Prof. Gábor Székely | Computer Vision Laboratory | www.vision.ee.ethz.ch

Prof. Gábor Székely Biomedical Image Computing

MissionDuring the past 30 years biomedical image computing has been gradually established as an independent research field within computer vision. While still relying on the same scientific and methodological principles of image analysis, the unique nature of the images to be processed (mostly acquired by specialized imaging modalities like radiology or microscopy) and the highly domain-specific requirements and expectations of the end-users formed a specialized re- search community. The advancement of the field has been highly motivated by the fast and continuing development of new very powerful biomedical imaging technology, leading to an explosion of the amount of image data produced, and making their manual interpretation highly ineffective, if not unfeasible in all biomedical application domains.

At the same time, traditional image analysis methods proved to be insufficient for the full utilization of the information provided by the radiological images acquired today even rou-tinely in the daily clinical practice. The relevant problems can only be successfully addressed by the combination of methods originating from computer vision, visualization, phys- iological modeling and simulation technologies. The ultimate target of our research group is to develop a theoretically well grounded, practice-oriented framework in the confluence of these research fields allowing to create efficient solu- tions not only for clinical diagnosis and therapy, but also for supporting basic and pre-clinical research.

Curriculum VitaeProf. Gábor SzékelyProfessor of Medical Image Analysis and Visualization

Degrees / Higher Education1986: Dr. Univ. in Analytical Chemistry, Technical University of Budapest, Hungary1981: MSC in Applied Mathematics, Eötvös Lorand University, Budapest, Hungary1974: MSC in Chemical Engineering, Technical University of Budapest, Hungary

Professional Career2012-present: Head of MedTech at the Swiss Commission for Technology and Innovation2008-present: Full Professor of Medical Image Analysis and Visualization2007-present: Co-founder and member of the board of the spin-off company Virtamed2002-2008: Associate Professor of Medical Image Analysis and Visualization2001-2013: Director, Swiss National Center of Competence and Research on Computer Aided and Image Guided Medical Interventions1991-2002: Senior Research Fellow, Computer Vision Laboratory1986-1990: Software Development, Spectrospin AG, Fällanden, Switzerland1974-1986: Member, later Head of Computer Department, Institute of Isotopes of the Hungarian Academy of Sciences, Budapest, Hungary

Major Honors and Awards2013: MICCAI Fellow2012: Best Paper Award, MICCAI Workshop on Medical Computer Vision2006: Best Workshop Paper Award, Int. Conf. on Scientific Computing2010-present: Member of the Swiss Commission for Technology and Innovation1981 / 82: SNSF Swiss / Hungarian exchange scholarship

Professor Gábor Székely

Prof. Gábor Székely | Computer Vision Laboratory | www.vision.ee.ethz.ch

Computational PhysiologyPhysical sciences have been very successful in characterizing complex systems depending on numerous, interdependent factors using formal mathematical models. The potential of such modeling and simulation techniques for the descrip-tion of biological systems has been recently demonstrated by major, coordinated research efforts, most notably within the frames of the Virtual Physiological Human initiative. Due to the broad availability of substantial computational power and the development of increasingly sophisticated modeling ap-proaches and numerical simulation techniques, biomechani-cal simulations have been established as valuable tools with significant potential for patient-specific treatment planning and the prediction of surgical outcome. While this approach has been successfully applied for the physiological charac-terization of all tissues and organs, our work has been mostly concentrating on the cardiovascular system. On one side we have been using computational fluid dynamic simulations for understanding arterial blood flow and its interaction with the vessel wall. We are especially interested in estimating wall shear stress and its role in aneurysm formation. We are also developing tools supporting diagnostic decisions by quantify-ing flow patterns in aneurysms, while also enabling individual planning of minimally invasive therapies by predicting the ef-fects of stent placement.

We also develop a biomechanical simulation framework for the planning of minimally invasive aortic valve replace-ment by accounting for the mechanical interaction between the expanding stent and the affected cardiac anatomy. This will allow both selecting the most appropriate artificial valve and the optimization of its position in a patient specific fashion by relying on pre-operative imaging data.

Beside such clinical platforms for improving patient care, these biophysical models can also be used as basic research tools, providing deeper insight into the physiological process-es underlying healthy and pathological development. A funda-mental challenge of creating such models is the integration of physiological processes on vastly differing spatio-temporal scales. We have been creating several simulation platforms for accounting for e.g. vessel formation during intussuscep-tive angiogenesis, vascularized tumor growth, or clot forma-tion in stented aneurysms.

Bioimage Analysis and ModelingThe bioimage analysis and modeling group aims at bridging quantitative models of the dynamics of multiscale biological processes with microscopy images as a primary source of data. In this task we have developed strong interactions with experimental molecular cell biologists at ETH. Our activity has been to develop both abstract and mechanistic models of biological processes spanning a wide range of spatio-tempo-ral scales. In synergy, we are developing the necessary data analysis frameworks to assess those quantitative models and help biologists in testing their hypotheses.

