Jan Egger

VLDB 2026 Research / reviewers in the wild / expert

dblp:31/3828 · DBLP profile ↗

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25ranked-venue papers

3as first author

15since 2021 · last 2026

0000-0002-5225-1982ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 17 · 3 first-author · 9 since 2021Artificial intelligence and machine learning · 8 · 3 first-author · 4 since 2021Graphics, computer vision, multimedia, augmented reality and games · 6 · 3 since 2021Human-computer interaction and ubiquitous computing · 3 · 3 first-author

Year	Publication	Venue	Position
2026	Beyond benchmarks: Towards robust artificial intelligence bone segmentation in socio-technical systems abstract Despite the advances in automated medical image segmentation, AI models still underperform in various clinical settings, posing challenges for integration into real-world workflows. In this pre-registered prospective multicenter evaluation, we analyzed 20 state-of-the-art mandibular segmentation models across 19,218 segmentations of 1,000 clinically resampled CT/CBCT scans. Our results suggest that for a given model, segmentation accuracy can vary by up to 25% in Dice score as socio-technical factors such as voxel size, bone orientation, and patient conditions (e.g., osteosynthesis or pathology) shift from favorable to adverse. Higher sharpness, isotropic smaller voxels, and neutral orientation significantly improved results, while metallic osteosynthesis and anatomical complexity led to significant degradation. Our findings challenge the common view of AI models as “plug-and-play” tools and suggest evidence-based optimization recommendations for both clinicians and developers. This will in turn boost the integration of AI segmentation tools in routine healthcare. Kunpeng Xie, Lennart Johannes Gruber, Martin Crampen, Elias Tappeiner, Maxime Gillot, Jan Schepers, Jiangchang Xu, Tobias Pankert, Michel Beyer, Negar Shahamiri, Reinier ten Brink, Gauthier Dot, Charlotte Weschke, Niels van Nistelrooij, Pieter-Jan Verhelst, Zhibin Xu, Jonas Bienzeisler, Ashkan Rashad, Tabea Flügge, Ross Cotton, Shankeeth Vinayahalingam, Robert R. Ilesan, Stefan Raith, Dennis Madsen, Constantin Seibold, Tong Xi 0001, Stefaan Bergé, Sven Nebelung, Oldrich Kodym, Osku Sundqvist, Florian M. Thieringer, Hans Lamecker, Antoine Coppens, Thomas Potrusil, Joep Kraeima, Max J. H. Witjes, Guomin Wu, Xiaojun Chen 0003, Adriaan Lambrechts, Stefan Zachow, Alexander Hermans, Daniel Truhn, Victor Alves, Jan Egger, Rainer Röhrig, Frank Hölzle, Behrus Hinrichs-Puladi	Expert Syst. Appl.	48
2025	Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling abstract Shape reconstruction from imaging volumes is a recurring need in medical image analysis. Common workflows start with a segmentation step, followed by careful post-processing and, finally, ad hoc meshing algorithms. As this sequence can be time-consuming, neural networks are trained to reconstruct shapes through template deformation. These networks deliver state-of-the-art results without manual intervention, but, so far, they have primarily been evaluated on anatomical shapes with little topological variety between individuals. In contrast, other works favor learning implicit shape models, which have multiple benefits for meshing and visualization. Our work follows this direction by introducing deep medial voxels, a semi-implicit representation that faithfully approximates the topological skeleton from imaging volumes and eventually leads to shape reconstruction via convolution surfaces. Our reconstruction technique shows potential for both visualization and computer simulations. Code available at https://github.com/apepe91/dmv. Antonio Pepe 0003, Richard Schussnig, Jianning Li 0002, Christina Schwarz-Gsaxner, Dieter Schmalstieg, Jan Egger	IEEE Trans. Medical Imaging	6
