Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Stefanie Demirci Akad. Rätin
Dr. Stefanie Demirci

Coordination Master program Biomedical Computing
Managing Director
Chair for Computer Aided Medical Procedures & Augmented Reality
Fakultät für Informatik
Technische Universität München
Boltzmannstr. 3
85748 Garching b. München

room: MI 03.13.056
phone: +49 89 289-19405
fax: +49 89 289-17059

Google Scholar Profile: Google+


  • Diploma Thesis: A comparison of discriminant analysis methods for optimizing high-dimensional feature spaces (in collaboration with Siemens Corporate Technology)
  • Dissertation: New Approaches to Computer Assistance for Endovascular Abdominal Aortic Repair (download link)

Research Interests

  • Interventional image registration
  • Disocclusion in medical images
  • Medical device detection/navigation
  • Segmentation in medical treatment planning

Active research projects

Estimation of Flow Parameters from X-ray Data

Estimation of Flow Parameters from X-ray Data

This project focuses on the quantification of blood flow based on 2D and 3D angiographic X-ray image data, which is of particular interest during minimally invasive interventional procedures such as recanalizations of stenotic vessels and embolizations of arteries that feed tumors or vascular malformations in the patient's brain, for example. Our projects cover the evaluation of model-based approaches involving CFD (computational fluid dynamics) simulations as well as methods that aim at estimating the optical flow in the images and deriving quantitative blood flow information from these results.
Living Review

Living Review

Review papers generally provide a comprehensive overview over the plethora of existing techniques with a particular focus on the suitable classification criteria. However, we wish to go beyond a static literature review which is naturally doomed to be outdated after a certain period of time due to the rapid research progress. Our living review concept augments conventional review papers with an extendable and interactive database in order to obtain an intuitive nd structured visualization whose currency goes beyond the one of a printed paper. All papers in this database are labeled with one or multiple tags according to 13 carefully defined categories. The classification of all entries can then be visualized as one or multiple trees which are presented via a web-based interactive app ( allowing the user to choose a unique perspective for literature review. In addition, the user can search the underlying database for specific tags or publications related to vessel registration. Many applications of this framework are conceivable, including the use for getting a general overview on the topic or the utilization by physicians for deciding about the best-suited algorithm for a specific application.
Cross Modalities Image Synthesis

Cross Modalities Image Synthesis

One of the interesting talks in the Generative and Discriminative Learning for Medical Imaging Tutorial in the last MICCAI 2014 was presented by Jerry Prince regarding Image Synthesis and cross-modalities, motivating us to work on such a challenging topic. In our project for MLMI course, we would like to implement kind of Supervised Dictionary Learning incorporating the global consistency, discriminative labeling, and the Transformation-invariant.
Registration of Angiographic Images

Registration of Angiographic Images

Angiographic images visualize vascular structure in different modalities like X-Ray, CT, or MR data sets. In many medical applications, a registration and proper visualization of the data sets, especially the vasculature is useful for a better navigation. The focus of this project lies on 2D/3D registration of angiographic data where intensity-based, feature-based, and hybrid approaches are evaluated, the latter two of them requiring an accurate 2D and 3D segmentation of the data. The main clinical partner is the radiology department of the Universitätsklinikum Großhadern (Ludwig-Maximilian Universität München) , industrial partner is Siemens Medical Solutions, Forchheim.
Assessment of Fluid Tissue Interaction Using Multi-Modal Image Fusion for Characterization and Progression of Coronary Atherosclerosis

Assessment of Fluid Tissue Interaction Using Multi-Modal Image Fusion for Characterization and Progression of Coronary Atherosclerosis

Coronary artery diseases such as atherosclerosis are the leading cause of death in the industrialized world. In this project, we develop computational tools for segmentation and registration problems on intravascular images including IVUS (Intravascular Ultrasound) and OCT (Optical Coherence Tomography). One sample component of this project is Automatic Stent Implant Follow-up from Intravascular OCT Pullbacks. The stents are automatically detected and their distribution is analyzed for monitoring of the stents: their malpositioning and/or tissue growth over stent struts.
Endovascular Stenting of Aortic Aneurysms