Currently our main focus is to model and analyze micro-tubule dynamics in yeast cells. This project is mainly sup-ported by a SystemsX grant, involving 3 other laboratories. We are responsible for the image analysis of 4D fluorescent microscopy images of yeast cytoplasmic microtubules at a high

spatio-temporal resolution. To overcome the resolution limits in space and time we are developing a model-based image processing framework, accounting both for the image acqui-sition features (blurring, noise sources, sampling) and for the microtubule geometry and photometry. To assess those issues and help designing image acquisition setups we are developing a virtual microscope framework, simulating artifi-cial images from simulated dynamics and the specification of the image acquisition system.

Surgical Training SimulationEndoscopic operations have recently become a very popular technique for both diagnosis and treatment of many kinds of human diseases and injuries. The basic idea of endoscopic surgery is to minimize damage of the surrounding healthy tis-sue, by replacing relatively large cuts in open surgery by small perforation holes, resulting in a major gain in patient recov-ery after operation. The price for these advantages is paid by the surgeon, who loses direct contact with the operation site. Performing operations under these conditions demands very specific skills of the surgeon, which can only be gained with extensive training. Virtual reality based surgical simulator systems offer a very elegant solution to this training prob-lem. These can theoretically provide a realistic and configu-rable training environment bridging the gap between basic training and performing the actual interventions on patients. Our research group has been working on the development of such simulators over the past 20 years, addressing all related issues like highly realistic visualization of the surgical scene, virtual surgical instruments fully mimicking their clinical counterpart and providing force feedback to the surgeon, as well as real-time simulation of tissue behavior including de-formation, cutting, or bleeding. Dedicated devices for training numerous interventions on the field of gynecology, urology, or orthopedics have been developed in collaboration with the spin-off company Virtamed which is successfully marketing the corresponding products worldwide. Most recently a train-ing system for arthroscopic interventions has been developed, shown in Figure 3.

Figure 3: Training simulator for arthroscopic interventions

Medical Image Analysis and Clinical Guidance One major direction for our efforts focuses on using statis-tical models for image analysis and interventional planning. Mapping the thalamus (Figure 1 top) and localizing thalamic nuclei for the accurate targeting in neurosurgical procedures such as the treatment of Parkinson’s disease is one exam-ple. For this, we rely on machine learning techniques, image registration, and shape modeling. Our efforts are also tar-geted towards the use of Bayesian approaches and graphical models, in particular for medical image segmentation and registration. A major direction in this field is the extraction of skeletal bone surfaces through automatic methods without any operator interaction as seen in Figure 1 (bottom). Such automated techniques that are applicable to large clinical image datasets in an unsupervised manner are a major step towards one of our immediate goals: the generation of statis-tical shape models, which has been an important paradigm shift in clinical medicine in recent years.

Our efforts also target instrumentation and system inte-gration aspects of clinical guidance. For instance, regarding cardiac RF-ablation, which is a common treatment for cardiac arrhythmia, our group has developed the catheter tip proto-type seen in Figure 2 (top) that is custom-fitted with a miniatur-ized ultrasound transducer and custom-designed force sen-sors. This design aims to give control on the ablation extent by allowing the operator both to apply a uniform and predictable ablation, and to image the size of ablated lesion, which is a feedback currently lacking in clinical practice. Another exam-ple of our work on ultrasound image processing is the system seen in Figure 2 (bottom) that can measure the venous pres-sure noninvasively by means of ultrasound imaging. This is achieved by acquiring real-time images from an ultrasound machine concurrently with real-time measurements of con-tact pressure between the skin and the ultrasound transduc-er.

Organ Motion Modeling and CompensationMinimal invasive tumor therapies are getting ever more so-phisticated with novel treatment approaches and new devic-es allowing for improved targeting precision. Applying these effectively requires precise localization of the structures of interest. Vital processes, such as respiration and heartbeat, induce organ motion, which cannot be neglected during ther-apy. Yet observing and quantifying the motion of all structures of interest, as well as simulations required for accurate ther-apy planning, can often be computationally very expensive, prohibiting real-time calculations.

We employ prior knowledge about the expected motion to bridge this gap. We have shown that high-quality 4D-MRIs of the liver and the lung during free-breathing can be obtained by retrospective sorting. Our population 4D motion models, derived from these images, provide spatial predictions from partial observations within a clinically acceptable range. Ac-curacies are further improved by determining the prediction uncertainties and employing a gated tracking approach. Be-sides the liver as therapy target, we successfully devised a method for detecting and tracking ribs in MRIs, which is very challenging but extremely important for an effective focused ultrasound or proton therapy. The spatial motion modeling re-search was complemented by developing a fast, accurate, and robust ultrasound tracking method for long sequences, and by an improved temporal prediction approach to compensate for system latencies. In collaboration with the Paul Scherrer Institute, optimal planning strategies for proton therapy of the liver during free-breathing are investigated.

Research Activities and Achievements

Prof. Gábor Székely | Computer Vision Laboratory | www.vision.ee.ethz.ch

Figure 1 (top): Mapping thalamic nuclei for improved surgical targeting. (Bottom): Automatic segmentation of femur and jugular bone.

Figure 2 (top): Prototype catheter tip for cardiac RF-ablation with ultrasound transducer and custom-designed force sensor, and ablated heart samples. (bottom): System for automatic measurement of intravenous pressure using ultra sound.