned and averaged ECG-gated FDK reconstructions. The quality measures used for comparison are spatial resolution and SNR. Evaluation is performed using phantom data and animal models. We show that data driven and subject-specific motion estimation combined with motion correction can decrease motion-related blurring substantially. Furthermore, SNR can be increased by up to 70% while maintaining spatial resolution at the same level as is provided by the ECG-gated FDK. The presented framework provides excellent image quality for cardiac C-arm CT. © 2009 IEEE.

Cardiac C-arm CT: Efficient motion correction for 4D-FBP - Prümmer, Marcus; Wigström, Lars; Hornegger, Joachim; Boese, Jan; Lauritsch, Günter; Strobel, Norbert; Fahrig, Rebecca (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)
Cardiac C-arm CT is a promising technique that enables 3D cardiac image acquisition and real-time fluoroscopy on the same system. The goal is to bring 3D imaging to the interventional suite for improved therapy planning, guidance, and monitoring. For the reconstruction of 3D cardiac image data, a complete set of projections from a specific heart phase is required. One approach to reduce motion blurring caused by the beating heart is to acquire multiple sweeps using the Carm and retrospectively select the projections that are closest to the desired cardiac phase. In order to further improve the temporal resolution, novel image processing algorithms that utilize retrospective motion correction were investigated in this study. The main focus of this work is to extend the well established FDK algorithm to incorporate motion correction during the backprojection step using a subject specific computed motion field. In a simulation study we show that motion blurring can be decreased significantly using the subjects' individual estimated heart motion based on a time series of retrospectively gated FDK reconstructions. In our experiments using an animal model we investigated the following two scenarios: (I) Can the image quality from a single sweep be improved given a subjects' individual prior computed motion field? (II) Can improved image quality be achieved using the full temporal resolution of a multi-sweep scan for motion estimation in combination with motion correction? Our results show that increasing temporal resolution using an first order estimated 4D motion vector field of the subjects' individual heart motion in the FDK-4D algorithm can decrease motion blurring substantially for both investigated scenarios. © 2006 IEEE.

Cardiac C-arm CT: Image-based gating - Rohkohl, Christopher; Prümmer, Marcus; Fahrig, Rebecca; Lauritsch, Günter; Hornegger, Joachim (Medical Imaging 2008 - Physics of Medical Imaging, 2008)
Image guidance during cardiac interventional procedures (IP) using cardiac C-arm CT systems is desirable for many procedures. Applying the concept of retrospective electrocardiogram gating (ECG) to the acquisition of multiple, ECG-triggered rotational acquisitions using a C-arm system allows the 3D+t reconstruction of the heart. The process of retrospective gating is a crucial component of 3-D reconstruction. The gold-standard in gating is still ECG based. However, the ECG signal does not directly reflect the mechanical situation of the heart. Therefore an alternative gating method, based on the acquired projection data is required. Our goal is to provide an image-based gating (IBG) method without ECG such that already acquired projection data from a multi-sweep acquisition can still be used for reconstruction. We formulate the gating problem as a shortest-path optimization problem. All acquired projection images build a directed graph and the path costs are defined by projection image similarities that are based on image metrics to measure the heart phase similarity. The optimization is additionally regularized to prefer solutions where the path segment of consecutive selected projections acquired along a particular forward or backward C-arm sweep is short. This regularization depends on an estimated average heart rate that is also estimated using an image-based method. First promising results using in-vivo data are presented and compared to standard ECG gating. We conclude that the presented IBG method provides a reliable gating.

Cardiac C-Arm CT: SNR Enhancement by Combining Multiple Retrospectively Motion Corrected FDK-like Reconstructions - Prümmer, Marcus; Wigström, Lars; Fahrig, Rebecca; Lauritsch, Günter; Hornegger, Joachim (Bildverarbeitung für die Medizin 2007, 2007)

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge - Zhuang, Xiahai; Xu, Jiahang; Luo, Xinzhe; Chen, Chen; Ouyang, Cheng; Rueckert, Daniel; Campello, Victor M.; Lekadir, Karim; Vesal, Sulaiman; Ravikumar, Nishant; Liu, Yashu; Luo, Gongning; et al. (Medical Image Analysis, 2022)
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/).
CRIS-Team Scopus Importer:2022-08-12
Keywords: Benchmark; Cardiac MRI segmentation; Challenge; Multi-sequence

C-arm Computed Tomography with Extended Axial Field-of-View - Yu, Zhicong (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2013)
C-arm computed tomography (CT) is an innovative imaging technique in the interventional room that enables a C-arm system to generate 3D images like a CT system. Clinical reports demonstrate that this technique can help reduce treatment-related complications and may improve interventional efficacy and safety. However, currently, C-arm CT is only capable of imaging axially-short object, because it employs a single circular data acquisition geometry. This shortcoming can be a problem in some intraoperative cases when imaging a long object, e.g., the entire spine, is crucial. A new technique, C-arm CT for axially-long objects, namely extended-volume C-arm CT, has to be developed. This thesis aims at achieving this development. In particular, this thesis designs and analyzes data acquisition geometries as well as develops and implements reconstruction algorithms for extended-volume C-arm CT. The thesis consists of three parts. In the first part, we studied three data acquisition geometries and invented two thereof. For these geometries, we investigated their feasibility on a C-arm system and analyzed their possibility for efficient, theoretically-exact and -stable (TES) reconstruction algorithms. We observed that the reverse helical trajectory is a good start for real data test and the novel ellipse-line-ellipse trajectory is a good candidate for efficient TES image reconstruction. In the second part, we developed and implemented geometry-specific reconstruction algorithms. For the reverse helix, we designed three Feldkamp-Davis-Kress (FDK)-type reconstruction methods. Among the three methods, the Fusion-RFDK and Fusion-HFDK methods are preferred as they are more practical and produce acceptable images for extended-volume C-arm CT. For the ellipse-line-ellipse trajectory, we established an efficient TES reconstruction scheme, which makes proficient use of the geometry of this trajectory. In the third part, we conducted the first experiment for extended-volume C-arm CT on a laboratorial Artis zeego system. In this experiment, cone-beam data were reliably acquired using the reverse helical trajectory and 3D images were successfully reconstructed by the Fusion-RFDK method. The consistency among theoretical understanding, simulation results and achieved image quality from a real system strongly demonstrate feasibility of extended-volume C-arm CT in the interventional room.<br
UnivIS-Import:2016-07-26:Pub.2013.tech.IMMD.IMMD5.carmco
Keywords: C-arm; Computed Tomography

C-arm Cone-Beam Computed Tomography Reconstruction for Knee Imaging under Weight-Bearing Conditions - Bier, Bastian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Medical imaging is essential for the assessment of osteoarthritis and the overall knee health. For that purpose, radiographs of the knees of standing patients are acquired commonly. These suffer, however, under projective transformation and thus do not allow conclusions to be drawn about the complex 3-D joint anatomy. Conversely, compared to many 3-D capable imaging modalities, imaging under load is easily feasible using X-rays. This is beneficial, since it has been shown that this reflects the knee joint under more realistic conditions. Recently, a 3-D imaging protocol has been proposed that enables cone-beam computed tomography (CBCT) reconstruction of the knees acquired under weight-bearing conditions. To this end, the C-arm rotates on a horizontal trajectory around the standing patient. Involuntary patient motion and scattered radiation deteriorate the reconstructions’ image quality substantially. In this thesis, novel concepts and methods are proposed to further develop this imaging protocol in order to improve the reconstructions. In a first approach, a primary modulator-based scatter correction method has been transferred on a clinical C-arm CBCT system. The method is a suitable candidate to be applied to projection images of the knees, since it is capable of estimating heterogeneous scatter distributions. To this end, extensions to an existing method have been developed to compensate for the system wobble and the automatic exposure control of the C-arm systems. In multiple experiments, it is demonstrated that the primary modulator method works on clinical C-arm scanners and also for imaging under load. A current state-of-the-art motion estimation method is based on metallic fiducial markers that are placed on the patient’s skin. The marker placement is, however, a tedious and time-consuming process. To this end, two marker-free alternatives are proposed. In a first attempt, a range camera is utilized to track the patient surface simultaneous to a CBCT image acquisition. Using point cloud registration of the acquired depth frames, transformations can be computed that correspond to the patient motion, which directly can be integrated into the image reconstruction. In a simulation study, comparable results to the marker-based method could be achieved. Yet, initial real data experiments on a clinical scanner did not achieve satisfying image quality, even though part of the motion could be estimated. Therefore, the promising results make this method to a pre-cursor to future research. Although this method is marker-free, a prepared environment is required. Hence, another purely image-based motion estimated approach has been investigated. The idea is to replace the positions of the fiducial markers with the ones of anatomical landmarks present in the projections. Anatomical landmark detection in X-ray images from different directions is difficult and, to the best of our knowledge, has not been investigated yet. For this purpose, a novel deep learning-based approach has been developed. In a first evaluation, the method was tested on X-ray images of the pelvis. Here, it could be demonstrated that the detection accuracy sufficed to initialize a 2-D/3-D registration. Subsequently, the approach is transferred to knee projection images, where the good detection results served as input for the motion estimation. Despite limited results on real data acquisition, the achieved improvements of the image quality are an indicator for a successful future application for motion estimation.<br
Keywords: 3-D Reconstruction; Cone-Beam Computed Tomography; Motion Compensation; Scatter Correction

C-arm CT: Reconstruction of dynamic high contrast objects applied to the coronary sinus - Rohkohl, Christopher; Lauritsch, Günter; Nöttling, Alois; Prümmer, Marcus; Hornegger, Joachim (2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008, 2008)
For many interventional procedures the 3-D reconstruction of dynamic high contrast objects from C-arm data is desirable. We present a method for compensating artifacts from periodic motions by providing a modified filtered backprojection algorithm. The proposed algorithm comprises three steps: First, the reconstruction of an initial reference volume from a phaseconsistent subset of the projection data. Secondly, the selection of proper data for a motion corrected reconstruction using as many projections as possible in the third step. The first step is addressed by gating in combination with a modified backprojection operator which reduces streak artifacts, the second by analysis of the cardiac motion characteristics and the impact on gated reconstruction quality and the third by accumulating gated sub-reconstructions registered with the reference volume. We present first clinical results from real patient data for the reconstruction of the coronary sinus. © 2008 IEEE.

Catheter artifact reduction (CAR) in dynamic cardiac chamber imaging with interventional C-arm CT - Müller, Kerstin; Lauritsch, Günter; Schwemmer, Chris; Maier, Andreas; Taubmann, Oliver; Abt, Bernd; Köhler, Henning; Nöttling, Alois; 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.cathet_3

Catheter tracking: Filter-based vs. learning-based - Brost, Alexander; Wimmer, Andreas; Liao, Rui; Hornegger, Joachim; Strobel, Norbert (Springer-verlag, 2010)
Atrial fibrillation is the most common sustained arrhythmia. One important treatment option is radio-frequency catheter ablation (RFCA) of the pulmonary veins attached to the left atrium. RFCA is usually performed under fluoroscopic (X-ray) image guidance. Overlay images computed from pre-operative 3-D volumetric data can be used to add anatomical detail otherwise not visible under X-ray. Unfortunately, current fluoro overlay images are static, i.e., they do not move synchronously with respiratory and cardiac motion. A filter-based catheter tracking approach using simultaneous biplane fluoroscopy was previously presented. It requires localization of a circumferential tracking catheter, though. Unfortunately, the initially proposed method may fail to accommodate catheters of different size. It may also detect wrong structures in the presence of high background clutter. We developed a new learning-based approach to overcome both problems. First, a 3-D model of the catheter is reconstructed. A cascade of boosted classifiers is then used to segment the circumferential mapping catheter. Finally, the 3-D motion at the site of ablation is estimated by tracking the reconstructed model in 3-D from biplane fluoroscopy. We compared our method to the previous approach using 13 clinical data sets and found that the 2-D tracking error improved from 1.0 mm to 0.8 mm. The 3-D tracking error was reduced from 0.8 mm to 0.7 mm. © 2010 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD5.cathet

CAVAREV - An open platform for evaluating 3D and 4D cardiac vasculature reconstruction - Rohkohl, Christopher; Lauritsch, Günter; Keil, Andreas; Hornegger, Joachim (Physics in Medicine and Biology, 2010)
The 3D reconstruction of cardiac vasculature, e.g. the coronary arteries, using C-arm CT (rotational angiography) is an active and challenging field of research. There are numerous publications on different reconstruction techniques. However, there is still a lack of comparability of achieved results for several reasons: foremost, datasets used in publications are not open to public and thus experiments are not reproducible by other researchers. Further, the results highly depend on the vasculature motion, i.e. cardiac and breathing motion patterns which are also not comparable across publications. We aim to close this gap by providing an open platform, called Cavarev (CArdiac VAsculature Reconstruction EValuation). It features two simulated dynamic projection datasets based on the 4D XCAT phantom with contrasted coronary arteries which was derived from patient data. In the first dataset, the vasculature undergoes a continuous periodic motion. The second dataset contains aperiodic heart motion by including additional breathing motion. The geometry calibration and acquisition protocol were obtained from a real-world C-arm system. For qualitative evaluation of the reconstruction results, the correlation of the morphology is used. Two segmentation-based quality measures are introduced which allow us to assess the 3D and 4D reconstruction quality. They are based on the spatial overlap of the vasculature reconstruction with the ground truth. The measures enable a comprehensive analysis and comparison of reconstruction results independent from the utilized reconstruction algorithm. An online platform (www.cavarev.com) is provided where the datasets can be downloaded, researchers can manage and publish algorithm results and download a reference C++ and Matlab implementation. © 2010 Institute of Physics and Engineering in Medicine.