Endovascular Stenting of Aortic Aneurysms

Endovascular stenting is a minimally invasive treatment technique for aortic aneurysms or dissections. Thereby, a certain aortic prosthesis (stent graft) is placed inside the aortic aneurysm in order to prevent a life-threatening rupture of the aortic wall. Prior to the intervention, a computed tomography angiography (CTA) is acquired on which the surgical staff can measure the parameter of the desired stent graft and finalize the intervention workflow. The entire interventional catheter navigation is done under 2D angiography imaging where the physician is missing the important 3D information. The purpose of our project is two-fold:
1. In the planning phase, a modified graph cuts algorithm automatically segments the aorta and aneurysm, so the surgical staff can choose an appropriate type of stent to match the segmented location, length, and diameter of the aneurysm and aorta. By visualizing the defined stent graft next to the three-dimensionally reconstructed aneurysm, mismeasurements can be detected in an early stage. Our main goal is the creation of an interactive simulation system that predicts the behaviour of the aortic wall and the movement of the implanted stent graft.
2. During implantation of the stent graft, after an intensity based registration of CTA and angiography data, the current navigation can be visualized in the 3D CT data set at any time. This includes solutions for electro-magnetic tracking of catheters as well as guide wires and stent grafts. Eventually, Our main goal is the creation of solutions that enable the surgeon to enhance the accuracy of the navigation and positioning, along with a minimum use of angiography, leading to less radiation exposure and less contrast agent injection.
Crowdsourcing for Healthcare

Crowdsourcing for Healthcare

Today's clinical procedures often generate a large amount of digital images requiring close inspection. Manual examination by physicians is time-consuming and machine learning in computer vision and pattern recognition is playing an increasing role in medical applications. In contrast to pure machine learning methods, crowdsourcing can be used for processing big data sets, utilising the collective brainpower of huge crowds. Since individuals in the crowd are usually no medical experts, preparation of medical data as well as an appropriate visualization to the user becomes indispensable. The concept of gamification typically allows for embedding non-game elements in a serious game environment, providing an incentive for persistent engagement to the crowd. Medical image analysis empowered by the masses is still rare and only a few applications successfully use the crowd for solving medical problems. The goal of this project is to bring the gamification and crowdsourcing to the Medical Imaging community.
Prediction of Rupture Risk for Abdominal Aortic Aneurysms

Prediction of Rupture Risk for Abdominal Aortic Aneurysms

An abdominal aortic aneurysm (AAA) describes an enlarged aortic diameter in the abdominal part of the body. Due to weakening rupture of the inner aortic wall layer, blood cells accumulate inside the wall layers and lead to a thrombus. In order to choose a suitable individual treatment, a prediction of rupture risk would be helpful. However, it is not possible to predict the ruputure risk only with quantative parameters extracted out of CT-images such as size and diameter. It is important to also include qualitative predictors like characteristics of the aortic wall and fluid dynamics.
Together with our medical and academic partners, we are interested in creating a model of the aorta and its thrombus in order to do certain calculations on wall stress and fluid dynamic computations. A further integration of other medical imaging devices such as PET/CT and IVUS into the geometrical model can provide more information about biochemical activities inside the aneurysmatic walls.


To ease the development of a hybrid modality we have created a generic, portable, lightweight, and easily extensible, client-server based framework, implemented in C++, to control the communication between multiple imaging and tracking devices. Our framework Computer-Aided Medical Procedures Communication (CAMPCom), allows developers of hybrid systems to focus on the most important implementation aspects by freeing them from the more generic tasks of data serialization and exchange.


Professional Activity



  • Journals: Medical Image Analysis, Medical Physics, Cardiovascular Imaging, IEEE Transactions on Medical Imaging, International Journal of CARS, Physics in Medicine and Biology, Computers in Medicine and Biology, Annals of Biomedical Engineering, European Radiology
  • Conferences & Workshops: MICCAI, CVPR, ICCV, ECCV, ISBI, ICIP, MEDICON

Conferences & Workshops

Invited Talks



S. Demirci
New Approaches to Computer Assistance for Endovascular Abdominal Aortic Repairs
Technische Universtät München, 2011 (bib)

Abstract: This work presents new approaches to computer assistance for endovascular abdominal aortic repairs. In the current clinical workflow there is no technical guidance during the intervention except for two dimensional X-Ray images. Here, we introduce interventional registration methods that are able to handle such occlusions and further image dissimilarities. Thereby, contrast removal and disocclusion techniques are integrated within an image registration procedure. Furthermore, we show for the first time that a detection of the stent graft in the interventional image and a three dimensional recovery of its shape can be obtained at the same time. This allows image-based tracking of the prosthesis and simultaneous visualization within a volume rendering of the patient scan. All introduced methods are completely image based and do not require additional equipment to be introduced into the intervention room.


Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting

Editors: Simone Balocco, Maria Zuluaga, Guillaume Zahnd, Su-Lin Lee, S. Demirci
Hardcover ISBN: 9780128110188
eBook ISBN: 9780128110195
Imprint: Academic Press
Published Date: 5th December 2016


S. Matl, R. Brosig, M. Baust, N. Navab, S. Demirci
Vascular Image Registration Techniques: A Living Review
Medical Image Analysis, Volume 35, pp. 1-7, 2017 (bib)
S. Albarqouni, S. Matl, M. Baust, N. Navab, S. Demirci
Playsourcing: A Novel Concept for Knowledge Creation in Biomedical Research
Proceedings of MICCAI Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, Athens, Greece, October 2016 (bib)
C. Baur, S. Albarqouni, S. Demirci, N. Navab, P. Fallavollita
CathNets: Detection and Single-View Depth Prediction of Catheter Electrodes
7th International Conference on Medical Imaging and Augmented Reality (MIAR), 24-26 August, 2016, Bern, Switzerland. (Best Paper Award) (bib)
S. Albarqouni, U. Konrad, L. Wang, N. Navab, S. Demirci
Single-View X-Ray Depth Recovery: Towards a Novel Concept for Image-Guided Interventions
International Journal of Computer Assisted Radiology and Surgery (IJCARS), 2016, June 2016, Volume 11, Issue 6, pp 873-880. (bib)
S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci, N. Navab
AggNet: Deep Learning from Crowds for Mitosis Detection in Breast Cancer Histology Images
IEEE Transactions on Medical Imaging (TMI), Special Issue on Deep Learning, vol. 35, no. 5, pp. 1313 - 1321, 2016. (bib)
K. Hofschen, T. Geissler, N. Rieke, C. Schulte zu Berge, N. Navab, S. Demirci
Image Descriptors in Angiography
Workshop Bildverarbeitung fuer die Medizin (BVM), March 13-15, 2016. (bib)
M. Zweng, P. Fallavollita, S. Demirci, M. Kowarschik, N. Navab, D. Mateus
Automatic Guide-Wire Detection for Neurointerventions Using Low-Rank Sparse Matrix Decomposition and Denoising
MICCAI 2015 Workshop on Augmented Environments for Computer-Assisted Interventions (bib)
S. Virga, V. Dogeanu, P. Fallavollita, R. Ghotbi, N. Navab, S. Demirci
Optimal C-arm Positioning for Aortic Interventions
Bildverarbeitung fuer die Medizin (BVM 2015), Luebeck, Germany, March 2015 (bib)
S.-L. Lee, S. Balocco, C. Gatta, S. Demirci, G. A. Tangen
Editorial of the special issue STENT
Computerized Medical Imaging and Graphics, Special Issue STENT, In Press, 2015 (bib)
D. Volpi, M. H. Sarhan, R. Ghotbi, N. Navab, D. Mateus, S. Demirci
Online Tracking of Interventional Devices for Endovascular Aortic Repair
International Journal of Computer Assisted Radiology and Surgery, June 2015, Volume 10, Issue 6, pp 773-781 (bib)
P. Maday, M. Kowarschik, S. Demirci, N. Navab
Towards blood flow quantification using dense flow volumes
R. Brosig, P. Maday, M. Kowarschik, A. Katouzian, S. Demirci, N. Navab
Blood Flow Quantification using 1D CFD Parameter Identification
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, San Diego, CA, USA, Feb 19-23 2014. (bib)
Xiang Wang, S. Demirci, C. Schulte zu Berge, P. Fallavollita, N. Navab
Improved Interventional X-ray Appearance
International Symposium on Mixed and Augmented Reality (ISMAR), 2014 (bib)
T. Benz, M. Kowarschik, J. Endres, T. Redel, S. Demirci, N. Navab
A Fourier-Based Approach to the Angiographic Assessment of Flow Diverter Efficacy in the Treatment of Cerebral Aneurysms
IEEE Transactions on Medical Imaging, volume 33, issue 9, pages 1788-1802, September 2014. (bib)
S.-L. Lee, S. Demirci, P. Radeva, G. Unal
Editorial Note
Computerized Medical Imaging and Graphics, vol. 38, pages 69-69, 2014 (bib)
T. Aksoy, G. Unal, S. Demirci, N. Navab, M. Degertekin
Template-based CTA to X-ray Angio Rigid Registration of Coronary Arteries in Frequency Domain with Automatic X-Ray Segmentation
Medical Physics, Volume 40, Issue 10, October 2013, Pages 1903-1918 (bib)
S. Demirci, F. Manstad-Hulaas, N. Navab
Interventional 2D-3D Registration in the Presence of Occlusion
XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, IFMBE Proceedings Volume 41, 2014, pp 277-280. (bib)
A. Schoch, B. Fuerst, F. Achilles, S. Demirci, N. Navab
A Lightweight and Portable Communication Framework for Multimodal Image-Guided Therapy
