1:00 pm to 2:00 pm, 17 October 2018
UCL Bloomsbury - Sir David Davies LT G08 Roberts building
Title: Deep Learning for Medical Image Registration - The story so far
Recent medical image registration methods based on deep neural networks have seemly converged to the so-called end-to-end learning approaches, in which the networks are trained to directly predict displacements between a given pair of images from the unprocessed image data. Besides fast inference that enables sub-second volumetric registration, some have shown superior robustness over classical methods. In particular, I will describe a new weakly-supervised formulation that we proposed to learn voxel correspondence, arguably the overarching goal of image registration. These registration networks can be trained with practical-to-obtain anatomical labels without being driven by intensity-based similarity measures, therefore they are also suitable for multimodality registration tasks. In this seminar, I will also give an overview of recent literature in image registration using deep-learning methods. I will then conclude with a discussion on the challenges and opportunities in this technical area.