Cavity Segmentation in X-ray Microscopy Scans of Mouse Tibiae - Gu, Mingxuan; Thies, Mareike; Wagner, Fabian; Pechmann, Sabrina; Aust, Oliver; Weidner, Daniela; Neag, Georgiana; Pan, Zhaoya; Utz, Jonas; Schett, Georg; Christiansen, Silke; Uderhardt, Stefan; et al. (Informatik aktuell, 2023)
Osteoporosis is a chronic disease that causes lower bone density and makes bones fragile. This severely impairs patient life qualities and increases the burden on the social and health care system. X-ray microscopy (XRM) allows tracking of osteoporosis-related changes at a microstructural level in the bone, entailing the characterization of osteocyte lacunae and blood vessel canals. Unfortunately, no segmentation methods for micro-structures in XRM images have yet been established. In this work, we compare the performance of a traditional thresholding-based method with three deep learning networks including 2D and 3D models in both binary and multi-class segmentation. We further propose a clustering method to automatically distinguish blood vessels from lacunae for the binary methods. The performance is evaluated with Dice score (F1 score). The thresholding-based method reaches a mean Dice score of 0.729, which the deep learning models improve by 0.129 - 0.168.
CRIS-Team Scopus Importer:2023-11-03

Cell Culture Monitoring with Novel Bright Field Miniature Microscopy Prototypes - Schöll, Simon (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2016)
Cell cultures are monitored to develop new drugs, to find efficient ways to produce vaccines and to perform toxicity tests. The cells are cultivated in an incubator and the monitoring steps such as the acquisition of images and the counting of cells are often done outside. As part of a research project, novel bright field miniature microscopy prototypes were developed. These prototypes were designed to work inside the incubator, and hence, they need to be very small. In this thesis, image processing methods for these systems (at different development stages) are presented. These methods made the systems usable for cell monitoring in an incubator. This is a main contribution of the thesis. Our analyses of the system and its components helped to improve the development of the systems. A calibration procedure and algorithms for adjusting the illumination and the focus position of these systems are introduced. Moreover, the proposed preprocessing steps such as illumination correction and contrast enhancement improved the image quality. An image processing library and a cell monitoring software using the library were developed. An algorithm for counting cells in images of the prototype system was included in the image processing library. Features for viability determination were investigated and also included in the library. Another main contribution is related to all bright field microscopes. They have the following effect in common: Focusing of very thin (phase) objects differs from focusing of objects that are thicker and less transparent for light. This effect is investigated in detail, explained, and the calculation of different useful focus positions for phase objects is derived. The optical focus position can be used for applications such as phase retrieval. Slightly defocused cell images with a maximum in contrast at small details can be useful for applications such as cell segmentation or cell analysis. Strongly defocused cell images with a maximum in contrast for the cell borders can be used for applications such as cell detection.<br
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.cellcu
Keywords: cell culture

Cell Detection for Asthma on Partially Annotated Whole Slide Images: Learning to be EXACT - Marzahl, Christian; Bertram, Christof A.; Wilm, Frauke; Voigt, Jörn; Barton, Ann K.; Klopfleisch, Robert; Breininger, Katharina; Maier, Andreas; Aubreville, Marc (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems - Huang, Yixing; Fan, Fuxin; Syben-Leisner, Christopher; Roser, Philipp; Mill, Leonid; Maier, Andreas (Medical Image Analysis, 2021)
Due to the lack of a standardized 3D cephalometric analysis methodology, 2D cephalograms synthesized from 3D cone-beam computed tomography (CBCT) volumes are widely used for cephalometric analysis in dental CBCT systems. However, compared with conventional X-ray film based cephalograms, such synthetic cephalograms lack image contrast and resolution, which impairs cephalometric landmark identification. In addition, the increased radiation dose applied to acquire the scan for 3D reconstruction causes potential health risks. In this work, we propose a sigmoid-based intensity transform that uses the nonlinear optical property of X-ray films to increase image contrast of synthetic cephalograms from 3D volumes. To improve image resolution, super resolution deep learning techniques are investigated. For low dose purpose, the pixel-to-pixel generative adversarial network (pix2pixGAN) is proposed for 2D cephalogram synthesis directly from two cone-beam projections. For landmark detection in the synthetic cephalograms, an efficient automatic landmark detection method using the combination of LeNet-5 and ResNet50 is proposed. Our experiments demonstrate the efficacy of pix2pixGAN in 2D cephalogram synthesis, achieving an average peak signal-to-noise ratio (PSNR) value of 33.8 with reference to the cephalograms synthesized from 3D CBCT volumes. Pix2pixGAN also achieves the best performance in super resolution, achieving an average PSNR value of 32.5 without the introduction of checkerboard or jagging artifacts. Our proposed automatic landmark detection method achieves 86.7% successful detection rate in the 2 mm clinical acceptable range on the ISBI Test1 data, which is comparable to the state-of-the-art methods. The method trained on conventional cephalograms can be directly applied to landmark detection in the synthetic cephalograms, achieving 93.0% and 80.7% successful detection rate in 4 mm precision range for synthetic cephalograms from 3D volumes and 2D projections, respectively.<br
Keywords: Deep learning; cephalogram synthesis; super resolution; landmark detection; perspective deformation learning

Cerebral Microinfarcts in Primary Open-Angle Glaucoma Correlated With DTI-Derived Integrity of Optic Radiation - Schoemann, Johannes; Engelhorn, Tobias; Wärntges, Simone; Dörfler, Arnd; El-Rafei, Ahmed Mohamed Ibrahim; Michelson, Georg (Investigative Ophthalmology & Visual Science, 2014)
To evaluate the correlation between the extent of cerebral white matter lesions (WMLs) and the integrity of the visual pathway represented by fractional anisotropy (FA) in patients with primary open-angle glaucoma (POAG).This case-control study included a total of 61 German patients (39 POAG patients, 22 controls) matched for age and sex. Fractional anisotropy of the optic radiation was determined by 3-Tesla diffusion tensor imaging. White matter lesions and brain volumes were manually measured by using a T2-weighted, 3-D fluid-attenuated inversion recovery sequence.In POAG patients WML volumes were significantly (P = 0.04) increased in the subcortical area. This applied for both absolute and relative units to the specific patient's brain volume, compared to controls. The WML volumes were significantly (P = 0.003) greater in middle-aged (40-59 years) POAG patients than control patients. In controls there was a significant age correlation of WML volumes in the total brain, subcortical, and optic radiation regions of interest. There was a significant correlation between FA and WML in POAG regarding the total brain, the periventricular region, and the optic radiation in both hemispheres. In POAG, FA left and right optic radiation correlated significantly with age (P = 0.002).We were able to demonstrate that (1) POAG patients aged 40 to 60 years had higher volumes of cerebral microinfarcts and (2) POAG patients showed a significant correlation between cerebral microinfarcts and degeneration of the optic radiation. This indicates that cerebral microinfarcts might be an intracerebral risk factor for glaucomatous optic nerve atrophy.
EVALuna2:21212

Challenges posed by statistical weights and data redundancies in iterative X-ray CT reconstruction - Schmitt, Katharina; Schöndube, Harald; Stiersdorfer, Karl; Hornegger, Joachim; Noo, Frédéric (Proceedings of the 12th Fully Three-Dimensional image Reconstruction in Radiology and Nuclear Medicine, 2013)

Characterization Methods for the Detection of Multiple Voice Disorders: Neurological, Functional, and Laryngeal Diseases - Rafael Orozco-Arroyave, Juan; Alexander Belalcazar-Bolanos, Elkyn; David Arias-Londono, Julian; Francisco Vargas-Bonilla, Jesus; Skodda, Sabine; Rusz, Jan; Daqrouq, Khaled; Hönig, Florian Thomas; Nöth, Elmar (IEEE Journal of Biomedical and Health Informatics, 2015)
This paper evaluates the accuracy of different characterization methods for the automatic detection of multiple speech disorders. The speech impairments considered include dysphonia in people with Parkinson's disease (PD), dysphonia diagnosed in patients with different laryngeal pathologies (LP), and hypernasality in children with cleft lip and palate (CLP). Four different methods are applied to analyze the voice signals including noise content measures, spectral-cepstralmodeling, nonlinear features, and measurements to quantify the stability of the fundamental frequency. These measures are tested in six databases: three with recordings of PD patients, two with patients with LP, and one with children with CLP. The abnormal vibration of the vocal folds observed in PD patients and in people with LP is modeled using the stability measures with accuracies ranging from 81% to 99% depending on the pathology. The spectral-cepstral features are used in this paper to model the voice spectrum with special emphasis around the first two formants. These measures exhibit accuracies ranging from 95% to 99% in the automatic detection of hypernasal voices, which confirms the presence of changes in the speech spectrum due to hypernasality. Noise measures suitably discriminate between dysphonic and healthy voices in both databases with speakers suffering from LP. The results obtained in this study suggest that it is not suitable to use every kind of features to model all of the voice pathologies; conversely, it is necessary to study the physiology of each impairment to choose the most appropriate set of features.
Keywords: Hypernasality; laryngeal pathologies (LP); noise measures; nonlinear behavior; Parkinson's disease (PD); periodicity; spectral-cepstral modeling; stability.

Characterization of Choroidal Layers in Normal Aging Eyes Using Enface Swept-Source Optical Coherence Tomography - Adhi, Mehreen; Ferrara, Daniela; Mullins, Robert F.; Baumal, Caroline R.; Mohler, Kathrin J.; Kraus, Martin F.; Liu, Jonathan; Badaro, Emmerson; Alasil, Tarek; Hornegger, Joachim; Fujimoto, James G.; Duker, Jay S.; et al. (PLoS ONE, 2015)
Enface SS-OCT at 1050nm enables a precise qualitative and quantitative characterization of the individual choroidal layers in normal eyes. Only the CC is relatively thinner in the older eyes. In-vivo evaluation of the choroid at variable depths may be potentially valuable in understanding the natural history of age-related posterior segment disease.

Characterization of mixing in a T-mixer: A combined experimental and numerical study - Peukert, Wolfgang; Schwarzer, Hans-Christoph; Manhart, Michael (European Conference on Mixing, 2003)
UnivIS-Import:2015-04-16:Pub.2003.tech.ITC.mechve.charac
Keywords: mixing; simulation

Characterization of voice quality of Parkinson's disease using differential phonological posterior features - Cernak, Milos; Rafael Orozco-Arroyave, Juan; Rudzicz, Frank; Christensen, Heidi; Vasquez Correa, Juan; Nöth, Elmar (Computer Speech and Language, 2017)
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.charac
Keywords: Parkinson's disease; phonological features; deep learning

Cherenkov excited luminescence imaging induced by megavolt X-ray beams in the second near-infrared window - Meng, Xiangxi; Du, Yi; Wang, R.; Li, Ziyuan; Zhu, S.; Wu, Hao; Li, Changhui; Chen, Weiqiang; Nie, Shuming; Ren, Qiushi; Lu, Yanye (Optics Communications, 2019)
The Cherenkov radiation induced by megavolt X-ray beams of a medical linear accelerator is highly intense in blue and ultraviolet lights. This poses a challenge for in vivo imaging, which favors longer wavelengths. However, using quantum dots with a large Stokes shift, we converted the Cherenkov photons to fluorescent photons in the second near-infrared (NIR-II) window. A system was designed to provide shielding for the NIR-II camera, and high quality NIR-II CELI signal was acquired. The NIR-II CELI of X-ray beams was shown at various depths, and the condition to trigger it was also studied. Animal experiments showed this technique could visualize the delivery of X-ray radiation in mice qualitatively. We concluded that CELI induced by megavolt X-ray beams in the NIR-II window possesses a great potential as a quality control tool in radiotherapy.
CRIS-Team Scopus Importer:2019-08-09
Keywords: Cherenkov radiation; Radiotherapy; Second near-infrared window; X-ray

Choroidal Neovascularization Analyzed on Ultrahigh-Speed Swept-Source Optical Coherence Tomography Angiography Compared to Spectral-Domain Optical Coherence Tomography Angiography - Novais, Eduardo A.; Adhi, Mehreen; Moult, Eric M.; Louzada, Ricardo N.; Cole, Emily D.; Husvogt, Lennart; Lee, Byungkun; Dang, Sabin; Regatieri, Caio V S; Witkin, André J.; Baumal, Caroline R.; Hornegger, Joachim; et al. (American Journal of Ophthalmology, 2016)
Purpose To compare visualization of choroidal neovascularization (CNV) secondary to age-related macular degeneration (AMD) using an ultrahigh-speed swept-source (SS) optical coherence tomography angiography (OCTA) prototype vs a spectral-domain (SD) OCTA device. Design Comparative analysis of diagnostic instruments. Methods Patients were prospectively recruited to be imaged on SD OCT and SS OCT devices on the same day. The SD OCT device employed is the RTVue Avanti (Optovue, Inc, Fremont, California, USA), which operates at ∼840 nm wavelength and 70 000 A-scans/second. The SS OCT device used is an ultrahigh-speed long-wavelength prototype that operates at ∼1050 nm wavelength and 400 000 A-scans/second. Two observers independently measured the CNV area on OCTA en face images from the 2 devices. The nonparametric Wilcoxon signed rank test was used to compare area measurements and P values of <.05 were considered statistically significant. Results Fourteen eyes from 13 patients were enrolled. The CNV in 11 eyes (78.6%) were classified as type 1, 2 eyes (14.3%) as type 2, and 1 eye (7.1%) as mixed type. Total CNV area measured using SS OCT and SD OCT 3 mm × 3 mm OCTA were 0.949 ± 1.168 mm and 0.340 ± 0.301 mm, respectively (P =.001). For the 6 mm × 6 mm OCTA the total CNV area using SS OCT and SD OCT were 1.218 ± 1.284 mm and 0.604 ± 0.597 mm, respectively (P =.0019). The field of view did not significantly affect the measured CNV area (P =.19 and P =.18 for SS OCT and SD OCT, respectively). Conclusion SS OCTA yielded significantly larger CNV areas than SD OCTA. It is possible that SS OCTA is better able to demarcate the full extent of CNV vasculature.

Choroid, Haller's, and Sattler's Layer Thickness in Intermediate Age-Related Macular Degeneration With and Without Fellow Neovascular Eyes - Esmaeelpour, Marieh; Ansari-Shahrezaei, Siamak; Glittenberg, Carl; Nemetz, Susanne; Kraus, Martin; Hornegger, Joachim; Fujimoto, James G.; Drexler, Wolfgang; Binder, Susanne (Investigative Ophthalmology & Visual Science, 2014)
CONCLUSIONS. The 3D 1060-nm OCT choroidal imaging visualized significant changes in choroidal, Sattler's, and Haller's layer thickness in relation to the progression of AMD. This may be important for understanding the choroidopathy in the pathophysiology of AMD.
Keywords: age-related macular degeneration; optical coherence tomography; choroidal thickness maps; retina; choroid; retina

Chromoendoscopy in magnetically guided capsule endoscopy - Mewes, Philip; Foertsch, Stefan; Juloski, Aleksandar Lj; Angelopoulou, Elli; Gölder, Stefan; Guldi, Dirk Michael; Hornegger, Joachim; Messmann, Helmut (Biomedical Engineering Online, 2013)
Background: Diagnosis of intestinal metaplasia and dysplasia via conventional endoscopy is characterized by low interobserver agreement and poor correlation with histopathologic findings. Chromoendoscopy significantly enhances the visibility of mucosa irregularities, like metaplasia and dysplasia mucosa. Magnetically guided capsule endoscopy (MGCE) offers an alternative technology for upper GI examination. We expect the difficulties of diagnosis of neoplasm in conventional endoscopy to transfer to MGCE. Thus, we aim to chart a path for the application of chromoendoscopy on MGCE via an ex-vivo animal study.Methods: We propose a modified preparation protocol which adds a staining step to the existing MGCE preparation protocol. An optimal staining concentration is quantitatively determined for different stain types and pathologies. To that end 190 pig stomach tissue samples with and without lesion imitations were stained with different dye concentrations. Quantitative visual criteria are introduced to measure the quality of the staining with respect to mucosa and lesion visibility. Thusly determined optimal concentrations are tested in an ex-vivo pig stomach experiment under magnetic guidance of an endoscopic capsule with the modified protocol.Results: We found that the proposed protocol modification does not impact the visibility in the stomach or steerability of the endoscopy capsule. An average optimal staining concentration for the proposed protocol was found at 0.4% for Methylene blue and Indigo carmine. The lesion visibility is improved using the previously obtained optimal dye concentration.Conclusions: We conclude that chromoendoscopy may be applied in MGCE and improves mucosa and lesion visibility. Systematic evaluation provides important information on appropriate staining concentration. However, further animal and human in-vivo studies are necessary. © 2013 Mewes et al.; licensee BioMed Central Ltd.

