Prof. Dr. Jan Baumbach

Chair of Experimental Bioinformatics Technical University of Munich

Systems Medicine - The next generation of computer-assisted precision medicine

Recent advances in modern OMICS technology allow measuring the expression of all kinds of biological entities (genes, proteins, metabolites, miRNAs, etc.) at low cost and in high-throughput. Computational challenges for analyzing such big data emerge, ranging from the low signal to noise ratio to high model complexity, which render simple statistical questions arbitrarily complicated. We will discuss several bioinformatics tools for de-isolating biological networks and multiple OMICS data types: de novo pathway enrichment, in vitro high-throughput screening (HTS) data integration, time-course network enrichment, cancer subtyping, and breath analysis. Using Huntington’s disease patients’ expression data we will employ a guilt-by-association approach to illuminate the power of molecular networks to identify novel disease mechanisms. We will then extend this principle to study HTS data gained from large-scale drug screens, siRNA knock-down and CRISPR/CAS9 knock-out screens, as well as microRNA screens. In addition, we will show how to unravel temporal systems-level response patterns using whole-genome time-series gene expression profiles of lung cells after Influenza infection. We discuss how this kind of computational network biology has strong potential to enable precision medicine by classifying breast cancer subtypes utilizing complex combo-features gained from combining networks with multiple OMICS data. Finally, we will show how modern image analysis technology can be used for non-invasive precision medicine by profiling metabolic patterns in human breath from COPD and lung cancer patients.



Prof. Dr. Philippe C. Cattin

Department of Biomedical Engineering, University of Basel, Switzerland

Reinventing Bone Surgery: From Planning to Execution of a Hard-Tissue Cut

Cutting bones is one of the oldest medical procedure performed
to human patients. Thanks to the high mineral content of bone we have
archaeological evidence of skull trepanation dating back more than
10’000 years. Despite the rapid development of surgical instruments over
the last 200 years, the fundamental mechanism of bone cutting has not
changed ever since.

In this talk I will show you how researchers of the flagship project
MIRACLE (Minimally Invasive Robot-Assisted Computer-guided
LaserosteotomE) are working on laser technology to reinvent bone
surgery. The MIRACLE Project not only reinvents the way hard-tissues are
being cut but also works on novel concepts to plan and visualise these
surgical interventions in Virtual Reality and Augmented Reality


Philippe Cattin was born in Switzerland in 1967. He received his B.Sc.
degree from the University of Applied Science in Brugg/Wind
isch in 1991. In 1995 he received the M.Sc. degree in computer science
and in 2003 the Ph.D. degree in robotics from ETH Zurich, S
witzerland. From 2003 to 2007 he was a Postdoctoral Fellow with the
Computer Vision Laboratory at ETH Zurich. In 2007 he became an
Assistant Professor at the University of Basel and was promoted to
Associate Professor in 2015. He is the founder of the Center f
or medical Image Analysis and Navigation (CIAN) group at the Medical
Faculty of the University of Basel. He is currently heading t
he recently founded Department of Biomedical Engineering at the
University of Basel. Philippe just was this year a Research Fellow
at Brigham and Women’s Hospital in Boston/MA.

His research interests include medical image analysis, image-guided
therapy, robotics-guided laser osteotomy and virtual reality.
As a Principal Investigator he has finished many projects in these areas
and published over 100 papers, patents and book chapters.
He is also the founder of two spin-off companies and licensed his
patents and software to medtech companies.

Alejandro Frangi

PhD, IEEE Fellow, Professor of Biomedical Image Computing, CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine, The University of Sheffield, UK

Precision imaging: from population imaging analytics to in silico clinical trials

Medical image computing is witnessing exciting times. Specifically to this talk, new opportunities and challenges have emerged with the growing availability of large population imaging repositories being collected in the UK, USA, Canada, Germany, and The Netherlands, to name just a few.

Against this backdrop, we are interested specifically in developing new methods for and applications of Precision Imaging to maximally exploit the wealth of information behind large imaging repositories and associated metadata. Precision Imaging is not a new discipline per se but rather a distinct emphasis in medical imaging and image computing borne at the crossroads between, and unifying the efforts behind mechanistic and phenomenological model-based imaging and image computing. Precision Imaging fundamentally recognises the need of both data-driven and hypothesis-driven approaches to image analysis and image-based modelling.

The exponential rate at which data availability is growing will rapidly outpace the exponential growth rate of available computational resources and is never sufficiently abundant to deal with the combinatorial complexity intrinsic to many disease mechanisms. As described by Helbing, this implies the problem of “dark data” i.e. the share of data we cannot process is increasing with time. Consequently, we must know what data to process and how, which requires science. Anderson’s vision of Big Data (i.e. assuming we will not need theory and science anymore) is unlikely to prevail. Artificial intelligence will unlikely change this situation fundamentally. 

