题目：A Novel Framework for Segmentation of Deep Brain Structures Based on Markov Dependence Tree（一种新型的基于马尔可夫依存树的深脑结构分割框架）报告人：钟志成教授
报告摘要: We will talk about a new framework for multi-object segmentation of deep brain structures in medical brain images. Deep brain segmentation is difficult and challenging because the structures of interest are of relatively small size and have significant shape variations. The structure boundaries may be blurry or even missing, and the surrounding background is full of irrelevant edges. To tackle these problems, we propose a template-based framework to fuse the information of edge features, region statistics and inter-structure constraints for detecting and locating all target brain structures such that initialization by hand is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree, and multiple objects are efficiently matched to a target image by a top-to-down optimization strategy. The final segmentation is obtained through refinement by a B-spline based non-rigid registration between the exemplar image and the target image. Our approach needs only one example as training data.
Dr. Chung is an associate professor at the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology. Dr. Chung joined the Oxford Wolfson Medical Vision Laboratory as a doctoral student in 1998. In 2001, he was a visiting scientist at the MIT CSAI Laboratory. He won the 2002 British Machine Vision Association Sullivan Thesis Award for the best doctoral thesis submitted to a United Kingdom university in the field of computer vision or natural vision.