Classical FE Analysis to Classify Parkinson’s Disease Patients - Rafael Calvo-Ariza, Nestor; Felipe Gomez-Gomez, Luis; Orozco Arroyave, Juan Rafael (Electronics, 2022)
Parkinson’s disease (PD) is a neurodegenerative condition that affects the correct functioning of the motor system in the human body. Patients exhibit a reduced capability to produce facial expressions (FEs) among different symptoms, namely hypomimia. Being a disease so hard to be detected in its early stages, automatic systems can be created to help physicians in assessing and screening patients using basic bio-markers. In this paper, we present several experiments where features are extracted from images of FEs produced by PD patients and healthy controls. Classical machine learning methods such as local binary patterns and histograms of oriented gradients are used to model the images. Similarly, a well-known classification method, namely support vector machine is used for the discrimination between PD patients and healthy subjects. The most informative regions of the faces are found with a principal component analysis algorithm. Three different FEs were modeled: angry, happy, and surprise. Good results were obtained in most of the cases; however, happiness was the one that yielded better results, with accuracies of up to 80.4%. The methods used in this paper are classical and well-known by the research community; however, their main advantage is that they provide clear interpretability, which is valuable for many researchers and especially for clinicians. This work can be considered as a good baseline such that motivates other researchers to propose new methodologies that yield better results while keep the characteristic of providing interpretability.
CRIS-Team Scopus Importer:2022-11-25
Keywords: classic techniques; FE; hypomimia; image processing; machine learning; Parkinson’s Disease

Classification-based summation of cerebral digital subtraction angiography series for image post-processing algorithms - Schuldhaus, Dominik; Spiegel, Martin; Redel, T.; Polyanskaya, Maria; Struffert, Tobias; Hornegger, Joachim; Dörfler, Arnd (Physics in Medicine and Biology, 2011)
X-ray-based 2D digital subtraction angiography (DSA) plays a major role in the diagnosis, treatment planning and assessment of cerebrovascular disease, i.e. aneurysms, arteriovenous malformations and intracranial stenosis. DSA information is increasingly used for secondary image post-processing such as vessel segmentation, registration and comparison to hemodynamic calculation using computational fluid dynamics. Depending on the amount of injected contrast agent and the duration of injection, these DSA series may not exhibit one single DSA image showing the entire vessel tree. The interesting information for these algorithms, however, is usually depicted within a few images. If these images would be combined into one image the complexity of segmentation or registration methods using DSA series would drastically decrease. In this paper, we propose a novel method automatically splitting a DSA series into three parts, i.e. mask, arterial and parenchymal phase, to provide one final image showing all important vessels with less noise and moving artifacts. This final image covers all arterial phase images, either by image summation or by taking the minimum intensities. The phase classification is done by a two-step approach. The mask/arterial phase border is determined by a Perceptron-based method trained from a set of DSA series. The arterial/parenchymal phase border is specified by a threshold-based method. The evaluation of the proposed method is two-sided: (1) comparison between automatic and medical expert-based phase selection and (2) the quality of the final image is measured by gradient magnitudes inside the vessels and signal-to-noise (SNR) outside. Experimental results show a match between expert and automatic phase separation of 93%/50% and an average SNR increase of up to 182% compared to summing up the entire series. © 2011 Institute of Physics and Engineering in Medicine.

Classification of Body Regions Based on MRI Log Files - Kuhnert, Nadine; Lindenmayr, Oliver; Maier, Andreas (Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017, 2018)
UnivIS-Import:2017-11-06:Pub.2018.tech.IMMD.IMMD5.classi
Keywords: Classification; Data Mining; Log File Analysis; System Usage

Classification of body regions based on MRI log files - Kuhnert, Nadine; Lindenmayr, Oliver; Maier, Andreas (Proceedings of the 10th International Conference on Computer Recognition Systems, CORES 2017, 2018)
Every Siemens Magnetic Resonance Imaging (MRI) system consistently writes events into log files while the system is running. The log files and their contents are constantly refined by software developers. This results in different information contents depending on the software version. One information that is missing in some log files is the examined body region. As the body region is crucial for usage analysis, we used pattern recognition methods to estimate the examined body region for software versions not logging it automatically. We learned the examined body region from a set of used MRI acquisition parameters such as grid and voxel size and could classify body region information with a classification rate up to 94.7%. We compared Bayesian Network augmented Naïve Bayes, Decision Trees, and Neural Networks, and found Neural Networks resulting in the best classification rate.<br
CRIS-Team Scopus Importer:2022-06-05
Keywords: Classification; Data mining; Log file analysis; Pattern recognition; System usage

Classification of Boundaries and Accents in Spontaneous Speech - Kießling, Andreas; Kompe, Ralf; Batliner, Anton; Niemann, Heinrich; Nöth, Elmar (Proc. of the 3rd CRIM / FORWISS Workshop, 1996)

Classification of Breast Cancer Histology Images Using Transfer Learning - Vesal, Sulaiman; Ravikumar, Nishant; Davari, Amirabbas; Ellmann, Stephan; Maier, Andreas (ICIAR 2018: Image Analysis and Recognition, 2018)

Classification of Confocal Laser Endomicroscopic Images of the Oral Cavity to Distinguish Pathological from Healthy Tissue - Jaremenko, Christian; Maier, Andreas; Steidl, Stefan; Hornegger, Joachim; Oetter, Nicolai; Knipfer, Christian; Stelzle, Florian; Neumann, Helmut (Bildverarbeitung für die Medizin 2015, 2015)
Confocal laser endomicroscopy is a recently introduced advanced imaging technique which enables microscopic imaging of the mucosa in-vivo. This technique has already been applied successfully during diagnosis of gastrointestinal diseases. Whereas for this purpose several computer aided diagnosis approaches exist, we present a classification system that is able to differentiate between healthy and pathological images of the oral cavity. Varying textural features of small rectangular regions are evaluated using random forests and support vector machines. Preliminary results reach up to 99.2% classification rate. This indicates that an automatic classification system to differentiate between healthy and pathological mucosa of the oral cavity is feasible.
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.classi_8

Classification of Daily Life Activities by Decision Level Fusion of Inertial Sensor Data - Schuldhaus, Dominik; Leutheuser, Heike; Eskofier, Björn (8th International Conference on Body Area Networks, 2013)

Classification of leukemic B-Lymphoblast cells from blood smear microscopic images with an attention-based deep learning method and advanced augmentation techniques - Marzahl, Christian; Aubreville, Marc; Voigt, Jörn; Maier, Andreas (ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, 2019)
Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer type characterized by an increased number of immature lymphocytes. It is one of the leading cancer inflicted causes of death among children and affected around 876.000 people globally in 2015. In this work, we describe methods used for the “Classification of Normal versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images” ISBI challenge. Due to the morphological similarity between malignant and normal cells, the classification of these cell types is a very challenging task, even for human experts. Deep learning using Convolutional Neural Networks (CNN) provides state-of-the-art techniques for image classification, even with limited annotation data, based on techniques like transfer learning and data augmentation. Our solution to tackle this classification problem is based upon advanced augmentation techniques to counter overfitting the small dataset and transfer learning with adaptive learning rates. Additionally, we incorporated a basic attention mechanism based on a region proposal subnetwork to boost our results for this competition. The ResNet 18 network with an additional regression head achieved a weighted F1 score of 0.8284 on the final test set and 0.8746 on the preliminary test set.
CRIS-Team Scopus Importer:2020-02-21
Keywords: Acute Lymphoblastic Leukemia; Attention modeling; Deep learning

Classification of Mitotic Cells - Potentials Beyond the Limits of Small Data Sets - Krappmann, Maximilian; Aubreville, Marc; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas (Bildverarbeitung für die Medizin 2018. Informatik aktuell, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.classi_9

Classification of Parkinson's Disease Patients-A Deep Learning Strategy - Carvajal-Castano, Helber Andres; Pérez Toro, Paula Andrea; Orozco Arroyave, Juan Rafael (Electronics, 2022)
(1) Background and objectives: Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases whose typical symptoms include bradykinesia, abnormal gait and posture, shortened strides, and other movement disorders. In this study, we present a novel framework to evaluate PD gait patterns using state of the art deep learning algorithms. A comparative analysis with three different approaches is presented and evaluated upon three groups of subjects: PD patients, Young Healthy Controls (YHC), and Elderly Healthy Controls (EHC). (2) Methods: The three approaches used in the study include: (i) The energy content of the gait signals in the frequency domain is captured with spectrograms that are used to feed a CNN model, (ii) Temporal information is incorporated by creating GRU networks, (iii) Temporal and spectral information is simultaneously captured by creating a new architecture based on CNNs and GRUs. (3) Results: Accuracies of up to 83.7% and 92.7% are found for the classification between PD vs. EHC and PD vs. YHC, respectively. According to our observations, the proposed approach based on the combination of temporal and spectral information, yields better results than others reported in the state of the art. (4) Conclusions: The results obtained in this study suggest that the combination of temporal and spectral information is more accurate than individual approaches used to classify and evaluate gait patterns in PD patients. To the best of our knowledge, this is the first study in gait analysis where temporal and spectral information is combined in an architecture of deep learning.
CRIS-Team WoS Importer:2022-09-23
Keywords: Parkinson’s disease; gait analysis; gate recurrent units; deep learning; convolutional neural networks

Classification of Polyethylene Particles and the Local CD3+ Lymphocytosis in Histological Slice Images - Steffes, Lara-Maria; Aubreville, Marc; Sesselmann, Stefan; Krenn, Veit; Maier, Andreas (Bildverarbeitung für die Medizin 2018. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.classi_2

Classification of Poverty Condition Using Natural Language Processing - Muneton-Santa, Guberney; Escobar-Grisales, Daniel; Orlando Lopez-Pabon, Felipe; Pérez Toro, Paula Andrea; Orozco Arroyave, Juan Rafael (Social Indicators Research, 2022)
This work introduces a methodology to classify between poor and extremely poor people through Natural Language Processing. The approach serves as a baseline to understand and classify poverty through the people's discourses using machine learning algorithms. Based on classical and modern word vector representations we propose two strategies for document level representations: (1) document-level features based on the concatenation of descriptive statistics and (2) Gaussian mixture models. Three classification methods are systematically evaluated: Support Vector Machines, Random Forest, and Extreme Gradient Boosting. The fourth best experiments yielded around 55% of accuracy, while the embeddings based on GloVe word vectors yielded a sensitivity of 79.6% which could be of great interest for the public policy makers to accurately find people who need to be prioritized in social programs.
CRIS-Team WoS Importer:2022-02-18

Classification of Running Surface on an Embedded System - a Digital Sports Example Application - Eskofier, Björn; Hornegger, Joachim; Oleson, Mark; Munson, Ian; Krabbe, Berthold; DiBenedetto, Christian (Innovationen bei der Erfassung und Analyse bioelektrischer und biomagnetischer Signale, 2008)

Classification of stuttering – The ComParE challenge and beyond - Bayerl, Sebastian P.; Gerczuk, Maurice; Batliner, Anton; Bergler, Christian; Amiriparian, Shahin; Schuller, Björn; Nöth, Elmar; Riedhammer, Korbinian (Computer Speech and Language, 2023)
The ACM Multimedia 2022 Computational Paralinguistics Challenge (ComParE) featured a sub-challenge on the classification of stuttering in order to bring attention to this important topic and engage a wider research community. Stuttering is a complex speech disorder characterized by blocks, prolongations of sounds and syllables, and repetitions of sounds and words. Accurately classifying the symptoms of stuttering has implications for the development of self-help tools and specialized automatic speech recognition systems (ASR) that can handle atypical speech patterns. This paper provides a review of the challenge contributions and improves upon them with new state-of-the-art classification results for the KSF-C dataset, and explores cross-language training to demonstrate the potential of datasets in multiple languages. To facilitate further research and reproducibility, the full KSF-C dataset, including test-set labels, is also released.
CRIS-Team Scopus Importer:2023-04-28
Keywords: ComParE challenge; Dysfluency; Paralinguistics; Pathological speech; Stuttering

Classification of Surfaces and Inclinations During Outdoor Running Using Shoe-Mounted Inertial Sensors - Schuldhaus, Dominik; Kugler, Patrick; Leible, Magnus; Jensen, Ulf; Schlarb, Heiko; Eskofier, Björn (Proceedings of 21st International Conference on Pattern Recognition, 2012)
Embedded mobile systems for analysis and classificationbecome more and more important in the field ofsports and sports science. Small and lightweight sensorsin sportswear offer the possibility to monitor theathletes in a realistic environment, e.g. during an outdoorrun. During the activity, the sportswear can automaticallyadapt to the current environment and henceoptimizes the performance of the athlete. A major needis a running shoe, which can automatically be adaptedto the current ground.In this paper, a classification system was developed,which distinguished between different surfaces and inclinationsbased on inertial sensors. They were placedon the heel of a running shoe and acquired kinematicdata of 21 subjects. For each subject, several roundsof an one hour outdoor run were available and wereused for the evaluation of the system. The classificationsystem reached a mean classification rate of more than80 %.