Precision Imaging captures three main directions in the effort to deal with the information deluge in imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is finally characterised by being descriptive, predictive and integrative about the imaged object. This paper provides a brief and personal perspective on how the field has evolved, summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights connections with past research and current trends in the field.


Alejandro (Alex) obtained his undergraduate degree in Telecommunications Engineering from the Technical University of Catalonia (Barcelona) in 1996. In 2001, he obtained his PhD in Radiology at the Image Sciences Institute of the University Medical Centre Utrecht on model-based cardiovascular image analysis. During this period he was visiting researcher at the Imperial College in London, UK, and in Philips Medical Systems BV, The Netherlands.

Prof Frangi is Professor of Biomedical Image Computing at the University of Sheffield (USFD), Sheffield, UK. He leads the Centre for Computational Imaging and Simulation Technologies in Biomedicine and is the Academic Coordinator of the MSc Bioengineering: Imaging and Sensing programme in Sheffield.

Prof Frangi has main research interests lay at the crossroad of medical image analysis and modelling with emphasis on machine learning (phenomenological models) and computational physiology (mechanistic models). He has particular interest in statistical methods applied to population imaging and in silico clinical trials. His highly interdisciplinary work has been translated to the areas of cardiovascular, musculoskeletal and neuro sciences. He been principal investigator or scientific coordinator of over 25 national and European projects, both funded by public and private bodies totalling over £ 45M over the last 15 years.

Prof Frangi has edited several books, published 7 editorial articles and over 190 journal papers in key international journals of his research field and more than over 200 book chapters and international conference papers. He was General Chair for ISBI 2012 held in Barcelona and is the General Chair of MICCAI 2018 to be held in Granada, Spain. Prof Frangi is Chair of the Editorial Board of the MICCAI-Elsevier Book Series (2017-2020), and serves as Associate Editor of IEEE Trans on Medical Imaging, Medical Image Analysis, SIAM Journal Imaging Sciences, Computer Vision and Image Understanding journals. Prof Frangi is a recipient of the IEEE Engineering in Medicine and Biology Early Career Award in 2006. He also was awarded the UPF Medal (2011) for his service as Dean of the Escuela Politècnica Superior, and the ICREA-Academia Prize by the Institució Catalana de Recerca i Estudis Avançats (ICREA) in 2008

Under his leadership, CISTIB develop GIMIAS and MULTIX, two open-source platforms for rapidly developing pre-commercial software prototypes in the areas of image computing and image-based computational physiology modelling, and for the extraction of quantitative image analysis and modelling of large-scale imaging databases, respectively.

Dr. Zeike Taylor

CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine, The University of Sheffield.

From mechanistic to data-driven models for surgical planning, guidance and simulation
Biomechanical and biophysical models are key tools in many applications of surgical planning and optimisation, surgical guidance, and interactive simulation for training and rehearsal. The most robust and accurate models usually are those based on the relevant equations of continuum mechanics (solid, fluid, thermal, etc.), and which are generally solved with numerical methods such as FEM. Given high quality patient-specific inputs, these can enable accurate prediction of, e.g., deformations of soft tissues, flow patterns in blood vessels, energy delivery profiles around ablation devices, etc. Two main difficulties arise, however: 1) computation times can be prohibitive, especially from the point of view of clinical deployment; and 2) the requisite „high quality patient-specific inputs“ may simply not be available. To address these issues, our group and collaborators are beginning to explore how data-driven approaches can be used as surrogates for full mechanistic models, both to achieve faster computation, and to either mitigate the effects of parameter uncertainty or at least better characterise its effect. We believe that by exploiting the huge advances in machine learning and related areas experienced in recent years, entirely new classes of flexible, fast, and reliable simulation techniques can be achieved. Results achieved so far to this end will be described.


Dr Zeike Taylor is a Senior Lecturer in Mechanical Engineering at the University of Sheffield, and a member of the CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine, with expertise in computational methods for real-time surgical modelling. He has co-authored over 80 peer-reviewed journal and conference papers in areas of biomedical simulation and image computing, particularly focused on applications in surgical simulation, planning, and guidance. He is the original architect of the NiftySim simulation package, leads the Intervention Planning and Real-Time Computational Update theme within the EPSRC UK Image-Guided Therapies Network+, and is a Steering Committee member of the EPSRC-NIHR HTC Medical Image Analysis Network. He is also PI/co-I on various national and European projects.

Clinical Track

Prof. Dr. rer. nat. Christoph Bert

Uniklinikum Erlangen

Prof. Dr. med. Arnd Dörfler

Universitätsklinikum Erlangen

Prof. Dr. Robert Klopfleisch

Freie Universität Berlin