The Sixth International Workshop on Systems and Architectures for Computer Assisted Interventions (SACAI), Nagoya, Japan, September 2013 (bib)
S. Demirci, M. Baust, O. Kutter, F. Manstad-Hulaas, H-H. Eckstein, N. Navab
Disocclusion-based 2D-3D Registration for Aortic Interventions
Computers in Biology and Medicine, Volume 43, Issue 4, 1 May 2013, Pages 312-322 (bib)
T. Aksoy, S. Demirci, M. Degertekin, N. Navab, G. Unal
Template-based CTA X-ray Angio Rigid Registration of Coronary Arteries in Frequency Domain
Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 867127, March 2013 (bib)
S. Demirci, A. Bigdelou, L. Wang, C. Wachinger, M. Baust, R. Tibrewal, R. Ghotbi, H-H. Eckstein, N. Navab
3D Stent Recovery from One X-ray Projection
14th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Toronto (CA), September 18-22, 2011 (bib)
D. Ugurlu, S. Demirci, N. Navab, M. S. Celebi
A Vessel Segmentation Method for MRA Images Based on Multi-scale Analysis and Level Set Framework
3rd International MICCAI-Workshop on Computation and Visualization on (Intra)Vascular Imaging, Toronto, Canada, September 18, 2011 (bib)
S. Demirci, N. Navab
The CAMP Lab - Computer Aided Medical Procedures and Augmented Reality
SBC Journal on 3D Interactive Systems - Special Issue on VR and HCI Labs, Vol. 2, No. 2, pp. 28-31 (bib)
F. Manstad-Hulaas, G. A. Tangen, S. Demirci, M. Pfister, S. Lydersen, T. A. Nagelhus Hernes
Endovascular Image-Guided Navigation - Validation of Two Volume-Volume Registration Algorithms
Minimally Invasive Therapy & Allied Technologies 20(5), pp. 282 - 289, 2011 (bib)
S. Demirci, G. Lejeune, N. Navab
Hybrid Deformable Model for Aneurysm Segmentation
Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on, pp. 33-36, 2009. (bib)
M. Baust, S. Demirci, N. Navab
Stent Graft Removal for Improving 2D-3D Registration
IEEE International Symposium on Biomedical Imaging (ISBI), Boston, MA, USA, June 28-July 1 2009. (bib)
S. Demirci, F. Manstad-Hulaas, N. Navab
Extracting a Purely Non-Rigid Deformation Field of a Single Structure
Proceedings of Bildverarbeitung fuer die Medizin (BVM 2009), Heidelberg, Germany, March 2009 (bib)
S. Demirci, F. Manstad-Hulaas, N. Navab
Quantification of Abdominal Aortic Deformation after EVAR
Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 72611U, February 2009 (bib)
S. Demirci, O. Kutter, F. Manstad-Hulaas, R. Bauernschmitt, N. Navab
Advanced 2D-3D Registration for Endovascular Aortic Interventions: Addressing Dissimilarity in Images
Proc. SPIE 6918, Medical Imaging 2008: Visualization, Image-guided Procedures, and Modeling, 69182S, February 2008 (bib)
O. Kutter, S. Kettner (=Demirci), E.U. Braun, N. Navab, R. Lange, R. Bauernschmitt
Towards an Integrated Planning and Navigation System for Aortic Stent-Graft Placement
Proc. of Computer Assisted Radiology and Surgery (CARS), Berlin, Germany, June 2007. (bib)
S. Kettner (=Demirci), C. Madden, R. Ziegler
Direct Rotational Interaction with a Spherical Projection
Proceedings of the Creativity&Cognition Symposium on Interaction: Systems, Practice and Theory, 2004. The original publication is available online. (bib)


European patent EP2088556 A1: "Method and system for registration to align at least one preoperative image to at least one intraoperativley acquired image"

Title: Dr.
Circumference of your head (in cm):  
Firstname: Stefanie
Lastname: Demirci
Picture: s_demirci_small.jpg
Birthday: 12.01.1981
Nationality: Blank
Languages: English, German, French, Turkish, Bavarian
Groups: Registration/Visualization, Segmentation, Medical Imaging, Computer-Aided Surgery, Crowdsourcing
Expertise: Registration/Visualization, Segmentation, Medical Imaging, Computer-Aided Surgery, 3D Interaction
Position: Scientific Staff
Status: Active
Emailbefore: demirci
Room: MI 03.13.056
Telephone: +49 89 289 19405
Defensedate: 12 April 2011
Thesistitle: New Approaches to Computer Assistance for Endovascular Abdominal Aortic Repairs

Edit | Attach | Refresh | Diffs | More | Revision r1.146 - 15 Jan 2017 - 16:18 - StefanieDemirci

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