Classification of User States with Physiological Signals: On-line Generic Features vs. Specialized (with corrections) - Hönig, Florian Thomas; Wagner, Johannes; Batliner, Anton; Nöth, Elmar (Proceedings of the 17th European Signal Processing Conference (EUSIPCO), 2009)

Classification With Truncated l1 Distance Kernel - Huang, Xiaolin; Suykens, Johan A. K.; Wang, Shuning; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Neural Networks, 2018)
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.classi_7
Keywords: Classification; indefinite kernel; piecewise linear (PWL)

Classifying Dementia in the Presence of Depression: A Cross-Corpus Study - Braun, Franziska; Pérez Toro, Paula Andrea; Bayerl, Sebastian P.; Pérez-Toro, Paula A.; Hönig, Florian; Lehfeld, Hartmut; Hillemacher, Thomas; Nöth, Elmar; Bocklet, Tobias; Riedhammer, Korbinian (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023)
Automated dementia screening enables early detection and intervention, reducing costs to healthcare systems and increasing quality of life for those affected. Depression has shared symptoms with dementia, adding complexity to diagnoses. The research focus so far has been on binary classification of dementia (DEM) and healthy controls (HC) using speech from picture description tests from a single dataset. In this work, we apply established baseline systems to discriminate cognitive impairment in speech from the semantic Verbal Fluency Test and the Boston Naming Test using text, audio and emotion embeddings in a 3-class classification problem (HC vs. MCI vs. DEM). We perform cross-corpus and mixed-corpus experiments on two independently recorded German datasets to investigate generalization to larger populations and different recording conditions. In a detailed error analysis, we look at depression as a secondary diagnosis to understand what our classifiers actually learn.
CRIS-Team Scopus Importer:2023-09-29
Keywords: cross-corpus analysis; dementia screening; neuropsychological tests; pathological speech

Classifying glaucoma with image-based features from fundus photographs - Bock, Rüdiger; Meier, Jörg; Michelson, Georg; Nyul, Laszlo Gabor; Hornegger, Joachim (Springer-verlag, 2007)
Glaucoma is one of the most common causes of blindness and it is becoming even more important considering the ageing society. Because healing of died retinal nerve fibers is not possible early detection and prevention is essential. Robust, automated mass-screening will help to extend the symptom-free life of affected patients. We devised a novel, automated, appearance based glaucoma classification system that does not depend on segmentation based measurements. Our purely data-driven approach is applicable in large-scale screening examinations. It applies a standard pattern recognition pipeline with a 2-stage classification step. Several types of image-based features were analyzed and are combined to capture glaucomatous structures. Certain disease independent variations such as illumination inhomogeneities, size differences, and vessel structures are eliminated in the preprocessing phase. The "vesselfree" images and intermediate results of the methods are novel representations of the data for the physicians that may provide new insight into and help to better understand glaucoma. Our system achieves 86 % success rate on a data set containing a mixture of 200 real images of healthy and glaucomatous eyes. The performance of the system is comparable to human medical experts in detecting glaucomatous retina fundus images. © Springer-Verlag Berlin Heidelberg 2007.
UnivIS-Import:2015-04-16:Pub.2007.tech.IMMD.IMMD5.classi

CLCNet: Deep learning-based noise reduction for hearing aids using complex linear coding - Schröter, Hendrik; Rosenkranz, Tobias; Escalante Banuelos, Alberto; Aubreville, Marc; Maier, Andreas (ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020)
Noise reduction is an important part of modern hearing aids and is included in most commercially available devices. Deep learning-based state-of-the-art algorithms, however, either do not consider real-time and frequency resolution constrains or result in poor quality under very noisy conditions. To improve monaural speech enhancement in noisy environments, we propose CLCNet, a framework based on complex valued linear coding. First, we define complex linear coding (CLC) motivated by linear predictive coding (LPC) that is applied in the complex frequency domain. Second, we propose a framework that incorporates complex spectrogram input and coefficient output. Third, we define a parametric normalization for complex valued spectrograms that complies with low-latency and on-line processing. Our CLCNet was evaluated on a mixture of the EUROM database and a real-world noise dataset recorded with hearing aids and compared to traditional real-valued Wiener-Filter gains.
Keywords: noise reduction; speech enhancement; LPC; hearing aid signal processing; deep learning

Clinical Data Evaluation of C-arm-based Motion Compensated Coronary Artery Reconstruction - Schwemmer, Chris; Lauritsch, Günter; Kleinfeld, Albrecht; Rohkohl, Christopher; Müller, Kerstin; Hornegger, Joachim (Proceedings of the third international conference on image formation in x-ray computed tomography, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.clinic_7

Clinical Evaluation of Endorientation:Gravity related rectification for endoscopic images - Höller, Kurt Emmerich; Penne, Jochen; Hornegger, Joachim; Schneider, Armin; Gillen, Sonja; Feußner, Hubertus; Jahn, Jasper; Gutierrez, Javier; Wittenberg, Thomas (6th International Symposium on Image and Signal Processing and Analysis, ISPA 2009, 2009)
Providing a stable horizon on endoscopic images especially in non-rigid endoscopic surgery (particularly NOTES) is still an open issue. Image rectification can be realized with a tiny MEMS tri-axial inertial sensor that is placed on the tip of an endoscope. By measuring the impact of gravity on each of the three orthogonal axes the rotation angle can be estimated with some calculations out of these three acceleration values. Achievable repetition rate for angle termination has to be above the usual endoscopic video frame rate of 25-30Hz. The accelerometer frame rate can be set up to 400 Hz. Accuracy has to be less than one degree even within periods of high movement and superposed acceleration. Therefore an intelligent downsampling algorithm has to be found. The image rotation is performed by rotating digitally a capture of the endoscopic analog video signal. Improvements and benefits have been evaluated in a clinical evaluation: For different peritoneoscopic tasks time was taken and instrument position was tracked and recorded.

Closing the Calibration Loop: An Inside-Out-Tracking Paradigm for Augmented Reality in Orthopedic Surgery - Hajek, Jonas; Unberath, Mathias; Fotouhi, Javad; Bier, Bastian; Lee, Sing Chun; Osgood, Greg; Maier, Andreas; Armand, Mehran; Navab, Nassir (MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV, 2018)
CRIS-Team WoS Importer:2019-08-09

Cloud based Energy Management System Compatible with the Industry 4.0 Requirements - Javied, Tallal; Huprich, Sven; Franke, Jörg (IFAC PAPERSONLINE, 2019)
CRIS-Team WoS Importer:2019-11-22

Cluster Analysis of Glaucoma Patients Using the Retinal Nerve Fiber Layer Thickness of the Optic Nerve and DTI Parameters of the Optic Radiation - Michelson, Georg; Wärntges, Simone; Engelhorn, Tobias; El-Rafei, Ahmed Mohamed Ibrahim; Hornegger, Joachim; Dörfler, Arnd (Clinical and Experimental Ophthalmology, 2011)

CNNs for automatic glaucoma assessment using fundus images: an extensive validation - Diaz-Pinto, Andres; Morales, Sandra; Naranjo, Valery; Köhler, Thomas; Mossi, Jose M.; Navea, Amparo (Biomedical Engineering Online, 2019)
BackgroundMost current algorithms for automatic glaucoma assessment using fundus images rely on handcrafted features based on segmentation, which are affected by the performance of the chosen segmentation method and the extracted features. Among other characteristics, convolutional neural networks (CNNs) are known because of their ability to learn highly discriminative features from raw pixel intensities.MethodsIn this paper, we employed five different ImageNet-trained models (VGG16, VGG19, InceptionV3, ResNet50 and Xception) for automatic glaucoma assessment using fundus images. Results from an extensive validation using cross-validation and cross-testing strategies were compared with previous works in the literature.ResultsUsing five public databases (1707 images), an average AUC of 0.9605 with a 95% confidence interval of 95.92-97.07%, an average specificity of 0.8580 and an average sensitivity of 0.9346 were obtained after using the Xception architecture, significantly improving the performance of other state-of-the-art works. Moreover, a new clinical database, ACRIMA, has been made publicly available, containing 705 labelled images. It is composed of 396 glaucomatous images and 309 normal images, which means, the largest public database for glaucoma diagnosis. The high specificity and sensitivity obtained from the proposed approach are supported by an extensive validation using not only the cross-validation strategy but also the cross-testing validation on, to the best of the authors' knowledge, all publicly available glaucoma-labelled databases.ConclusionsThese results suggest that using ImageNet-trained models is a robust alternative for automatic glaucoma screening system. All images, CNN weights and software used to fine-tune and test the five CNNs are publicly available, which could be used as a testbed for further comparisons.
CRIS-Team WoS Importer:2019-04-09

CoachLea: an Android Application to Evaluate the Speech Production and Perception of Children with Hearing Loss - Schäfer, Paula; Pérez Toro, Paula Andrea; Klumpp, Philipp; Orozco Arroyave, Juan Rafael; Nöth, Elmar; Maier, Andreas; Abad, A.; Schuster, M.; Arias Vergara, Tomás (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2022)
Hearing loss can affect children's language, speech, and social skills development. Hearing problems can result from impaired auditory feedback due to various reasons such as trauma, a clinical condition, genetic alterations, and infections, among others. Early treatment is the key to successful hearing and speech rehabilitation if the hearing loss occurs before or during spoken language acquisition. In this work, we present CoachLea, an Android application to support the clinical evaluation and therapy of the speech production and perception of children with hearing loss. The app includes numerous daily exercises to capture speech and hearing data continuously and longitudinally using a game-like user interface. Speech exercises include the “Snail race”, “Animal Sounds”, and “Image identification”, whereas the hearing exercise consists of a word identification game based on minimal pairs with speech-in-noise recordings. In the long term, CoachLea aims to be a tool that supports the therapy of children with hearing loss.
CRIS-Team Scopus Importer:2022-10-28
Keywords: Cochlear implant; Hearing aid; Hearing loss; Smartphone application; Speech production

CoachLea: an Android Application to Evaluate the Speech Production and Perception of Children with Hearing Loss - Schäfer, Paula; Pérez Toro, Paula Andrea; Klumpp, Philipp; Orozco-Arroyave, Juan Rafael; Nöth, Elmar; Maier, Andreas; Abad, Alberto; Schuster, Maria; Arias Vergara, Tomás (23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022, 2022)
Keywords: Cochlear implant; Smartphone application; Speech production; Hearing aid; Hearing loss

Cognitive Determinants of Dysarthria in Parkinson's Disease: An Automated Machine Learning Approach - Garcia, Adolfo M.; Arias-Vergara, Tomas; C. Vasquez-Correa, Juan; Nöth, Elmar; Schuster, Maria; Welch, Ariane E.; Bocanegra, Yamile; Baena, Ana; Orozco-Arroyave, Juan R. (Movement Disorders, 2021)
Background Dysarthric symptoms in Parkinson's disease (PD) vary greatly across cohorts. Abundant research suggests that such heterogeneity could reflect subject-level and task-related cognitive factors. However, the interplay of these variables during motor speech remains underexplored, let alone by administering validated materials to carefully matched samples with varying cognitive profiles and combining automated tools with machine learning methods. Objective We aimed to identify which speech dimensions best identify patients with PD in cognitively heterogeneous, cognitively preserved, and cognitively impaired groups through tasks with low (reading) and high (retelling) processing demands. Methods We used support vector machines to analyze prosodic, articulatory, and phonemic identifiability features. Patient groups were compared with healthy control subjects and against each other in both tasks, using each measure separately and in combination. Results Relative to control subjects, patients in cognitively heterogeneous and cognitively preserved groups were best discriminated by combined dysarthric signs during reading (accuracy = 84% and 80.2%). Conversely, patients with cognitive impairment were maximally discriminated from control subjects when considering phonemic identifiability during retelling (accuracy = 86.9%). This same pattern maximally distinguished between cognitively spared and impaired patients (accuracy = 72.1%). Also, cognitive (executive) symptom severity was predicted by prosody in cognitively preserved patients and by phonemic identifiability in cognitively heterogeneous and impaired groups. No measure predicted overall motor dysfunction in any group. Conclusions Predominant dysarthric symptoms appear to be best captured through undemanding tasks in cognitively heterogeneous and preserved cohorts and through cognitively loaded tasks in patients with cognitive impairment. Further applications of this framework could enhance dysarthria assessments in PD. (c) 2021 International Parkinson and Movement Disorder Society
CRIS-Team WoS Importer:2021-08-27

Coincidence detection and integration behavior in spiking neural networks - Stoll, Andreas; Maier, Andreas; Krauß, Patrick; Gerum, Richard; Schilling, Achim (Cognitive Neurodynamics, 2023)
Recently, the interest in spiking neural networks (SNNs) remarkably increased, as up to now some key advances of biological neural networks are still out of reach. Thus, the energy efficiency and the ability to dynamically react and adapt to input stimuli as observed in biological neurons is still difficult to achieve. One neuron model commonly used in SNNs is the leaky-integrate-and-fire (LIF) neuron. LIF neurons already show interesting dynamics and can be run in two operation modes: coincidence detectors for low and integrators for high membrane decay times, respectively. However, the emergence of these modes in SNNs and the consequence on network performance and information processing ability is still elusive. In this study, we examine the effect of different decay times in SNNs trained with a surrogate-gradient-based approach. We propose two measures that allow to determine the operation mode of LIF neurons: the number of contributing input spikes and the effective integration interval. We show that coincidence detection is characterized by a low number of input spikes as well as short integration intervals, whereas integration behavior is related to many input spikes over long integration intervals. We find the two measures to linearly correlate via a correlation factor that depends on the decay time. Thus, the correlation factor as function of the decay time shows a powerlaw behavior, which could be an intrinsic property of LIF networks. We argue that our work could be a starting point to further explore the operation modes in SNNs to boost efficiency and biological plausibility.
CRIS-Team Scopus Importer:2023-12-22
Keywords: Artificial intelligence; Coincidence detection; Computational modeling; Leaky-integrate-and-fire neuron; Neural networks

Collinearity and Sample Coverage Issues in the Objective Measurement of Vocal Quality: The Case of Roughness and Breathiness - Ferrer Riesgo, Carlos Ariel; Haderlein, Tino; Maryn, Youri; de Bodt, Marc; Nöth, Elmar (Journal of Speech Language and Hearing Research, 2018)
Purpose: The aim of the study was to address the reported inconsistencies in the relationship between objective acoustic measures and perceptual ratings of vocal quality. Method: This tutorial moves away from the more widely examined problems related to obtaining the perceptual ratings and the acoustic measures and centers in less scrutinized issues regarding the procedure to establish the correspondence. Expressions for the most common measure of association between perceptual and acoustic measures (Pearson’s r) are derived using a multiple linear regression model. The particular case where the multiple linear regression involves only roughness and breathiness is discussed to illustrate the issues. Results: Most problems reported regarding inconsistent findings in the relationship between given acoustic measures and particular perceptual ratings could be linked to sample properties not directly related to the actual relationship. The influential sample properties are the collinearity between the regressors in the multiple linear regression and their relative variances. Recommendations on how to rule out this possible cause of inconsistency are given, varying in scope from data collection, reporting, manipulation, and results interpretation. Conclusions: The problems described can be extended to more general cases than the exemplified roughness and breathiness sample’s coverage. Ruling out this possible cause of inconsistency would increase the validity of the results reported.
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.collin

Co-localized augmented human and X-ray observers in collaborative surgical ecosystem - Fotouhi, Javad; Unberath, Mathias; Song, Tianyu; Hajek, Jonas; Lee, Sing Chun; Bier, Bastian; Maier, Andreas; Osgood, Greg; Armand, Mehran; Navab, Nassir (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose: Image-guided percutaneous interventions are safer alternatives to conventional orthopedic and trauma surgeries. To advance surgical tools in complex bony structures during these procedures with confidence, a large number of images is acquired. While image-guidance is the de facto standard to guarantee acceptable outcome, when these images are presented on monitors far from the surgical site the information content cannot be associated easily with the 3D patient anatomy. Methods: In this article, we propose a collaborative augmented reality (AR) surgical ecosystem to jointly co-localize the C-arm X-ray and surgeon viewer. The technical contributions of this work include (1) joint calibration of a visual tracker on a C-arm scanner and its X-ray source via a hand-eye calibration strategy, and (2) inside-out co-localization of human and X-ray observers in shared tracking and augmentation environments using vision-based simultaneous localization and mapping. Results: We present a thorough evaluation of the hand-eye calibration procedure. Results suggest convergence when using 50 pose pairs or more. The mean translation and rotation errors at convergence are 5.7 mm and 0. 26 ∘, respectively. Further, user-in-the-loop studies were conducted to estimate the end-to-end target augmentation error. The mean distance between landmarks in real and virtual environment was 10.8 mm. Conclusions: The proposed AR solution provides a shared augmented experience between the human and X-ray viewer. The collaborative surgical AR system has the potential to simplify hand-eye coordination for surgeons or intuitively inform C-arm technologists for prospective X-ray view-point planning.
CRIS-Team Scopus Importer:2019-08-06
Keywords: Augmented reality; C-arm; Calibration; Surgery; X-ray

