TY - CHAP A1 - Seim, Heiko A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - A System for Unsupervised Extraction of Orthopaedic Parameters from CT Data T2 - GI Workshop Softwareassistenten - Computerunterstützung für die medizinische Diagnose und Therapieplanung Y1 - 2009 SP - 1328 EP - 1337 CY - Lübeck, Germany ER - TY - CHAP A1 - Lamecker, Hans A1 - Mansi, Tomaso A1 - Relan, Jatin A1 - Billet, Florence A1 - Sermesant, Maxime A1 - Ayache, Nicholas A1 - Delingette, Hervé T1 - Adaptive Tetrahedral Meshing for Personalized Cardiac Simulations T2 - Proc. MICCAI Workshop on Cardiovascular Interventional Imaging and Biophysical Modelling (CI2BM) Y1 - 2009 SP - 149 EP - 158 ER - TY - CHAP A1 - Zachow, Stefan A1 - Zilske, Michael A1 - Hege, Hans-Christian T1 - 3D Reconstruction of Individual Anatomy from Medical Image Data: Segmentation and Geometry Processing T2 - 25. ANSYS Conference & CADFEM Users’ Meeting Y1 - 2007 CY - Dresden ER - TY - CHAP A1 - Neubert, Kerstin A1 - Lamecker, Hans A1 - Hege, Hans-Christian A1 - Menzel, Randolf A1 - Rybak, Jürgen T1 - Model-based autosegmentation of brain structures in the honeybee using statistical shape models T2 - Proc. 8th Int. Congr. of Neuroethology (ICN) Y1 - 2007 ER - TY - CHAP A1 - Lange, Thomas A1 - Wenckebach, Thomas A1 - Lamecke, Hans A1 - Seebaß, Martin A1 - Hünerbein, Michael A1 - Eulenstein, Sebastian A1 - Schlag, Peter-Michael ED - Lemke, Heinz T1 - Registration of portal and hepatic venous phase of MR/CT data for computer-assisted liver surgery planning T2 - Proc. Computer Assisted Radiology and Surgery (CARS) Y1 - 2005 U6 - https://doi.org/10.1016/j.ics.2005.03.332 SP - 768 EP - 772 PB - Elsevier ER - TY - CHAP A1 - Lange, Thomas A1 - Wenckebach, Thomas A1 - Lamecker, Hans A1 - Seebaß, Martin A1 - Hünerbein, Michael A1 - Eulenstein, Sebastian A1 - Schlag, Peter-Michael T1 - Registration of different phases of contrast-enhanced CT/MRI data for computer-assisted liver surgery planning T2 - Int. J. Medical Robotics and Computer Assisted Surgery Y1 - 2005 U6 - https://doi.org/10.1002/rcs.23 VL - 1 (3) SP - 6 EP - 20 PB - Wiley ER - TY - CHAP A1 - Prohaska, Steffen A1 - Hutanu, Andrei A1 - Kähler, Ralf A1 - Hege, Hans-Christian T1 - Interactive exploration of large remote micro-CT scans T2 - Proc. IEEE Visualization 2004 Y1 - 2004 U6 - https://doi.org/10.1109/VIS.2004.51 SP - 345 EP - 352 CY - Austin, Texas ER - TY - CHAP A1 - Dornheim, Lars A1 - Dornheim, Jana A1 - Seim, Heiko A1 - Tönnies, Klaus T1 - Aktive Sensoren: Kontextbasierte Filterung von Merkmalen zur modellbasierten Segmentierung T2 - Bildverarbeitung für die Medizin Y1 - 2006 U6 - https://doi.org/10.1007/3-540-32137-3_20 SP - 96 EP - 100 PB - Springer-Verlag ER - TY - CHAP A1 - Seim, Heiko A1 - Dornheim, Jana A1 - Preim, Uta T1 - Ein 2-Fronten-Feder-Masse-Modell zur Segmentierung von Lymphknoten in CT-Daten des Halses T2 - Bildverarbeitung für die Medizin Y1 - 2006 U6 - https://doi.org/10.1007/3-540-32137-3_22 SP - 106 EP - 110 PB - Springer-Verlag ER - TY - CHAP A1 - Zachow, Stefan A1 - Hege, Hans-Christian A1 - Deuflhard, Peter T1 - Computergestützte Operationsplanung in der Gesichtschirurgie T2 - Proc. VDE Kongress 2004 - Innovationen für Menschen, Band 2, Fachtagungsberichte DGBMT - GMM - GMA Y1 - 2004 SP - 53 EP - 58 ER - TY - CHAP A1 - Zachow, Stefan A1 - Hierl, Thomas A1 - Erdmann, Bodo T1 - A quantitative evaluation of 3D soft tissue prediction in maxillofacial surgery planning T2 - Proc. 3. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter-assistierte Chirurgie e.V. Y1 - 2004 CY - München ER - TY - CHAP A1 - Zachow, Stefan A1 - Hierl, Thomas A1 - Erdmann, Bodo T1 - On the Predictability of tissue changes after osteotomy planning in maxillofacial surgery T2 - Computer Assisted Radiology and Surgery (CARS) Y1 - 2004 U6 - https://doi.org/10.1016/j.ics.2004.03.043 SP - 648 EP - 653 CY - Chicago, USA ER - TY - CHAP A1 - Zachow, Stefan A1 - Hierl, Thomas A1 - Erdmann, Bodo T1 - Über die Genauigkeit einer 3D Weichgewebeprädiktion in der MKG-Cirurgie T2 - Workshop ’Bildverarbeitung für die Medizin’ (BVM) Y1 - 2004 SP - 75 EP - 79 CY - Berlin, Germany ER - TY - CHAP A1 - Lange, Thomas A1 - Wenckebach, Thomas A1 - Seebaß, Martin A1 - Lamecker, Hans A1 - Hünerbein, Michael T1 - Registration of different phases of contrast-enhanced MR data for liver surgery T2 - 3. Jahrestagung der Deutschen Gesellschaft für