Segmentation in echocardiography: is the problem finally solved ?

by Olivier Bernard, Professor, University of Lyon (INSA) and CREATIS laboratory

See you then – Eric and Christian

Abstract: For several years now, deep learning techniques have been successfully applied to medical imaging in general, and echocardiography in particular. Far from being the promised panacea, these methods have allowed major advances in many specific areas such as image acquisition, echocardiogram analysis, segmentation and tracking of anatomical structures or even user guidance. Moreover, the combination of these different techniques allows the deployment of complete and fully automated processing chains, making it possible to increase the reliability of clinical measurements and facilitate the use of ultrasound scanners outside of hospitals. In my talk, I will describe our last contributions on the segmentation of echocardiographic sequences for the automatic estimation of  clinical indices. Our ultimate goal is to develop a methodology to definitively solve this 30-year-old problem. This currently includes designing appropriate databases, exploiting the VAE formalism and modeling uncertainty through the CLIP framework.

Bio:  Olivier Bernard received his Electrical Engineering degree and Ph.D. from the University of Lyon (INSA), France, in 2003 and 2006, respectively. He was a Postdoctoral Fellow with the Biomedical Imaging Group at the Federal Polytechnic Institute of Lausanne, EPFL, Switzerland in 2007. Currently, he is a Professor with the University of Lyon (INSA) and the CREATIS laboratory in France. He is also the head of the Myriad research team, which specializes in medical image analysis, simulation, and modeling. His current research interests focus on image analysis through deep learning techniques, with applications in cardiovascular imaging, blood flow imaging, and population representation. Prof. Bernard was also an Associate Editor of the IEEE Transactions on Image Processing.