COLOMBIAN DIALECT RECOGNITION BASED ON INFORMATION EXTRACTED FROM SPEECH AND TEXT SIGNALS - Escobar-Grisales, D.; Rios-Urrego, C. D.; Lopez-Santander, D. A.; Gallo-Aristizabal, J. D.; Vasquez-Correa, J. C.; Nöth, Elmar; Orozco-Arroyave, J. R. (2021 IEEE AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING WORKSHOP (ASRU), 2021)
CRIS-Team WoS Importer:2022-05-27

Colombian Dialect Recognition from Call-Center Conversations Using Fusion Strategies - Escobar-Grisales, D.; Rios-Urrego, C. D.; Gallo-Aristizabal, J. D.; Lopez-Santander, D. A.; Calvo-Ariza, N. R.; Nöth, Elmar; Orozco-Arroyave, J. R. (APPLIED COMPUTER SCIENCES IN ENGINEERING, WEA 2022, 2022)
CRIS-Team WoS Importer:2023-02-24

Color constancy and non-uniform illumination: Can existing algorithms work? - Bleier, Michael; Riess, Christian; Beigpour, S.; Eibenberger, E.; Angelopoulou, E.; Tröger, Tobias; Kaup, André (2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011, 2011)
The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of real-world illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression. © 2011 IEE

Color Constancy in 3D-2D Face Recognition - Meyer, Manuel; Riess, Christian; Angelopoulou, Elli; Evangelopoulos, Georgios; Kakadiaris, Ioannis (Biometric and Surveillance Technology for Human and Activity Identification, 2013)
Keywords: Face recognition; Color constancy; Illumination normalization; Illumination invariance

Combination of Markerless Surrogates for Motion Estimation in Radiation Therapy - Geimer, Tobias; Unberath, Mathias; Taubmann, Oliver; Bert, Christoph; Maier, Andreas (Computer Assisted Radiology and Surgery (CARS) 2016: Proceedings of the 30th International Congress and Exhibition, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.combin_2
Keywords: Motion Modeling; X-ray; Range Imaging; Dimensionality Reduction; Regression

Combined Accelerometer and EMG Analysis to Differentiate Essential Tremor from Parkinson’s Disease - Haji Ghassemi, Nooshin; Marxreiter, Franz; Pasluosta, Cristian Federico; Schlachetzki, Johannes; Schramm, A; Eskofier, Björn; Klucken, Jochen (38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society of the IEEE Engineering in Medicine and Biology Society EMBC'16, 2016)

Combined analysis of sensor data from hand and gait motor function improves automatic recognition of Parkinson's disease - Barth, Jens; Sünkel, Michael; Bergner, Katharina; Schickhuber, Gerald; Winkler, Jürgen; Klucken, Jochen; Eskofier, Björn (Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, 2012)
Objective and rater independent analysis of movement impairment is one of the most challenging tasks in medical engineering. Especially assessment of motor symptoms defines the clinical diagnosis in Parkinson's disease (PD). A sensor-based system to measure the movement of the upper and lower extremities would therefore complement the clinical evaluation of PD. In this study two different sensor-based systems were combined to assess movement of 18 PD patients and 17 healthy controls. First, hand motor function was evaluated using a sensor pen with integrated accelerometers and pressure sensors, and second, gait function was assessed using a sports shoe with attached inertial sensors (gyroscopes, accelerometers). Subjects performed standardized tests for both extremities. Features were calculated from sensor signals to differentiate between patients and controls. For the latter, pattern recognition methods were used and the performance of four classifiers was compared. In a first step classification was done for every single system and in a second step for combined features of both systems. Combination of both motor task assessments substantially improved classification rates to 97% using the AdaBoost classifier for the experiment patients vs. controls. The combination of two different analysis systems led to enhanced, more stable, objective, and rater independent recognition of motor impairment. The method can be used as a complementary diagnostic tool for movement disorders.

Combined Cardiac and Respiratory Motion Compensation for Atrial Fibrillation Ablation Procedures - Brost, Alexander; Wu, Wen; Koch, Martin; Wimmer, Andreas; Chen, Terrence; Liao, Rui; Hornegger, Joachim; Strobel, Norbert (Lecture Notes in Computer Science, 2011)
Catheter ablation of atrial fibrillation has become an accepted treatment option if a patient no longer responds to or tolerates drug therapy. A main goal is the electrical isolation of the pulmonary veins attached to the left atrium. Catheter ablation may be performed under fluoroscopic image guidance. Due to the rather low soft-tissue contrast of X-ray imaging, the heart is not visible in these images. To overcome this problem, overlay images from pre-operative 3-D volumetric data can be used to add anatomical detail. Unfortunately, this overlay is compromised by respiratory and cardiac motion. In the past, two methods have been proposed to perform motion compensation. The first approach involves tracking of a circumferential mapping catheter placed at an ostium of a pulmonary vein. The second method relies on a motion estimate obtained by localizing an electrode of the coronary sinus (CS) catheter. We propose a new motion compensation scheme which combines these two methods. The effectiveness of the proposed method is verified using 19 real clinical data sets. The motion in the fluoroscopic images was estimated with an overall average error of 0.55 mm by tracking the circumferential mapping catheter. By applying an algorithm involving both the CS catheter and the circumferential mapping catheter, we were able to detect motion of the mapping catheter from one pulmonary vein to another with a false positive rate of 5.8 %.
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD5.combin_3

Combining 2-D and 3-D weight-bearing x-ray images: Application to preoperative implant planning in the knee - Luckner, Christoph; Herbst, Magdalena; Fuhrmann, Michael; Ritschl, Ludwig; Kappler, Steffen; Maier, Andreas (Informatik aktuell, 2020)
Osteoarthritis is a joint disease that commonly affects the hands, feet, spine, as well as the large weight-bearing joints, i. e., the hip, and knees. Worldwide, about 3.6% of the population suffer from osteoarthritis of the knee. If the symptoms are too severe to be treated with medication, the solution is often a total replacement. The precise preoperative planning of implants is a crucial task to achieve a good patient outcome. For planning, usually hybrid 2-D/3-D approaches are used. The main drawback of theses hybrid methods is the different patient positions during the acquisition, namely lying for the 3-D scan and standing for the 2-D scan. We proposed a method that allows acquiring both images in standing positions under natural weight-bearing without having to reposition the patient. To show the feasibility, we provide images from an anthropomorphic leg phantom. A preliminary study with medical experts has shown that the results are promising.
CRIS-Team Scopus Importer:2020-04-21

Combining Efforts for Improving Automatic Classification of Emotional User States - Batliner, Anton; Steidl, Stefan; Schuller, Björn; Seppi, Dino; Laskowski, Kornel; Vogt, Thurid; Devillers, Laurence; Vidrascu, Laurence; Amir, Noam; Kessous, Loic; Aharonson, Vered (Language Technologies, IS-LTC 2006, 2006)
Classification performance of emotional user states found in realistic, spontaneous speech is not very high, compared to the performance reported for acted speech in the literature. This might be partly due to the difficulty of providing reliable annotations, partly due to suboptimal feature vectors used for classification, and partly due to the difficulty of the task. In this paper, we present a co-operation between several sites, using a thoroughly processed emotional database. For the four-class problem motherese/neutral/emphatic/angry, we first report classification performance computed independently at each site. Then we show that by using all the best features from each site in a combined classification, and by combining classifier outputs within the ROVER framework, classification results can be improved; all feature types and features from all sites contributed.

Combining phonological and acoustic ASR-free features for pathological speech intelligibility assessment - Middag, Catherine; Bocklet, Tobias; Martens, Jean-Pierre; Nöth, Elmar (Proceedings of the 12th Annual Conference of the International Speech Communication Association, 2011)

Combining semantic word classes and sub-word unit speech recognition for robust OOV detection - Horndasch, Axel; Batliner, Anton; Kaufhold, Caroline; Nöth, Elmar (17th Annual Conference of the International Speech Communication Association (INTERSPEECH 2016): Understanding Speech Processing in Humans and Machines, 2016)
Out-of-vocabulary words (OOVs) are often the main reason for the failure of tasks like automated voice searches or human-machine dialogs. This is especially true if rare but task-relevant content words, e.g. person or location names, are not in the recognizer's vocabulary. Since applications like spoken dialog systems use the result of the speech recognizer to extract a semantic representation of a user utterance, the detection of OOVs as well as their (semantic) word class can support to manage a dialog successfully. In this paper we suggest to combine two well-known approaches in the context of OOV detection: semantic word classes and OOV models based on sub-word units. With our system, which builds upon the widely used Kaldi speech recognition toolkit, we show on two different data sets that - compared to other methods - such a combination improves OOV detection performance for open word classes at a given false alarm rate. Another result of our approach is a reduction of the word error rate (WER).
Keywords: OOV detection and classification; Kaldi; Sub-word unit speech recognition; Semantic word classes

Combining Ultrasound and X-Ray Imaging for Mammography: A Prototype Design - Li, Qiuting; Luckner, Christoph; Hertel, Madeleine; Radicke, Marcus; Maier, Andreas (Informatik aktuell, 2019)
This study aims at the combination of 3D breast ultrasound and 2D mammography images to improve the accuracy of diagnosis of breast cancer. It was shown that ultrasound breast imaging has advantages for differentiating cysts and solid masses which are not visible in an X-ray image. Moreover, the specificity in X-ray imaging decreases with an increasing breast thickness, so that ultrasound is usually used as an adjunct to X-ray breast imaging. A fully automatic system to obtain both 2D mammography and 3D ultrasound images is used. The alignment of a 2D mammography image in the cone-beam coordinate system and 3D ultrasound image in a Cartesian coordinate system is the essential task in this study. We have shown that deviations up to 23 mm caused by the cone-beam system can be calculated and corrected utilizing the geometry information of the hardware. The multimodal image reading tool is presented in a GUI for clinical diagnosis. The presented setup might lead to a distinct improvement in efficiency and add diagnostic value to the acquisition.
CRIS-Team Scopus Importer:2019-05-14

Combining Ultrasound and X-Ray Imaging for Mammography - Li, Qiuting; Luckner, Christoph; Hertel, Madeleine; Radicke, Marcus; Maier, Andreas (Bildverarbeitung für die Medizin 2019, 2019)

Combining Visual and Linguistic Models for a Robust Recipient Line Recognition in Historical Documents - Mayr, Martin; Felker, Alex; Maier, Andreas; Christlein, Vincent (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
Automatically extracting targeted information from historical documents is an important task in the field of document analysis and eases the work of historians when dealing with huge corpora. In this work, we investigate the idea of retrieving the recipient transcriptions from the Nuremberg letterbooks of the 15th century. This task can be solved with fundamentally different ways of approaching it. First, detecting recipient lines solely based on visual features and without any explicit linguistic feedback. Here, we use a vanilla U-Net and an attention-based U-Net as representatives. Second, linguistic feedback can be used to classify each line accordingly. This is done on the one hand with handwritten text recognition (HTR) for predicting the transcriptions and on top of it a light-wight natural language processing (NLP) model distinguishing whether the line is a recipient line or not. On the other hand, we adapt a named entity recognition transformer model. The system jointly performs the line transcription and the recipient line recognition. For improving the performance, we investigated all the possible combinations with the different methods. In most cases the combined output probabilities outperformed the single approaches. The best combination achieved on the hard test set an F1 score of 80% and recipient line recognition accuracy of about 96% while the best single approach only reached about 74% and 94%, respectively.
CRIS-Team Scopus Importer:2022-06-10
Keywords: Handwritten text recognition; Natural language processing; Recipient recognition; Semantic image segmentation

Common Phone: A Multilingual Dataset for Robust Acoustic Modelling - Klumpp, Philipp; Arias-Vergara, Tomas; Perez-Toro, Paula-Andrea; Nöth, Elmar; Orozco-Arroyave, Juan Rafael (LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, 2022)
CRIS-Team WoS Importer:2023-02-24

Comparative analysis of unsupervised algorithms for breast MRI lesion segmentation - Vesal, Sulaiman; Ravikumar, Nishant; Ellmann, Stephan; Maier, Andreas (Informatik aktuell, 2018)
Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors. However, this is a challenging task, since breast lesions have sophisticated shape, topological structure, and variation in the intensity distribution. In this paper, we evaluated the performance of three unsupervised algorithms for the task of breast Magnetic Resonance (MRI) lesion segmentation, namely, Gaussian Mixture Model clustering, K-means clustering and a markercontrolled Watershed transformation based method. All methods were applied on breast MRI slices following selection of regions of interest (ROIs) by an expert radiologist and evaluated on 106 subject’s images, which include 59 malignant and 47 benign lesions. Segmentation accuracy was evaluated by comparing our results with ground truth masks, using the Dice similarity coefficient (DSC), Jaccard index (JI), Hausdorff distance and precision-recall metrics. The results indicate that the marker-controlled Watershed transformation outperformed all other algorithms investigated.
CRIS-Team Scopus Importer:2022-06-05

Comparative Evaluation of Interactive Segmentation Approaches - Amrehn, Mario; Glasbrenner, Jens; Steidl, Stefan; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
Image segmentation is a key technique in image processing with the goal to extract important objects from the image. This evaluation study focuses on the segmentation quality of three different interactive segmentation techniques, namely Region Growing, Watershed and the cellular automaton based GrowCut algorithm. Three different evaluation measures are computed to compare the segmentation quality of each algorithm: Rand Index, Mutual Information, and the Dice Coefficient. For the images in the publicly available ground truth data base utilized for the evaluation, the GrowCut method has a slight advantage over the other two. The presented results provide insight into the performance and the characteristics with respect to the image quality of each tested algorithm.
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.compar_6
Keywords: medical; image segmentation; 3-D; lesion; tumor; liver; hepatic; cancer

Comparative evaluation of linear interpolation models for iterative reconstruction in X-ray CT - Schmitt, Katharina; Schöndube, Harald; Stiersdorfer, Karl; Hornegger, Joachim; Noo, Frédéric (Medical Imaging 2013: Physics of Medical Imaging, 2013)
The forward projection operator is a key component of every iterative reconstruction method in X-ray computed tomography (CT). Besides the choices being made in the definition of the objective function and associated constraints, the forward projection model affects both bias and noise properties of the reconstruction. In this work, we compare three important forward projection models that rely on linear interpolation: the Joseph method, the distance-driven method, and the image representation using B-splines of order n = 1. The comparison focuses on bias and noise in the image as a function of the resolution. X-ray CT data that are simulated in fan-beam geometry with two different magnification factors are used. © 2013 SPIE.