Computer-Roboterassistierte Chirurgie CURAC Y1 - 2004 ER - TY - CHAP A1 - Lange, Thomas A1 - Eulenstein, Sebastian A1 - Hünerbein, Michael A1 - Lamecker, Hans A1 - Schlag, Peter-Michael ED - Barrilot, Christian T1 - Augmenting intraoperative 3D ultrasound with preoperative models for navigation in liver surgery T2 - Proceedings MICCAI Y1 - 2004 SP - 543 EP - 541 PB - Springer ER - TY - CHAP A1 - Wenckebach, Thomas A1 - Lamecker, Hans A1 - Hege, Hans-Christian ED - Christensen, Gary ED - Sonka, Milan T1 - Capturing anatomical shape variability using B-spline registration T2 - Information Processing in Medical Imaging: 19th International Conference (IPMI 2005) Y1 - 2005 U6 - https://doi.org/10.1007/b137723 VL - 3565/2005 SP - 578 EP - 590 PB - Springer-Verlag GmbH CY - Glenwood Springs, CO, USA ER - TY - CHAP A1 - Lamecker, Hans A1 - Zöckler, Maja A1 - Haberl, Hannes A1 - Zachow, Stefan A1 - Hege, Hans-Christian T1 - Statistical shape modeling for craniosynostosis planning T2 - 2nd International Conference Advanced Digital Technology in Head and Neck Reconstruction 2005, Abstract Volume Y1 - 2005 SP - 64 CY - Banff, Alberta ER - TY - CHAP A1 - Lamecker, Hans A1 - Wenckebach, Thomas A1 - Hege, Hans-Christian T1 - Atlas-based 3D-shape reconstruction from x-ray images T2 - Proc. Int. Conf. of Pattern Recognition (ICPR2006) Y1 - 2006 U6 - https://doi.org/10.1109/ICPR.2006.279 VL - Volume I SP - 371 EP - 374 PB - IEEE Computer Society ER - TY - CHAP A1 - Lamecker, Hans A1 - Wenckebach, Thomas A1 - Hege, Hans-Christian A1 - N. Duda, Georg A1 - Heller, Markus T1 - Atlas-basierte 3D-Rekonstruktion des Beckens aus 2D-Projektionsbildern T2 - Bildverarbeitung für die Medizin 2006: Algorithmen - Systeme - Anwendungen Y1 - 2006 U6 - https://doi.org/10.1007/3-540-32137-3_6 SP - 26 EP - 30 PB - Springer Berlin Heidelberg ER - TY - CHAP A1 - Zachow, Stefan A1 - Lamecker, Hans A1 - Elsholtz, Barbara A1 - Stiller, Michael T1 - Reconstruction of mandibular dysplasia using a statistical 3D shape model T2 - Proc. Computer Assisted Radiology and Surgery (CARS) Y1 - 2005 U6 - https://doi.org/10.1016/j.ics.2005.03.339 SP - 1238 EP - 1243 CY - Berlin, Germany ER - TY - CHAP A1 - Nkenke, Emeka A1 - Häusler, Gerd A1 - Neukam, Friedrich A1 - Zachow, Stefan T1 - Streak artifact correction of CT data by optical 3D imaging in the simulation of orthognathic surgery T2 - Computer Assisted Radiology and Surgery (CARS) Y1 - 2005 U6 - https://doi.org/doi:10.1016/j.ics.2005.03.278 CY - Berlin Germany ER - TY - CHAP A1 - Nkenke, Emeka A1 - Zachow, Stefan A1 - Häusler, Gerd T1 - Fusion von optischen 3D- und CT-Daten des Gebisses zur Metallartefaktkorrektur vor computerassistierter Planung MKG-chirurgischer Eingriffe T2 - Symposium der Arbeitsgemeinschaf für Kieferchirurgie Y1 - 2005 CY - Bad Homburg v.d.H ER - TY - CHAP A1 - Ehlke, Moritz A1 - Heyland, Mark A1 - Märdian, Sven A1 - Duda, Georg A1 - Zachow, Stefan T1 - 3D Assessment of Osteosynthesis based on 2D Radiographs T2 - Proceedings of the Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC) N2 - We present a novel method to derive the surface distance of an osteosynthesis plate w.r.t. the patient-specific surface of the distal femur based on postoperative 2D radiographs. In a first step, the implant geometry is used as a calibration object to relate the implant and the individual X-ray images spatially in a virtual X-ray setup. Second, the patient- specific femoral shape and pose are reconstructed by fitting a deformable statistical shape and intensity model (SSIM) to the X-rays. The relative positioning between femur and implant is then assessed in terms of the displacement between the reconstructed 3D shape of the femur and the plate. We believe that the approach presented in this paper constitutes a meaningful tool to elucidate the effect of implant positioning on fracture healing and, ultimately, to derive load recommendations after surgery. KW - 3d-reconstruction from 2d X-rays KW - osteosynthesis follow-up KW - statistical shape and intensity models Y1 - 2015 SP - 317 EP - 321 ER - TY - CHAP A1 - Ehlke, Moritz A1 - Heyland, Mark A1 - Märdian, Sven A1 - Duda, Georg A1 - Zachow, Stefan T1 - Assessing the relative positioning of an osteosynthesis plate to the patient-specific femoral shape from plain 2D radiographs T2 - Proceedings of the 15th Annual Meeting of CAOS-International (CAOS) N2 - We present a