2024	DeepDR: Deep Structure-Aware RGB-D Inpainting for Diminished Reality abstract Diminished reality (DR) refers to the removal of real objects from the environment by virtually replacing them with their background. Modern DR frameworks use inpainting to hallucinate unobserved regions. While recent deep learning-based inpainting is promising, the DR use case is complicated by the need to generate coherent structure and 3D geometry (i.e., depth), in particular for advanced applications, such as 3D scene editing. In this paper, we propose Deep DR, a first RGB-D inpainting framework fulfilling all requirements of DR: Plausible image and geometry inpainting with coherent structure, running at real-time frame rates, with minimal temporal artifacts. Our structure-aware generative network allows us to explicitly condition color and depth outputs on the scene semantics, overcoming the difficulty of reconstructing sharp and consistent boundaries in regions with complex backgrounds. Experimental results show that the proposed framework can outperform related work qualitatively and quantitatively. Christina Schwarz-Gsaxner, Shohei Mori, Dieter Schmalstieg, Jan Egger, Gerhard Paar, Werner Bailer, Denis Kalkofen	3DV	4
2024	Accuracy and Precision of Mandible Segmentation and Its Clinical Implications: Virtual Reality, Desktop Screen and Artificial Intelligence Lennart Johannes Gruber, Jan Egger, Andrea Bönsch, Joep Kraeima, Max Ulbrich, Vincent van den Bosch, Ila Motmaen, Caroline Wilpert, Mark Ooms, Peter Isfort, Frank Hölzle, Behrus Puladi	Expert Syst. Appl.	2
2024	GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy abstract With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts of data is unavailable. For example, rare diseases and privacy issues can lead to restricted data availability. In non-medical fields, the high cost of obtaining enough high-quality data can also be a concern. A solution to these problems can be the generation of realistic synthetic data using Generative Adversarial Networks (GANs). The existence of these mechanisms is a good asset, especially in healthcare, as the data must be of good quality, realistic, and without privacy issues. Therefore, most of the publications on volumetric GANs are within the medical domain. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs. We therefore outline GAN-based methods in these areas with common architectures, loss functions and evaluation metrics, including their advantages and disadvantages. We present a novel taxonomy, evaluations, challenges, and research opportunities to provide a holistic overview of the current state of volumetric GANs. Jianning Li 0002, Kelsey L. Pomykala, Jens Kleesiek, Victor Alves, Jan Egger	Medical Image Anal.	6
2024	Corrigendum to: GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy [Medical Image Analysis 93 (2024)] Jianning Li 0002, Kelsey L. Pomykala, Jens Kleesiek, Victor Alves, Jan Egger	Medical Image Anal.	6
2024	CellViT: Vision Transformers for precise cell segmentation and classification abstract Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.50 and an F1-detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViT. Fabian Hörst, Moritz Rempe, Lukas Heine, Constantin Seibold, Julius Keyl, Giulia Baldini 0001, Selma Ugurel, Jens T. Siveke, Barbara Grünwald, Jan Egger, Jens Kleesiek	Medical Image Anal.	10
2024	Classification of lung cancer subtypes on CT images with synthetic pathological priors Wentao Zhu 0002, Gege Ma, Geng Chen 0001, Jan Egger, Shaoting Zhang 0001, Dimitris N. Metaxas	Medical Image Anal.	5
2024	Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy abstract Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks. Zdravko Marinov, Paul F. Jaeger, Jan Egger, Jens Kleesiek, Rainer Stiefelhagen	IEEE Trans. Pattern Anal. Mach. Intell.	3
2023	Why is the Winner the Best? abstract International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Tizabi, Fabian Isensee, Tim Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David Gage Ellis, Sandy Engelhardt, Melanie Ganz-Benjaminsen, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek 0001, Jianning Li 0002, Hongwei Li 0004, Jun Ma 0016, Carlos Martín-Isla, Bjoern Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, Martin Wagner 0001, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Qi Dou 0001, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo J. Kuijf, M. Luu, Tomaz Martincic, Pedro Morais, Mohamed A. Naser, Bruno Oliveira 0002, David Owen 0001, S. Pang, Szymon Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, Numan Saeed, Adam J. Shephard, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, Sen Yang 0006, Klaus H. Maier-Hein, Paul F. Jaeger, Annette Kopp-Schneider, Lena Maier-Hein	CVPR	22