Comparative Evaluation of Two Registration-based Segmentation Algorithms: Application to Whole Heart Segmentation in CT - Unberath, Mathias; Maier, Andreas; Fleischmann, Dominik; Hornegger, Joachim; Fahrig, Rebecca (Proceedings of the GRC, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.compar

Comparative Study of Deep Learning Models for Optical Coherence Tomography Angiography - Jiang, Zhe; Huang, Zhiyu; Qiu, Bin; Liu, Xi; Meng, Xiangxi; You, Yunfei; Liu, Gangjun; Zhou, Chuanqing; Yang, Kun; Maier, Andreas; Ren, Qiushi; Lu, Yanye (Biomedical Optics Express, 2020)
Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Meanwhile, deep learning has achieved rapid development in image-to-image translation tasks. Some studies have proposed applying deep learning models to OCTA reconstruction and have obtained preliminary results. However, current studies are mostly limited to a few specific deep neural networks. In this paper, we conducted a comparative study to investigate OCTA reconstruction using deep learning models. Four representative network architectures including single-path models, U-shaped models, generative adversarial network (GAN)-based models and multi-path models were investigated on a dataset of OCTA images acquired from rat brains. Three potential solutions were also investigated to study the feasibility of improving performance. The results showed that U-shaped models and multi-path models are two suitable architectures for OCTA reconstruction. Furthermore, merging phase information should be the potential improving direction in further research.<br

Comparative Study on Classifying Gait With a Single Trunk-Mounted Inertial-Magnetic Measurement Unit - Full, Katharina; Leutheuser, Heike; Schlessmann, Jason; Armitage, Roger; Eskofier, Björn (Wearable and Implantable Body Sensor Networks (BSN), 2015 IEEE 12th International Conference on, 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.compar_7

Comparative Validation of Single-Shot Optical Techniques for Laparoscopic 3-D Surface Reconstruction - Maier-Hein, L.; Groch, A.; Bartoli, A.; Bodenstedt, S.; Boissonnat, G.; Chang, P-L; Clancy, N. T.; Elson, D. S.; Haase, Sven; Heim, E.; Hornegger, Joachim; Jannin, P.; et al. (IEEE Transactions on Medical Imaging, 2014)
Intra-operative imaging techniques for obtaining the shape and morphology of soft-tissue surfaces in vivo are a key enabling technology for advanced surgical systems. Different optical techniques for 3-D surface reconstruction in laparoscopy have been proposed, however, so far no quantitative and comparative validation has been performed. Furthermore, robustness of the methods to clinically important factors like smoke or bleeding has not yet been assessed. To address these issues, we have formed a joint international initiative with the aim of validating different state-of-the-art passive and active reconstruction methods in a comparative manner. In this comprehensive in vitro study, we investigated reconstruction accuracy using different organs with various shape and texture and also tested reconstruction robustness with respect to a number of factors like the pose of the endoscope as well as the amount of blood or smoke present in the scene. The study suggests complementary advantages of the different techniques with respect to accuracy, robustness, point density, hardware complexity and computation time. While reconstruction accuracy under ideal conditions was generally high, robustness is a remaining issue to be addressed. Future work should include sensor fusion and in vivo validation studies in a specific clinical context. To trigger further research in surface reconstruction, stereoscopic data of the study will be made publically available at www.open-CAS.com upon publication of the paper.
Keywords: Biomedical imaging; endoscopes; optical imaging; stereo image processing; stereo vision; surgery

Comparing axial CT slices in quantized N-dimensional SURF descriptor space to estimate the visible body region - Feulner, Johannes; Zhou, S. Kevin; Angelopoulou, Elli; Seifert, Sascha; Hornegger, Joachim; Comaniciu, Dorin; Cavallaro, Alexander Josef (Computerized Medical Imaging and Graphics, 2011)
In this paper, a method is described to automatically estimate the visible body region of a computed tomography (CT) volume image. In order to quantify the body region, a body coordinate (BC) axis is used that runs in longitudinal direction. Its origin and unit length are patient-specific and depend on anatomical landmarks. The body region of a test volume is estimated by registering it only along the longitudinal axis to a set of reference CT volume images with known body coordinates. During these 1D registrations, an axial image slice of the test volume is compared to an axial slice of a reference volume by extracting a descriptor from both slices and measuring the similarity of the descriptors. A slice descriptor consists of histograms of visual words. Visual words are code words of a quantized feature space and can be thought of as classes of image patches with similar appearance. A slice descriptor is formed by sampling a slice on a regular 2D grid and extracting a Speeded Up Robust Features (SURF) descriptor at each sample point. The codebook, or visual vocabulary, is generated in a training step by clustering SURF descriptors. Each SURF descriptor extracted from a slice is classified into the closest visual word (or cluster center) and counted in a histogram. A slice is finally described by a spatial pyramid of such histograms. We introduce an extension of the SURF descriptors to an arbitrary number of dimensions (N-SURF). Here, we make use of 2-SURF and 3-SURF descriptors. Cross-validation on 84 datasets shows the robustness of the results. The body portion can be estimated with an average error of 15.5. mm within 9. s. Possible applications of this method are automatic labeling of medical image databases and initialization of subsequent image analysis algorithms. © 2010 Elsevier Ltd.

Comparing performance of many-core CPUs and GPUs for static and motion compensated reconstruction of C-arm CT data - Hofmann, Hannes; Keck, Benjamin; Rohkohl, Christopher; Hornegger, Joachim (Medical Physics, 2011)
Purpose: Interventional reconstruction of 3-D volumetric data from C-arm CT projections is a computationally demanding task. Hardware optimization is not an option but mandatory for interventional image processing and, in particular, for image reconstruction due to the high demands on performance. Several groups have published fast analytical 3-D reconstruction on highly parallel hardware such as GPUs to mitigate this issue. The authors show that the performance of modern CPU-based systems is in the same order as current GPUs for static 3-D reconstruction and outperforms them for a recent motion compensated (3-D+time) image reconstruction algorithm. Methods: This work investigates two algorithms: Static 3-D reconstruction as well as a recent motion compensated algorithm. The evaluation was performed using a standardized reconstruction benchmark, RABBITCT, to get comparable results and two additional clinical data sets. Results: The authors demonstrate for a parametric B-spline motion estimation scheme that the derivative computation, which requires many write operations to memory, performs poorly on the GPU and can highly benefit from modern CPU architectures with large caches. Moreover, on a 32-core Intel® Xeon® server system, the authors achieve linear scaling with the number of cores used and reconstruction times almost in the same range as current GPUs. Conclusions: Algorithmic innovations in the field of motion compensated image reconstruction may lead to a shift back to CPUs in the future. For analytical 3-D reconstruction, the authors show that the gap between GPUs and CPUs became smaller. It can be performed in less than 20 s (on-the-fly) using a 32-core server. © 2011 American Association of Physicists in Medicine.

Comparison and classification of 3D objects surface point clouds on the example of feet - Grimmer, Rainer; Eskofier, Björn; Schlarb, Heiko; Hornegger, Joachim (Machine Vision and Applications, 2011)
One of the main tasks of shoe manufacturing is the production of well fitting shoes for different specialized markets. The key to conduct this properly is the analysis of the factors that influence the variations of the foot shape. In this paper methods and results of clustering and analysis of 3D foot surfaces are presented. The data were collected from a study with more than 12,000 feet that have been laser-scanned. The database contains point clouds acquired from persons coming from different regions of the world. Furthermore, additional personal data were collected. Two different methods for quantifying the similarity of 3D surface point clouds are therefore developed. The first method generally works on nearly arbitrary 3D surface point clouds, while the second one is specialized on foot data sets. These similarity measures were used on the data sets of the foot-shape study, together with clustering and feature quality evaluation methods. The purpose was to obtain information about the impact of, and the relationship among, the different factors influencing the shape of a foot. Through the observations of the experiments presented here it was possible to build up a hierarchy of different levels of feature-groups determined by their impact on the foot shape. Furthermore, an investigation of the quality and amount of impact of the features, according to their ability to separate specific subgroups of persons, is shown. Based on these results it was possible to select those features, which result in the largest effect when designing shoes for e.g. the Asian versus European markets. © 2009 Springer-Verlag.

Comparison and Combination of Confidence Measures - Stemmer, Georg; Steidl, Stefan; Nöth, Elmar; Niemann, Heinrich; Batliner, Anton (Text, Speech and Dialogue, Proceedings of the Fifth International Conference on Text, Speech, Dialogue - TSD 2002, 2002)

Comparison and Evaluation of Methods for Liver Segmentation from CT datasets - Heimann, Tobias; Van Ginneken, Bram; Styner, Martin A.; Arzhaeva, Yulia; Aurich, Volker; Bauer, Christian; Beck, Andreas; Becker, Christoph; Beichel, Reinhard; Bekes, Gyoergy; Bello, Fernando; Binnig, Gerd; et al. (IEEE Transactions on Medical Imaging, 2009)

Comparison of a 2D- and 3D-Based Graphical User Interface for Localization Listening Tests - Schöffler, Michael; Westphal, Susanne; Adami, Alexander; Bayerlein, Harald; Herre, Jürgen (Proceedings, EAA Joint Symposium on Auralization and Ambisonics 2014, 2014)
herre_papers_subjective_audio_quality

Comparison of Algorithms solving School and Course Time Tabling Problems using the Erlangen Advanced Time Tabling System (EATTS) - Wilke, Peter; Killer, Helmut (Proc. PATAT 2010, 2010)

Comparison of A Priori Calibration Models for Respiratory Inductance Plethysmography During Running - Leutheuser, Heike; Heyde, Christian; Gollhofer, Albert; Eskofier, Björn (IEEE EMBC 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.compar_3

Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset - Galaz, Zoltan; Drotar, Peter; Mekyska, Jiri; Gazda, Matej; Mucha, Jan; Zvoncak, Vojtech; Smekal, Zdenek; Faundez-Zanuy, Marcos; Castrillon, Reinel; Orozco Arroyave, Juan Rafael; Rapcsak, Steven; Kincses, Tamas; et al. (Frontiers in Neuroinformatics, 2022)
Parkinson's disease dysgraphia (PDYS), one of the earliest signs of Parkinson's disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. Both approaches are evaluated on a multilingual dataset collected from 143 PD patients and 151 healthy controls in the Czech Republic, United States, Colombia, and Hungary. The subjects performed the spiral drawing task (SDT; a language-independent task) and the sentence writing task (SWT; a language-dependent task). Models based on logistic regression and gradient boosting were trained in several scenarios, specifically single language (SL), leave one language out (LOLO), and all languages combined (ALC). We found that the HF slightly outperformed the CNN-extracted features in all considered evaluation scenarios for the SWT. In detail, the following balanced accuracy (BACC) scores were achieved: SL-0.65 (HF), 0.58 (CNN); LOLO-0.65 (HF), 0.57 (CNN); and ALC-0.69 (HF), 0.66 (CNN). However, in the case of the SDT, features extracted by a CNN provided competitive results: SL-0.66 (HF), 0.62 (CNN); LOLO-0.56 (HF), 0.54 (CNN); and ALC-0.60 (HF), 0.60 (CNN). In summary, regarding the SWT, the HF outperformed the CNN-extracted features over 6% (mean BACC of 0.66 for HF, and 0.60 for CNN). In the case of the SDT, both feature sets provided almost identical classification performance (mean BACC of 0.60 for HF, and 0.58 for CNN).
CRIS-Team WoS Importer:2022-07-01

Comparison of Differences between Intra- and Inter-Ictal SPECT Images with MRI using Registration Techniques - Hahn, Dieter; Daum, Volker; Hornegger, Joachim; Kuwert, Torsten (Nuklearmedizin Kongressausgabe 02/07, 2007)

Comparison of Different Approaches for Measuring Tibial Cartilage Thickness - Maier, Jennifer; Black, Marianne; Bonaretti, Serena; Bier, Bastian; Eskofier, Björn; Choi, Jang-Hwan; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Journal of integrative bioinformatics, 2017)
Osteoarthritis is a degenerative disease affecting bones and cartilage especially in the human knee. In this context, cartilage thickness is an indicator for knee cartilage health. Thickness measurements are performed on medical images acquired in-vivo. Currently, there is no standard method agreed upon that defines a distance measure in articular cartilage. In this work, we present a comparison of different methods commonly used in literature. These methods are based on nearest neighbors, surface normal vectors, local thickness and potential field lines. All approaches were applied to manual segmentations of tibia and lateral and medial tibial cartilage performed by experienced raters. The underlying data were contrast agent-enhanced cone-beam C-arm CT reconstructions of one healthy subject’s knee. The subject was scanned three times, once in supine position and two times in a standing weight-bearing position. A comparison of the resulting thickness maps shows similar distributions and high correlation coefficients between the approaches above 0.90. The nearest neighbor method results on average in the lowest cartilage thickness values, while the local thickness approach assigns the highest values. We showed that the different methods agree in their thickness distribution. The results will be used for a future evaluation of cartilage change under weight-bearing conditions.
Keywords: Cartilage strain; Cone-beam C-arm CT; Weight-bearing; Local thickness; Potential field lines

Comparison of High-Speed Ray Casting on GPU using CUDA and OpenGL - Weinlich, Andreas; Keck, Benjamin; Scherl, Holger; Kowarschik, Markus; Hornegger, Joachim (Proceedings of the First International Workshop on New Frontiers in High-performance and Hardware-aware Computing (HipHaC'08), 2008)

Comparison of Image-Based Retrospective and Optical Prospective Motion Correction for Diffusion Tensor Imaging - Aksoy, Murat; Forman, Christoph; Straka, Matus; Holdsworth, Samantha; Skare, Stefan; Hornegger, Joachim; Bammer, Roland (Proceedings of the ISMRM Workshop on Current Concepts of Motion Correction for MRI & MRS, 2010)

Comparison of methods for sensitivity correction in Talbot–Lau computed tomography - Felsner, Lina; Roser, Philipp; Maier, Andreas; Riess, Christian (International Journal of Computer Assisted Radiology and Surgery, 2021)
Purpose: In Talbot–Lau X-ray phase contrast imaging, the measured phase value depends on the position of the object in the measurement setup. When imaging large objects, this may lead to inhomogeneous phase contributions within the object. These inhomogeneities introduce artifacts in tomographic reconstructions of the object. Methods: In this work, we compare recently proposed approaches to correct such reconstruction artifacts. We compare an iterative reconstruction algorithm, a known operator network and a U-net. The methods are qualitatively and quantitatively compared on the Shepp–Logan phantom and on the anatomy of a human abdomen. We also perform a dedicated experiment on the noise behavior of the methods. Results: All methods were able to reduce the specific artifacts in the reconstructions for the simulated and virtual real anatomy data. The results show method-specific residual errors that are indicative for the inherently different correction approaches. While all methods were able to correct the artifacts, we report a different noise behavior. Conclusion: The iterative reconstruction performs very well, but at the cost of a high runtime. The known operator network shows consistently a very competitive performance. The U-net performs slightly worse, but has the benefit that it is a general-purpose network that does not require special application knowledge.
CRIS-Team Scopus Importer:2021-09-17
Keywords: computed tomography; known operator learning; sensitivity correction; Talbot–Lau interferometer; X-ray phase contrast imaging

Comparison of Named Entity Recognition Methods on Real-World and Highly Imbalanced Business Document Datasets - Moreno-Acevedo, S. A.; Escobar-Grisales, D.; Vasquez-Correa, J. C.; Orozco Arroyave, Juan Rafael (APPLIED COMPUTER SCIENCES IN ENGINEERING, WEA 2022, 2022)
CRIS-Team WoS Importer:2023-02-24

Comparison of performance between rigid and non-rigid software registering CT to FDG-PET - Wolz, Gabriele; Nömayr, Anton; Hothorn, Torsten; Hornegger, Joachim; Römer, Wolfgang; Bautz, Werner; Kuwert, Torsten (International Journal of Computer Assisted Radiology and Surgery, 2007)
Object: This retrospective study compares the anatomical accuracy of automated rigid and non-rigid registration software for aligning data from separately performed X-ray computed tomography (CT) and positron emission tomography with F-18-deoxyglucose (PET). Materials and methods: Analyses were performed on independently acquired PET and CT data from 40 tumor patients. Rigid as well as non-rigid automated fusion was carried out using the commercially available Mirada 7D platform (MIR and MINR, respectively) as well as a second automated non-rigid registration based on a variational image registration approach (VIR). Distances between lesion representation on PET and CT of 105 malignant lesions were measured in X-, Y-, and Z-directions. Statistical evaluation was performed using mixed effect analysis, comparing separately MIR with MINR and VIR with MINR. Results: The percentage of lesions misregistered by less than 15 mm varied from 70% for MIR and MINR in Z-direction to 93% for VIR in X-direction. The average X-, Y- and Z-distances ranged between 5.9 ± 5.7 mm for VIR in X-direction and 12.8 ± 9.7 mm for MIR in Z-direction. MINR was significantly more accurate than MIR in Y-direction. Furthermore, VIR aligned thoracic lesions in the X- direction significantly better than MINR. Conclusion: The accuracy of rigid and non-rigid automated image registration can be expected to be better than 15 mm for the majority of lesions. Alignment tended to be more accurate with non-rigid registration. © CARS 2007.