novel method to derive the surface distance of an osteosynthesis plate w.r.t. the patient­specific surface of the distal femur based on 2D X­ray images. Our goal is to study from clinical data, how the plate­to­bone distance affects bone healing. The patient­specific 3D shape of the femur is, however, seldom recorded for cases of femoral osteosynthesis since this typically requires Computed Tomography (CT), which comes at high cost and radiation dose. Our method instead utilizes two postoperative X­ray images to derive the femoral shape and thus can be applied on radiographs that are taken in clinical routine for follow­up. First, the implant geometry is used as a calibration object to relate the implant and the individual X­ray images spatially in a virtual X­ray setup. In a second step, the patient­specific femoral shape and pose are reconstructed in the virtual setup by fitting a deformable statistical shape and intensity model (SSIM) to the images. The relative positioning between femur and implant is then assessed in terms of displacement between the reconstructed 3D shape of the femur and the plate. A preliminary evaluation based on 4 cadaver datasets shows that the method derives the plate­to­bone distance with a mean absolute error of less than 1mm and a maximum error of 4.7 mm compared to ground truth from CT. We believe that the approach presented in this paper constitutes a meaningful tool to elucidate the effect of implant positioning on fracture healing. KW - 3d-­reconstruction from 2d X­rays KW - statistical shape and intensity models KW - fracture fixation of the distal femur KW - pose estimation Y1 - 2015 ER - TY - CHAP A1 - Ehlke, Moritz A1 - Frenzel, Thomas A1 - Ramm, Heiko A1 - Shandiz, Mohsen Akbari A1 - Anglin, Carolyn A1 - Zachow, Stefan T1 - Towards Robust Measurement Of Pelvic Parameters From AP Radiographs Using Articulated 3D Models T2 - Computer Assisted Radiology and Surgery (CARS) N2 - Patient-specific parameters such as the orientation of the acetabulum or pelvic tilt are useful for custom planning for total hip arthroplasty (THA) and for evaluating the outcome of surgical interventions. The gold standard in obtaining pelvic parameters is from three-dimensional (3D) computed tomography (CT) imaging. However, this adds time and cost, exposes the patient to a substantial radiation dose, and does not allow for imaging under load (e.g. while the patient is standing). If pelvic parameters could be reliably derived from the standard anteroposterior (AP) radiograph, preoperative planning would be more widespread, and research analyses could be applied to retrospective data, after a postoperative issue is discovered. The goal of this work is to enable robust measurement of two surgical parameters of interest: the tilt of the anterior pelvic plane (APP) and the orientation of the natural acetabulum. We present a computer-aided reconstruction method to determine the APP and natural acetabular orientation from a single, preoperative X-ray. It can easily be extended to obtain other important preoperative and postoperative parameters solely based on a single AP radiograph. KW - Articulated Models, Statistical Shape And Intensity Models, 2D/3D Anatomy Reconstruction, Pelvic Parameters Measurement, Total Hip Arthroplasty Y1 - 2015 ER - TY - CHAP A1 - Mukhopadhyay, Anirban A1 - Morillo, Oscar A1 - Zachow, Stefan A1 - Lamecker, Hans T1 - Robust and Accurate Appearance Models Based on Joint Dictionary Learning Data from the Osteoarthritis Initiative T2 - Lecture Notes in Computer Science, Patch-Based Techniques in Medical Imaging. Patch-MI 2016 N2 - Deformable model-based approaches to 3D image segmentation have been shown to be highly successful. Such methodology requires an appearance model that drives the deformation of a geometric model to the image data. Appearance models are usually either created heuristically or through supervised learning. Heuristic methods have been shown to work effectively in many applications but are hard to transfer from one application (imaging modality/anatomical structure) to another. On the contrary, supervised learning approaches can learn patterns from a collection of annotated training data. In this work, we show that the supervised joint dictionary learning technique is capable of overcoming the traditional drawbacks of the heuristic approaches. Our evaluation based on two