2023	The HoloLens in medicine: A systematic review and taxonomy abstract The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality (AR) display, is the main player in the recent boost in medical AR research. In this systematic review, we provide a comprehensive overview of the usage of the first-generation HoloLens within the medical domain, from its release in March 2016, until the year of 2021. We identified 217 relevant publications through a systematic search of the PubMed, Scopus, IEEE Xplore and SpringerLink databases. We propose a new taxonomy including use case, technical methodology for registration and tracking, data sources, visualization as well as validation and evaluation, and analyze the retrieved publications accordingly. We find that the bulk of research focuses on supporting physicians during interventions, where the HoloLens is promising for procedures usually performed without image guidance. However, the consensus is that accuracy and reliability are still too low to replace conventional guidance systems. Medical students are the second most common target group, where AR-enhanced medical simulators emerge as a promising technology. While concerns about human-computer interactions, usability and perception are frequently mentioned, hardly any concepts to overcome these issues have been proposed. Instead, registration and tracking lie at the core of most reviewed publications, nevertheless only few of them propose innovative concepts in this direction. Finally, we find that the validation of HoloLens applications suffers from a lack of standardized and rigorous evaluation protocols. We hope that this review can advance medical AR research by identifying gaps in the current literature, to pave the way for novel, innovative directions and translation into the medical routine. Christina Schwarz-Gsaxner, Jianning Li 0002, Antonio Pepe 0003, Jens Kleesiek, Dieter Schmalstieg, Jan Egger	Medical Image Anal.	7
2023	Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge Jianning Li 0002, David Gage Ellis, Oldrich Kodym, Laurèl Rauschenbach, Christoph Rieß, Ulrich Sure, Karsten H. Wrede, Carlos M. Alvarez, Marek Wodzinski, Mateusz Daniol, Daria Hemmerling, Hamza Mahdi, Allison Clement, Evan Kim, Zachary Fishman, Cari M. Whyne, James G. Mainprize, Michael R. Hardisty, Shashwat Pathak, Chitimireddy Sindhura, Rama Krishna Sai S. Gorthi, Degala Venkata Kiran, Subrahmanyam Gorthi, Artem Kroviakov, Antonio Pepe 0003, Christina Schwarz-Gsaxner, Adam Herout, Victor Alves, Michal Spanel, Michele R. Aizenberg, Jens Kleesiek, Jan Egger	Medical Image Anal.	37
2021	Inside-Out Instrument Tracking for Surgical Navigation in Augmented Reality abstract Surgical navigation requires tracking of instruments with respect to the patient. Conventionally, tracking is done with stationary cameras, and the navigation information is displayed on a stationary display. In contrast, an augmented reality (AR) headset can superimpose surgical navigation information directly in the surgeon’s view. However, AR needs to track the headset, the instruments and the patient, often by relying on stationary infrastructure. We show that 6DOF tracking can be obtained without any stationary, external system by purely utilizing the on-board stereo cameras of a HoloLens 2 to track the same retro-reflective marker spheres used by current optical navigation systems. Our implementation is based on two tracking pipelines complementing each other, one using conventional stereo vision techniques, the other relying on a single-constraint-at-a-time extended Kalman filter. In a technical evaluation of our tracking approach, we show that clinically relevant accuracy of 1.70 mm/1.11° and real-time performance is achievable. We further describe an example application of our system for untethered end-to-end surgical navigation. Christina Schwarz-Gsaxner, Jianning Li 0002, Antonio Pepe 0003, Dieter Schmalstieg, Jan Egger	VRST	5