Comparison of PGAS Languages on a Linked Cell Algorithm - Bauer, Martin; Kuschel, Christian; Ritter, Daniel; Sembritzki, Klaus (PARS Mitteilungen, 2013)
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.lsinfs.compar_0

Comparison of process characteristics and resulting microstructures of maraging steel 1.2709 in Additive Manufacturing via Laser Metal Deposition and Laser Beam Melting in Powder Bed - Karg, Michael; Hentschel, Oliver; Ahuja, Bhrigu; Junker, Daniel; Haßler, Ulf; Schäperkötter, Claus; Haimerl, Andreas; Arnet, Horst; Merklein, Marion; Schmidt, Michael (6th International Conference on Additive Technologies, 2016)
Laser Metal Deposition (LMD) with Blown Powder and Laser Beam Melting (LBM) in Powder Bed are two Additive Manufacturing technologies that can produce high-quality metal parts in principle. Both technologies are based on melting of fine metal powders employing highintensity laser radiation. Apart from the obvious differences defined by system technology, LMD and LBM differ largely in fundamental boundary conditions of laser-materialinteraction. Process conditions in LMD and LBM and corresponding microstructures were compared on the example of martensitic aging steel 1.2709. This material has been one of the most popular ones in industrial use with LBM for about a decade, typically processed with laser powers below 400 W in continuous wave (cw) mode. With LMD, 1.2709 has not been commonly used so far. LMD typically engages higher cw laser powers of around 1000 W, larger laser spot diameters and smaller feed rates than LBM. Increased laser power kindles hopes for increased productivity, which is a key factor for economic viability. For a more comprehensive comparison, 1.2709 processed by LBM with 1000 W was also considered. Samples were analyzed metallographically, by computed x-ray tomography, hardness testing before and after heat treatment. Chemical compositions of powder and built samples were compared.
Keywords: Maraging Steel; 1.2709; microstructure; Laser Beam Melting; Laser Metal Deposition

Comparison of Real-Time Classification Systems for Arrhythmia Detection on Android-based Mobile Devices - Leutheuser, Heike; Gradl, Stefan; Kugler, Patrick; Anneken, Lars; Achenbach, Stephan; Eskofier, Björn; Arnold, Martin (IEEE EMBC 2014, 2014)

Comparison of Rigid Gradient-Based 2D/3D Registration Using Projection and Back-Projection Strategies - Schaffert, Roman; Wang, Jian; Borsdorf, Anja; Hornegger, Joachim; Maier, Andreas (Bildverarbeitung für die Medizin, 2016)
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.compar_0

Comparison of SART and eTV Reconstruction for increased C-arm CT Volume Coverage by proper Detector Rotation in Liver Imaging - Stromer, Daniel; Amrehn, Mario; Huang, Yixing; Kugler, Patrick; Bauer, Sebastian; Lauritsch, Günter; Maier, Andreas (Proceedings of the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
In this work, we present a method to increase the lateral field-of-view of a C-arm CT system by rotating the detector such that the diagonal of the detector lies on the u-axis of the detector's coordinate system. We investigated three different 3-D scan trajectories for liver imaging of an obese patient (waist circumference 130 cm) — a Short Scan, a Large Volume Scan and a Helical Large Volume Scan. We reconstructed a data set of the Visible Human Project with the SART and the eTV algorithm. Tests revealed that the coverage was increased with the presented method by 25.3 % for the Short Scan and 28.5 % for the Large Volume Scan. Performing helical scans compensated the axial data loss. The two implemented iterative approaches both provide acceptable results, with the eTV algorithm reducing the RMSE compared to SART by about 29 %. Given a liver imaging task, the rotated detector is able to image the entire liver section of the abdomen with a single Large Volume Sca
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.compar_1
Keywords: Diamond Scan; eTV; Field-of-View Enlargement; Iterative Reconstruction; iTV; SART

Comparison of the AMICA and the InfoMax Algorithm for the Reduction of Electromyogenic Artifacts in EEG Data - Leutheuser, Heike; Gabsteiger, Florian; Hebenstreit, Felix; Reis, Pedro; Lochmann, Matthias; Eskofier, Björn (IEEE EMBC 2013, 2013)

Comparison of User Models Based on GMM-UBM and I-Vectors for Speech, Handwriting, and Gait Assessment of Parkinson’s Disease Patients - Vasquez Correa, Juan; Bocklet, Tobias; Orozco Arroyave, Juan Rafael; Nöth, Elmar (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020)
Parkinson’s disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Information from speech, handwriting, and gait signals have been considered to evaluate the neurological state of the patients. On the other hand, user models based on Gaussian mixture models - universal background models (GMMUBM) and i-vectors are considered the state-of-the-art in biometric applications like speaker verification because they are able to model specific speaker traits. This study introduces the use of GMM-UBM and i-vectors to evaluate the neurological state of Parkinson’s patients using information from speech, handwriting, and gait. The results show the importance of different feature sets from each type of signal in the assessment of the neurological state of the patients.<br
Keywords: Parkinson’s disease; GMM-UBM; ivectors; gait analysis; handwriting analysis; speech analysis

Compensation of extrinsic variability in speaker verification systems on simulated skype and hf channel data - Riedhammer, Korbinian Thomas; Bocklet, Tobias; Nöth, Elmar (Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2011)

Complete Valvular Heart Apparatus Model from 4D Cardiac CT - Vitanovski, Dime; Voigt, Ingmar; et al. (Proceedings of 13th International Conference on Medical Image Computing and Computer Assisted Intervention, 2010)

Comprehensive Mapping of Ostia Locations Requires Multiple Sectioning Orientations - Kagemann, Larry; Wollstein, Gadi; Kostanyan, Tigran; Lucy, Katie A.; Ishikawa, Hiroshi; Fujimoto, James G.; Lu, Chen D.; Hornegger, Joachim; Sigal, Ian A.; Schuman, Joel S. (Investigative Ophthalmology & Visual Science, 2015)

Computational decision support for percutaneous aortic valve implantation - Voigt, Ingmar; Ionasec, Razvan; Georgescu, Bogdan; Boese, Jan; Brockmann, Gernot; Hornegger, Joachim; Comaniciu, Dorin (Springer-verlag, 2010)
Valve replacement is the most common therapy for diseased aortic valves. Percutaneous approaches are becoming increasingly popular, due to reduced procedural complications and lower follow-up rates. Still there is a lack of efficient tools for valve quantification and preoperative simulation of replacement and repair procedures. Thus the success of the intervention relies to a large portion on experience and skills of the operator. In this paper we propose a novel framework for preoperative planning, intraoperative guidance and post-operative assessment of percutaneous aortic valve replacement procedures with stent mounted devices. A comprehensive model of the aortic valvular complex including aortic valve and aorta ascendens is estimated with fast and robust learning-based techniques from cardiac CT images. Consequently our model is used to perform a in-silico delivery of the valve implant based on deformable simplex meshes and geometrical constraints. The predictive power of the model-based in-silico valve replacement was validated on 3D cardiac CT data from 20 patients through comparison of preoperative prediction against postoperatively imaged real device. In our experiments the method performed with an average accuracy of 2.18 mm and a speed of 55 seconds. To the best of our knowledge, this is the first time a computational framework is validated using real pre- and postoperative patient data. © 2010 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD5.comput

Computed Tomography - Taubmann, Oliver; Berger, Martin; Bögel, Marco; Xia, Yan; Balda, Michael; Maier, Andreas (Medical Imaging Systems, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.comput

Computer-aided assessment of anomalies in the scoliotic spine in 3-D MRI images - Jäger, Florian; Hornegger, Joachim; Schwab, Siegfried; Janka, Rolf Matthias (Springer-verlag, 2009)
The assessment of anomalies in the scoliotic spine using Magnetic Resonance Imaging (MRI) is an essential task during the planning phase of a patient's treatment and operations. Due to the pathologic bending of the spine, this is an extremely time consuming process as an orthogonal view onto every vertebra is required. In this article we present a system for computer-aided assessment (CAA) of anomalies in 3-D MRI images of the spine relying on curved planar reformations (CPR). We introduce all necessary steps, from the pre-processing of the data to the visualization component. As the core part of the framework is based on a segmentation of the spinal cord we focus on this. The proposed segmentation method is an iterative process. In every iteration the segmentation is updated by an energy based scheme derived from Markov random field (MRF) theory. We evaluate the segmentation results on public available clinical relevant 3-D MRI data sets of scoliosis patients. In order to assess the quality of the segmentation we use the angle between automatically computed planes through the vertebra and planes estimated by medical experts. This results in a mean angle difference of less than six degrees. © 2009 Springer-Verlag.

Computer-Aided Assessment of Anomalies in the Scoliotic Spine in 3-D MRI Images - Jäger, Florian; Hornegger, Joachim; Schwab, Siegfried; Janka, Rolf Matthias (Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009, 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part II, 2009)
The assessment of anomalies in the scoliotic spine using Magnetic Resonance Imaging (MRI) is an essential task during the planning phase of a patient's treatment and operations. Due to the pathologic bending of the spine, this is an extremely time consuming process as an orthogonal view onto every vertebra is required. In this article we present a system for computer-aided assessment (CAA) of anomalies in 3-D MRI images of the spine relying on curved planar reformations (CPR). We introduce all necessary steps, from the pre-processing of the data to the visualization component. As the core part of the framework is based on a segmentation of the spinal cord we focus on this. The proposed segmentation method is an iterative process. In every iteration the segmentation is updated by an energy based scheme derived from Markov random field (MRF) theory. We evaluate the segmentation results on public available clinical relevant 3-D MRI data sets of scoliosis patients. In order to assess the quality of the segmentation we use the angle between automatically computed planes through the vertebra and planes estimated by medical experts. This results in a mean angle difference of less than six degrees.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.comput_4

Computer-Aided Detection of Lesions in Digital Breast Tomosynthesis Images - Prinzen, Martin; Wagner, Florian; Nowack, Sebastian; Schulz-Wendtland, Rüdiger; Paulus, Dietrich; Wittenberg, Thomas (Bildverarbeitung für die Medizin 2014, 2014)
The most common cancer among women in the western world is breast cancer. Early detection of lesions greatly influences the progress and success of its treatment. Digital breast tomosynthesis (DBT) is a new imaging technique that facilitates a three-dimensional reconstruction of the breast. DBT reduces superimposition of breast tissues and provides better insight into the breast compared to the common digital mammography. In order to assist radiologists with the examination and assessment of the large amount of DBT data, a computer aided detection (CADe) of focal lesions can be an essential tool, leading to increased sensitivity and specificity. We present and compare two different approaches for a fully automated detection of lesions in DBT data using voxel-wise classification, one being the state of the art and the other one an enhancement. Multiple difference of Gaussians detect lesions based on their common higher intensity and contrast in relation to surrounding tissue. A gradient orientation analysis detects round and spiculated lesions, even when they are weak in contrast and intensity. By combining these features and using a support vector machine, a classification performance of 88% can be achieved.
UnivIS-Import:2017-12-18:Pub.2014.tech.IMMD.IMMD9.comput
Keywords: Mammography; Lesion Detection

Computer-aided Diagnosis for Magnetically Guided Capsule Endoscopy - Mewes, Philip (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Digestive diseases and disorders are diverse with some being more common and of short duration, whereas others may be life threatening if left untreated. They are among the most common problems that people are afflicted with at least once in their lifetime. As in many other clinical disciplines, diagnosis and treatment of digestive diseases are driven towards minimally invasive approaches. Flexible endoscopy, though today’s gold standard, is still characterized by a considerable level of invasiveness, patient discomfort and the need for anesthesia. In cases with a given diagnostic indication, passive video capsule endoscopy (VCE), developed in the early 2000s, may significantly decrease the invasiveness in performing a differential diagnosis of upper and lower gastrointestinal tract diseases. However, capsule endoscopes have the drawback of not being actively steerable inside the gastrointestinal tract, which limits their diagnostic abilities. Magnetically guided capsule endoscopy (MGCE), which is the imaging modality used in this thesis, has overcome this problem by providing steerability of the capsule endoscope. Although the feasibility of MGCE procedures has been proven in two clinical studies, the quality of the examination depends on the skill of the operator and his ability to detect visual signs of possible pathologies in real time. During a MGCE procedure, the physician needs to focus on two images simultaneously as diagnosis and image acquisition happen mostly at the same time. In comparison, VCE images can be reviewed at a speed suitable for the physician and the reviewing process can be paused whenever necessary. Therefore, a system for assisting the physician in scanning and interpreting images in real time can be beneficial for MGCE. To that end, in this thesis a computer-aided diagnosis (CAD) system, comprised of multiple computational steps, is developed specifically for MGCE images. The first part of this work focuses on the segmentation of bubbles, particles and other debris, which frequently appear in MGCE images, and interfere with the CAD process. The developed approach is capable of segmenting image areas with such content with an average accuracy of 87.8% and is, therefore, providing an image region of interest for further processing. In the second part of this thesis a novel system for semantic and topological classification of MGCE images is presented. Based on a series of processing steps and extracted features, images are grouped into different categories. These categories are, for example, the anatomical location of the acquisition, or the image pose relative to anatomical structures in the upper gastrointestinal tract. An average accuracy of over 80% for all classification stages could be achieved. Such information facilitates the post-procedure review process or provides strong a priori knowledge for CAD algorithms. In the third part of this work a CAD method is developed that can automatically detect two different pathologies in MGCE images. The objective of this technique is to operate during the intervention. It indicates in real time the presence of pathological structures to the physician, who can then decide whether to more closely examine the indicated area. The presented approach yields a 95% average sensitivity. The last part of this work focuses on the detection of pre-cancerous signs. To that end, a staining technique, so-called chromoendoscopy, is applied to MGCE and is evaluated in order to highlight pathologies that are barely visible for the human observer. This work charts a path for the application of chromoendoscopy on MGCE via an ex-vivo animal study, in order to improve the visibility of pre-cancerous changes in cells and tissues. In summary for my thesis, I designed a diverse set of tools for computer-aided diagnosis in MGCE. The developed CAD system offers both real time interventional support, as well as facilitation of off-line processing.<br