different applications (liver/CT and knee/MR) reveals that our approach generates appearance models, which can be used effectively and efficiently in a deformable model-based segmentation framework. Y1 - 2016 U6 - https://doi.org/10.1007/978-3-319-47118-1_4 VL - 9993 SP - 25 EP - 33 ER - TY - CHAP A1 - Mukhopadhyay, Anirban A1 - Oksuz, Ilkay A1 - Bevilacqua, Marco A1 - Dharmakumar, Rohan A1 - Tsaftaris, Sotirios T1 - Data-Driven Feature Learning for Myocardial Segmentation of CP-BOLD MRI T2 - Functional Imaging and Modeling of the Heart N2 - Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP- BOLD) MR is capable of diagnosing an ongoing ischemia by detecting changes in myocardial intensity patterns at rest without any contrast and stress agents. Visualizing and detecting these changes require significant post-processing, including myocardial segmentation for isolating the myocardium. But, changes in myocardial intensity pattern and myocardial shape due to the heart’s motion challenge automated standard CINE MR myocardial segmentation techniques resulting in a significant drop of segmentation accuracy. We hypothesize that the main reason behind this phenomenon is the lack of discernible features. In this paper, a multi scale discriminative dictionary learning approach is proposed for supervised learning and sparse representation of the myocardium, to improve the myocardial feature selection. The technique is validated on a challenging dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canine subjects. The proposed method significantly outperforms standard cardiac segmentation techniques, including segmentation via registration, level sets and supervised methods for myocardial segmentation. Y1 - 2015 U6 - https://doi.org/10.1007/978-3-319-20309-6_22 VL - 9126 SP - 189 EP - 197 PB - Springer ER - TY - CHAP A1 - Mukhopadhyay, Anirban A1 - Oksuz, Ilkay A1 - Bevilacqua, Marco A1 - Dharmakumar, Rohan A1 - Tsaftaris, Sotirios T1 - Unsupervised myocardial segmentation for cardiac MRI T2 - Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 N2 - Though unsupervised segmentation was a de-facto standard for cardiac MRI segmentation early on, recently cardiac MRI segmentation literature has favored fully supervised techniques such as Dictionary Learning and Atlas-based techniques. But, the benefits of unsupervised techniques e.g., no need for large amount of training data and better potential of handling variability in anatomy and image contrast, is more evident with emerging cardiac MR modalities. For example, CP-BOLD is a new MRI technique that has been shown to detect ischemia without any contrast at stress but also at rest conditions. Although CP-BOLD looks similar to standard CINE, changes in myocardial intensity patterns and shape across cardiac phases, due to the heart’s motion, BOLD effect and artifacts affect the underlying mechanisms of fully supervised segmentation techniques resulting in a significant drop in segmentation accuracy. In this paper, we present a fully unsupervised technique for segmenting myocardium from the background in both standard CINE MR and CP-BOLD MR. We combine appearance with motion information (obtained via Optical Flow) in a dictionary learning framework to sparsely represent important features in a low dimensional space and separate myocardium from background accordingly. Our fully automated method learns background-only models and one class classifier provides myocardial segmentation. The advantages of the proposed technique are demonstrated on a dataset containing CP-BOLD MR and standard CINE MR image sequences acquired in baseline and ischemic condition across 10 canine subjects, where our method outperforms state-of-the-art supervised segmentation techniques in CP-BOLD MR and performs at-par for standard CINE MR. Y1 - 2015 U6 - https://doi.org/10.1007/978-3-319-24574-4_2 VL - LNCS 9351 SP - 12 EP - 20 ER - TY - CHAP A1 - Oksuz, Ilkay A1 - Mukhopadhyay, Anirban A1 - Bevilacqua, Marco A1 - Dharmakumar, Rohan A1 - Tsaftaris, Sotirios T1 - Dictionary Learning Based Image Descriptor for Myocardial Registration of CP-BOLD MR T2 - Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 N2 - Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP- BOLD) MRI is a new contrast agent- and stress-free imaging technique for the assessment of myocardial ischemia at rest. The precise registration among the cardiac