2021	Automatic skull defect restoration and cranial implant generation for cranioplasty Jianning Li 0002, Gord von Campe, Antonio Pepe 0003, Christina Schwarz-Gsaxner, Enpeng Wang, Xiaojun Chen 0003, Ulrike Zefferer, Martin Tödtling, Marcell Krall, Hannes Deutschmann, Ute Schäfer, Dieter Schmalstieg, Jan Egger	Medical Image Anal.	13
2021	AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design abstract The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi. Jianning Li 0002, Pedro Pimentel, Angelika Szengel, Moritz Ehlke, Hans Lamecker, Stefan Zachow, Laura Jovani Estacio Cerquin, Christian Doenitz, Heiko Ramm, Xiaojun Chen 0003, Franco Matzkin, Virginia F. J. Newcombe, Enzo Ferrante, David Gage Ellis, Michele R. Aizenberg, Oldrich Kodym, Michal Spanel, Adam Herout, James G. Mainprize, Zachary Fishman, Michael R. Hardisty, Amirhossein Bayat, Suprosanna Shit, Bomin Wang, Zhi Liu 0004, Matthias Eder, Antonio Pepe 0003, Christina Schwarz-Gsaxner, Victor Alves, Ulrike Zefferer, Gord von Campe, Karin Pistracher, Ute Schäfer, Dieter Schmalstieg, Bjoern Menze, Ben Glocker, Jan Egger	IEEE Trans. Medical Imaging	39
2020	Detection, segmentation, simulation and visualization of aortic dissections: A review Antonio Pepe 0003, Jianning Li 0002, Malte Rolf-Pissarczyk, Christina Schwarz-Gsaxner, Xiaojun Chen 0003, Gerhard A. Holzapfel, Jan Egger	Medical Image Anal.	7
2019	Depth-Awareness in a System for Mixed-Reality Aided Surgical Procedures Mauro Sylos Labini, Christina Schwarz-Gsaxner, Antonio Pepe 0003, Jürgen Wallner, Jan Egger, Vitoantonio Bevilacqua	ICIC (3)	5
2019	Markerless Image-to-Face Registration for Untethered Augmented Reality in Head and Neck Surgery Christina Schwarz-Gsaxner, Antonio Pepe 0003, Jürgen Wallner, Dieter Schmalstieg, Jan Egger	MICCAI (5)	5
2019	Automated Computer-aided Design of Cranial Implants Using a Deep Volumetric Convolutional Denoising Autoencoder Ana Morais, Jan Egger, Victor Alves	WorldCIST (3)	2
2015	Development of a surgical navigation system based on augmented reality using an optical see-through head-mounted display Xiaojun Chen 0003, Huixiang Wang, Xiangsen Zeng, Qiugen Wang, Jan Egger	J. Biomed. Informatics	8
2014	Robust Detection and Segmentation for Diagnosis of Vertebral Diseases Using Routine MR Images abstract Abstract The diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures, is part of the daily clinical routine. Very frequently, magnetic resonance image data are used to diagnose these kinds of pathologies in order to avoid exposing patients to harmful radiation, like X‐ray. We present a method which detects and segments all acquired vertebral bodies, with minimal user intervention. This allows an automatic diagnosis to detect scoliosis, spondylolisthesis and crushed vertebrae. Our approach consists of three major steps. First, vertebral centres are detected using a Viola–Jones like method, and then the vertebrae are segmented in a parallel manner, and finally, geometric diagnostic features are deduced in order to diagnose the three diseases. Our method was evaluated on 26 lumbar datasets containing 234 reference vertebrae. Vertebra detection has 7.1% false negatives and 1.3% false positives. The average Dice coefficient to manual reference is 79.3% and mean distance error is 1.76 mm. No severe case of the three illnesses was missed, and false alarms occurred rarely—0% for scoliosis, 3.9% for spondylolisthesis and 2.6% for vertebral fractures. The main advantages of our method are high speed, robust handling of a large variety of routine clinical images, and simple and minimal user interaction. Dzenan Zukic, Ales Vlasák, Jan Egger, Daniel Horínek, Christopher Nimsky, Andreas Kolb 0001	Comput. Graph. Forum	3