Computer-Aided Diagnostics and Pattern Recognition: Automated Glaucoma Detection - Köhler, Thomas; Bock, Rüdiger; Hornegger, Joachim; Michelson, Georg (Teleophthalmology in Preventive Medicine, 2015)
Glaucoma is one of the major causes for blindness with a high rate of unreported cases. To reduce this number, screening programs are performed. However, these are characterized by a high workload for manual and cost-intensive assessment. Computer-aided diagnostics (CAD) to perform an automated pre-exclusion of normals might help to improve program's efficiency. This chapter reviews and discusses recent advances in the development of pattern recognition algorithms for automated glaucoma detection based on structural retinal image data. Two main methodologies for glaucoma detection are introduced: (i) structure-driven approaches that mainly rely on the automated extraction of specific medically relevant indicators and (ii) data-driven techniques that perform a generic machine learning approach on entire image data blobs. Both approaches show a reasonable and comparable performance although they rely on different basic assumptions. A combination of these might further improve CAD for a more efficient and cost-sensitive workflow as a major proportion of normals will be excluded from unnecessary detailed investigations.
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.comput

Computer-aided evaluation of anatomical accuracy of image fusion between X-ray CT and SPECT - Han, Jingfeng; Köstler, Harald; Bennewitz, Christian; Kuwert, Torsten; Hornegger, Joachim (Computerized Medical Imaging and Graphics, 2008)
Hybrid scanners, which enable the performance of single photon emission computed tomography (SPECT) and X-ray computed tomography (CT) in one imaging session, have considerable diagnostic potential. However, evaluating the anatomical accuracy of image fusion inherent to these systems remains a challenge. This paper proposes a method for evaluating this variable with minimum user interaction. It focuses on measuring the distance between the centers of gravity of the SPECT hot spot and its counterpart in the CT image. A localized maximally stable extremal regions method is proposed to automatically segment SPECT hot spots, while the corresponding CT structures are segmented by the semi-automatic random walk method, based on a fast multigrid solver. Accuracy and reproducibility of the validation method have been preliminary confirmed by the test with 21 clinical data-sets. © 2008 Elsevier Ltd. All rights reserved.

Computer-aided evaluation of the anatomical accuracy of hybrid SPECT/spiral-CT imaging of lesions localized in the neck and upper abdomen - Hahn, Dieter; Bennewitz, Christian; Kuwert, Torsten; Han, Jingfeng; Ritt, Philipp; Hahn, Dieter; Thimister, Willie; Hornegger, Joachim; Uder, Michael; Schmidt, Daniela (Nuclear Medicine Communications, 2012)
OBJECTIVE: The purpose of this study was to investigate the anatomical accuracy of hardware-based single-photon emission computed tomography/computed tomography (SPECT/CT) registration in the upper abdomen and neck. METHODS: The database consisted of 90 patients referred for SPECT/CT for diagnostic workup of either thyroid/parathyroid disease (n=46) or abdominal neuroendocrine tumours (n=44). In the first group, Tc-MIBI was used as the tracer and in the second I-metaiodobenzylguanidine (n=13), In-octreotide (n=28) or Tc-octreotide (n=3). For predefined structures represented by both modalities, the distances between the centres of gravity of their CT and SPECT representation were determined in a semiautomated manner. In cervical data sets, this analysis was performed for the submandibular salivary glands (n=92) and in abdominal data sets for 69 neoplastic foci. RESULTS: The mean distances were 5.7±2.0 mm (range: 1.84-9.67 mm) in the neck and 6.8±3.3 mm (range: 1.4-19.7 mm) in the abdomen. In 42 out of 92 of the cervical and 40 out of 69 of the abdominal data sets at least one of the X-direction-determined, Y-direction-determined, and Z-direction-determined distances was greater than the SPECT pixel width of 4.6 mm. CONCLUSION: The anatomical accuracy of hardware-based SPECT/CT fusion depends also on the region of the body studied. For example, in the neck and upper abdomen the accuracy is lower than in the lower lumbar spine. In clinical routine, SPECT/CT data sets acquired for the neck and upper abdomen should be regularly checked and corrected for SPECT/CT misalignment. This is, in particular, important when CT-based corrections of SPECT involving pixelwise data integration such as for attenuation correction are made. © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins.

Computer-Aided Tumor Diagnosis of Microscopy Images - Aubreville, Marc (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020)
For the diagnosis of medical images, computer-aided methods can help to lower time requirements and reduce human error by highlighting regions of particular interest, by aiding inexperienced physicians in their interpretation, or just by providing new visualizations using secondary data. In this work, several new methods to support medical experts in the diagnosis of microscopy images are presented. The first part of the thesis focuses on the processing of images acquired using confocal laser endomicroscopy. This relatively novel imaging modality provides in-vivo, in-situ microscopy at very high magnification and hence allows for microstructural analysis of tissue. A common problem with this imaging modality is the occurrence of image deteriorations. Motion artifacts are amongst the most common of these and are especially challenging to detect algorithmically. This thesis investigates multiple methods for the detection of these impediments within the image. Besides a classical machine learning pipeline, involving features known from literature and novel, specially designed features for motion artifact detection, a custom deep learning architecture that achieved an accuracy of 94% and outperformed traditional approaches is introduced. Confocal laser endomicroscopy can be used, amongst other relevant questions, for the diagnosis of squamous cell carcinoma (a tumor of the epithelium). This malignancy has a high prevalence in the head and neck region and often occurs in the upper respiratory and digestive tract. Automatic detection of these tumors using non-invasive methods could enable usage for screening by medical personnel inexperienced in the modality and thus provide possibilities for earlier detection, which could improve therapeutical outcomes. In this thesis, new, deep learning-based methods are presented and shown to achieve classification accuracies of greater than 90%, which is similar to human experts on the same modality. The second part of this thesis is about the detection of cells undergoing cell division (mitotic figures) in hematoxylin- and eosin-stained bright-field microscopy images. While it is a highly relevant task with many algorithmic competitions held on the topic, it is far from being solved. We were able to show that one crucial factor limiting the development of clinical-grade solutions is the availability of data sets of sufficient quantity and quality. In this thesis, we present new methods to efficiently create datasets for microscopy cell annotations and demonstrate their capabilities by introducing a newly created data set of unprecedented size that the methods allowed us to build. Using detection algorithms trained using the data set, it was, for the first time, possible to perform mitotic figure detection on completely labeled whole slide images of tumor tissue. In the technical validation of the data set, which was built using specimens from canine cutaneous mast cell tumors, an F1-score of 0.82 was found. An essential part of tumor grading is the selection of the mitotically most active area of tissue. Performing the mitotic count in this area is especially meaningful since the corresponding mitotic activity is known to be strongly correlated with tumor proliferation and is thus highly relevant for prognosis. This task was never before assessed algorithmically due to the lack of suitable data sets. Three novel approaches were described and compared for this purpose and shown to outperform human experts significantly. Finally, the work investigates domain-transfer and multi-domain applications of the generated models to a broader range of tissue types and species. When training and cross-domain-evaluating on two further novel whole slide image data sets of canine mammary carcinoma and human meningioma, a substantial domain shift was found. Besides the exploitation of this shift for detection, the work describes and evaluates new algorithmic approaches for unsupervised domain adaptation.<br
Keywords: tumor diagnostics; digital pathology; deep learning

Computergestützte Gefäßanalyse für die interventionelle Anwendung - Prümmer, Marcus; Hornegger, Joachim; Schnörr, Christoph (Bildverarbeitung für die Medizin 2004, 2004)

Computerized Automatic Modeling of Medical Prostheses - Sickel, Konrad (2013)
In this thesis we study artificial intelligence methods, rule-based expert systems in particular, for the task of automatically designing customized medical prostheses. Here, the term design denotes the shaping or modeling of the prostheses and not their functional design. The challenge of the task at hand lies in designing prostheses that fit perfectly to the anatomy of the patient, and in many cases have to support additional functionality. Hence, each prosthesis has to be unique. Therefore, medical prostheses are usually designed starting with a template of the patient’s anatomy, e. g. acquired using CT data or scanned and digitized molds. In this thesis we assume that the template data is given as a triangle mesh in 3-D. To address the challenge of automatically designing medical prostheses, we develop an expert system framework consisting of an expert system shell, a knowledge base and a feature detection unit. The framework is integrated into an existing modeling software. In the following, we denote the complete system as Expert System for Automatic Modeling (ESAM). The architecture of ESAM is generic and can be used for all kinds of design tasks. The specialization for the application in mind can be achieved by providing the necessary design rules and by adjusting the feature detection algorithms. Our expert system specializes in monitoring and controlling a CAD software. Thus, it defines the parameters of the CAD tools, executes the tools and monitors the results by constantly analyzing the current shape. As part of the expert system we develop a knowledge representation language to structure and store the expert knowledge. The language is easy to understand, flexible and can be extended as required. The knowledge base is created in interaction with experts of the field. In addition, we study methods to extend, improve and maintain the knowledge base. We evaluate two methods for rule creation and rule critic. On the one hand, we apply genetic programming as a rule learning technique. On the other hand, we use a heuristic method based on data generated by ESAM. For the latter, we develop a tool that generates statistics about rule performance, rule relationships and user interaction. The thereby gained knowledge is integrated into the knowledge base, resulting in a higher performance of ESAM, e. g. the completion rate increased by about 30 %. We apply two types of feature detection methods for the detection of surface features on the given templates. The first method analyzes the surface of the given template for peaks, depressions, ridges and combinations of these generic features. The generality of the detected features allows a simple adjustment to different anatomies. The second method uses registration in order to copy features from a labeled template to an unlabeled one. As a first step, it applies clustering techniques to identify a suitable set of representative templates. In the second step, these templates are labeled by a domain expert. Subsequently, the labels can be transferred based on the result of an ICP registration. Our experiments show that the second approach results in a higher quality of the detected features, e. g. mean deviation is reduced from approximately 3.8mm by about 30% to approximately 2.6 mm. ESAM is verified using the example of customized in-the-ear hearing aid design. An industry partner provides the domain knowledge necessary to create the knowledge base as well as the possibility to verify the system in a real production environment. In order to ensure the quality of the designed and manufactured in-the-ear hearing aids, the system is verified while running in a semi-automatic mode. The semi-automatic mode allows a modeling expert to monitor and correct the system if necessary. During the verification and practical usage of ESAM thousands of customized in-the-ear hearing aid shells are manufactured. It could be shown that compared to the manual approach the design consistency improves by about 10% and the design time is reduced by about 30 %. The overall acceptance rate of an expert system rule is 76 %. In addition to that, ESAM provides a framework, which guides the modeler through the complex design process, and thereby reduces the amount of design errors and avoids unnecessary process steps. As a consequence of these positive evaluation results our industry partner continues to apply ESAM on its production floor.<br
Keywords: Clustering; Iterative Closest Point; Merkmalserkennung; Regelbasierte Expertensysteme Clustering; Feature Detection; Genetic Programming; Iterative Closest Point Algorithm; Rule-based Expert Systems

Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent - Bertram, Christof A.; Aubreville, Marc; Gurtner, Corinne; Bartel, Alexander; Corner, Sarah M.; Dettwiler, Martina; Kershaw, Olivia; Noland, Erica L.; Schmidt, Anja; Sledge, Dodd D.; Smedley, Rebecca C.; Thaiwong, Tuddow; et al. (Veterinary Pathology, 2020)
Mitotic count (MC) is an important element for grading canine cutaneous mast cell tumors (ccMCTs) and is determined in 10 consecutive high-power fields with the highest mitotic activity. However, there is variability in area selection between pathologists. In this study, the MC distribution and the effect of area selection on the MC were analyzed in ccMCTs. Two pathologists independently annotated all mitotic figures in whole-slide images of 28 ccMCTs (ground truth). Automated image analysis was used to examine the ground truth distribution of the MC throughout the tumor section area, which was compared with the manual MCs of 11 pathologists. Computerized analysis demonstrated high variability of the MC within different tumor areas. There were 6 MCTs with consistently low MCs (MC<7 in all tumor areas), 13 cases with mostly high MCs (MC ≥7 in ≥75% of 10 high-power field areas), and 9 borderline cases with variable MCs around 7, which is a cutoff value for ccMCT grading. There was inconsistency among pathologists in identifying the areas with the highest density of mitotic figures throughout the 3 ccMCT groups; only 51.9% of the counts were consistent with the highest 25% of the ground truth MC distribution. Regardless, there was substantial agreement between pathologists in detecting tumors with MC ≥7. Falsely low MCs below 7 mainly occurred in 4 of 9 borderline cases that had very few ground truth areas with MC ≥7. The findings of this study highlight the need to further standardize how to select the region of the tumor in which to determine the MC.<br
Keywords: area selection; high-power field; mitotic activity; mitotic figure distribution; tumor grading; tumor periphery

Cone-beam Reconstruction on a Circlar Short-scan using the Factorization Approach - Dennerlein, Frank; Noo, Frédéric; Schöndube, Harald; Hornegger, Joachim; Lauritsch, Günter (Proceedings Fully 3D Meeting and HPIR Workshop, 2007)

Cone-beam tomography from short-scan cirele-plus-are data measured on a C-arm system - Hoppe, Stefan; Dennerlein, Frank; Lauritsch, Günter; Hornegger, Joachim; Noo, Frédéric (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 C-arm computed tomography (CT) systems, the source trajectory does not follow an ideal trajectory. Thus, the real data acquisition geometry is typically expressed by a sequence of projection matrices. However, exact reconstruction algorithms are based on an analytic expression of the projection geometry. In this work, we present a reformulation of an exact reconstruction method to handle projection matrices. In particular, the M-line approach is investigated for a short-scan circle-plus-arc data acquisition. The computation of the derivative with respect to the source trajectory is numerically most critical for which a novel and stable implementation is developed. In order to determine the backprojection range, a 2D polygonal weighting scheme is proposed. Image results are presented from phantom data acquired by a Siemens AXIOM Artis C-arm system. Excellent image results are achieved. Due to the complete data acquisition, the problem of cone artifacts is totally resolved. © 2006 IEEE.