phases in this cine type acquisition is essential for automating the analysis of images of this technique, since it can potentially lead to better specificity of ischemia detection. However, inconsistency in myocardial intensity patterns and the changes in myocardial shape due to the heart’s motion lead to low registration performance for state- of-the-art methods. This low accuracy can be explained by the lack of distinguishable features in CP-BOLD and inappropriate metric defini- tions in current intensity-based registration frameworks. In this paper, the sparse representations, which are defined by a discriminative dictionary learning approach for source and target images, are used to improve myocardial registration. This method combines appearance with Gabor and HOG features in a dictionary learning framework to sparsely represent features in a low dimensional space. The sum of squared differences of these distinctive sparse representations are used to define a similarity term in the registration framework. The proposed descriptor is validated on a challenging dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canines. Y1 - 2015 U6 - https://doi.org/10.1007/978-3-319-24571-3_25 VL - 9350 SP - 205 EP - 213 PB - Springer ER - TY - CHAP A1 - Saleh, Salah A1 - Sahu, Manish A1 - Zafar, Zuhair A1 - Berns, Karsten T1 - A Multimodal Nonverbal Human-robot Communication System T2 - VI International Conference on Computational Bioengineering N2 - Socially interactive robot needs the same behaviors and capabilities of human to be accepted as a member in human society. The environment, in which this robot should operate, is the human daily life. The interaction capabilities of current robots are still limited due to complex inter-human interaction system. Humans usually use different types of verbal and nonverbal cues in their communication. Facial expression and head movement are good examples of nonverbal cues used in feedback. This paper presents a biological inspired system for Human-Robot Interaction (HRI). This system is based on the interactive model of inter-human communication proposed by Schramm. In this model, the robot and its interaction partner can be send and receive information at the same time. For example, if the robot is talking, it also perceive the feedback of the human via his/her nonverbal cues. In this work, we are focusing on recognizing the facial expression of human. The proposed facial expression recognition technique is based on machine learning. Multi SVMs have been used to recognize the six basic emotions in addition to the neutral expression. This technique uses only the depth information, acquired by Kinect, of human face. Y1 - 2015 ER - TY - CHAP A1 - Tack, Alexander A1 - Kobayashi, Yuske A1 - Gauer, Tobias A1 - Schlaefer, Alexander A1 - Werner, René T1 - Groupwise Registration for Robust Motion Field Estimation in Artifact-Affected 4D CT Images T2 - ICART: Imaging and Computer Assistance in Radiation Therapy: A workshop held on Friday 9th October as part of MICCAI 2015 in Munich, Germany. MICCAI workshop. 2015. N2 - Precise voxel trajectory estimation in 4D CT images is a prerequisite for reliable dose accumulation during 4D treatment planning. 4D CT image data is, however, often affected by motion artifacts and applying standard pairwise registration to such data sets bears the risk of aligning anatomical structures to artifacts – with physiologically unrealistic trajectories being the consequence. In this work, the potential of a novel non-linear hybrid intensity- and feature-based groupwise registration method for robust motion field estimation in artifact-affected 4D CT image data is investigated. The overall registration performance is evaluated on the DIR-lab datasets; Its robustness if applied to artifact-affected data sets is analyzed using clinically acquired data sets with and without artifacts. The proposed registration approach achieves an accuracy comparable to the state-of-the-art (subvoxel accuracy), but smoother voxel trajectories compared to pairwise registration. Even more important: it maintained accuracy and trajectory smoothness in the presence of image artifacts – in contrast to standard pairwise registration, which yields higher landmark-based registration errors and a loss of trajectory smoothness when applied to artifact-affected data sets. Y1 - 2015 SP - 18 EP - 25 ER -