Prof Frangi is Professor of Biomedical Image Computing at the University of Sheffield (USFD), Sheffield, UK. He leads the Center for Computational Imaging & Simulation Technologies in Biomedicine (www.cistib.org). His main research interests are in medical image computing, medical imaging and image-based computational physiology.
Prof Frangi has been principal investigator or scientific coordinator of over 25 national and European projects, both funded by public and private bodies. Prof Frangi has edited a book, published 6 editorial articles and over 155 journal papers in key international journals of his research field and more than over 170 book chapters and international conference papers with an h-index 43 (Google Scholar) and over 14,200 citations. He is Associate Editor of IEEE Trans on Medical Imaging, Medical Image Analysis, SIAM Journal Imaging Sciences, Computer Vision and Image Understanding, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization and Recent Patents in Biomedical Engineering journals. Prof Frangi was recipient of the IEEE Engineering in Medicine and Biology Early Career Award in 2006, the ICT Knowledge Transfer Prize (2008) and two Teaching Excellence Prizes (2008, 2010) by the Social Council of the Universitat Pompeu Fabra. He also was awarded the UPF Medal (2011) for his service as Dean of the Escuela Politècnica Superior. He was awarded the ICREA-Academia Prize by the Institució Catalana de Recerca i Estudis Avançats (ICREA) in 2008. Prof Frangi was elected IEEE Fellow (2014), EAMBES Fellow (2015), and sits in the Board of Directors of the Medical Image Computing and Computer Assisted Interventions (MICCAI) Society (2014-2018).
Under his leadership, CISTIB develops GIMIAS (Graphical Interface for Medical Image Analysis and Simulation, www.gimias.org), an open-source platform for the rapid development of pre-commercial software prototypes in the areas of image computing and image-based computational physiology modelling.
Abstract – Current availability of and access to multifactorial and multiscale disease biomarkers opens the promise of comprehensive and personalised profiling of the health status of individuals. Ultimately, this is expected to deliver more effective diagnosis, prognosis, and treatment outcome. However, abundance of biomedical data in itself does not equate, in general, to improved patient care; it also contributes to the current information deluge and fragmented picture of health that clinicians have to deal with daily. A modern and integrative approach to decision making in healthcare requires the ability to discover quantitative disease biomarkers (e.g. genetics, biochemistry, anatomy, physiology, etc.) and to construct multifactorial decision support systems able to deal with the complexity and heterogeneity of current data sources. Computational methods, particularly those stemming from computational imaging and computational physiology are fundamental here. In turn, national and international initiatives are creating increasingly larger and richer multi-modal databases of cross-sectional and longitudinal data from where principled disease and disease progression models are to be derived by computational methods.
Computational Medicine aims at developing the framework and tools to tackle these challenges, address the unmet clinical need of such integrated investigation of the human body from modern data sources, and render practical methods and systems for personalized and predictive medicine. This lecture will focus on and illustrate two specific aspects: a) how the integration of biomedical imaging and sensing, signal and image computing, and computational physiology are essential components in addressing the challenge of a more personalized, predictive and integrative healthcare, and b) how such principles are pragmatically put at work to address specific clinical questions in the neurovascular domain. The latter, are taken from @neurIST (www.aneurist.org), an EU project led by the speaker.
Finally, this lecture will also underline the importance of model validation as a key factor in translational credibility and success; and how such validations span from technical validation of specific modelling components to clinical assessment of the proposed tools. The talk will conclude by outlining some of the areas where current research efforts fall short and where further investigation is required in the upcoming years.