2010	Body landmark detection for a fully automatic AAA stent graft planning software system abstract In this paper, we present an approach to automate the planning of an endovascular stent graft for abdominal aortic aneurysms (AAAs), which are treated with bifurcated prosthesis (Y-stents) when located close to the iliac bifurcation. During the intervention, the folded Y-stent graft — consisting of several parts — is inserted via the iliac region and expanded inside the patient's body. The first step of the proposed approach is to detect different body landmarks with a statistical method. In the next step, these landmarks are used to calculate two vascular centerlines, which provide multiplanar reformatting (MPR) slices that are used for an automatic segmentation of the artery walls. The segmented artery walls provide the manufacturer specific measures to choose an adequate bifurcated prosthesis. In a final step, the expansion of the stent is simulated in the patient's data. Results for 50 abdominal aortic aneurysm cases provided by computed tomography angiography (CTA) acquisitions are successfully verified by a virtual stenting expert. Jan Egger, Shaohua Kevin Zhou, Stefan Großkopf, David Liu 0001, Christian Hopfgartner, Dominik Bernhardt, Christina Biermann, Christopher Nimsky, Bernd Freisleben	CBMS	1
2010	A Fast and Robust Graph-Based Approach for Boundary Estimation of Fiber Bundles Relying on Fractional Anisotropy Maps abstract In this paper, a fast and robust graph-based approach for boundary estimation of fiber bundles derived from Diffusion Tensor Imaging (DTI) is presented. DTI is a non-invasive imaging technique that allows the estimation of the location of white matter tracts based on measurements of water diffusion properties. Depending on DTI data, the fiber bundle boundary can be determined to gain information about eloquent structures, which is of major interest for neurosurgery. DTI in combination with tracking algorithms allows the estimation of position and course of fiber tracts in the human brain. The presented method uses these tracking results as the starting point for a graph-based approach. The overall method starts by computing the fiber bundle centerline between two user-defined regions of interests (ROIs). This centerline determines the planes that are used for creating a directed graph. Then, the mincut of the graph is calculated, creating an optimal boundary of the fiber bundle. Miriam H. A. Bopp, Jan Egger, Tom O'Donnell, Sebastiano Barbieri, Jan Klein 0001, Bernd Freisleben, Horst K. Hahn, Christopher Nimsky	ICPR	2
2009	A software system for stent planning, stent simulation and follow-up examinations in the vascular domain abstract In this paper, a software system for supporting stenting in the vascular domain is presented. The system covers all treatment phases from diagnosis to follow-up examinations. During the preoperative phase, the system supports the physician by suggesting the date and the kind (open surgery, minimally invasive) of intervention based on segmenting the patient's CT data. Therapy planning is additionally supported by a computer-aided stent simulation. Using virtual stenting, it is possible to simulate stents of different manufacturers in the preoperative CT data of the patient. As a result, it can be decided whether a chosen stent has proper dimensions and should be used during the following intervention. The intraoperative phase is supported by visualizing the selected stent from the planning phase at the requested position. After stenting, regular follow-up examinations are necessary to detect stent migration and endoleaks. These time-consuming procedures are also supported by the developed system. Jan Egger, Stefan Großkopf, Thomas O'Donnell, Bernd Freisleben	CBMS	1
2007	A Fast Vessel Centerline Extraction Algorithm for Catheter Simulation abstract In this paper, we present a fast and robust algorithm for centerline extraction in blood vessels. The algorithm is suitable for catheter simulation in CT data of blood vessels. It creates an initial centerline based on two user-defined points (start- and endpoint). For curved vessel structures, this initial centerline is computed by Dijkstra's shortest path algorithm. For linear vessel structures, the algorithm directly connects the start- and the endpoint to get the initial centerline. Thereafter, this initial path will be aligned in the blood vessel, resulting in the vessels centerline (i.e. an optimal catheter simulation path). The alignment is done by an active contour model combined with polyhedra placed along it. Results of the proposed centerline algorithm are demonstrated for CTA with variations in anatomy and location of pathology. Jan Egger, Zvonimir Mostarkic, Stefan Großkopf, Bernd Freisleben	CBMS	1