2018.11.06 Abstract.docx

This talk will introduce our recent deep learning work in 2018 on  brain quantification and prostate cancer radiotherapy. Specifically,  for automatic quantification of early brain development in the first  year of life, i.e., with the goal of early identification of brain  diseases such as autism, deep learning based brain image segmentation  and cortical surface parcellation have been developed. For early  diagnosis of Alzheimer’s Disease (AD) with the goal of possible early  treatment, deep learning has been applied to unsupervised brain  registration for precise inter-subject comparison and  distinctive-regions based disease diagnosis. Besides, for effective  treatment of prostate cancer, especially for MRI-based cancer  treatment, a novel context-aware GAN (Generative Adversarial Networks)  has been developed for synthesizing CT from MRI. Also, two novel deep  learning techniques have been developed for automatic and precise  segmentation of pelvic organs from the planning CT images to better  guide radiotherapy. Both the clinical significance of each medical  problem and the motivation of each developed technique will be  clarified in this talk.

Dinggang Shen is Jeffrey Houpt Distinguished Investigator, and a  Professor of Radiology, Biomedical Research Imaging Center (BRIC),  Computer Science, and Biomedical Engineering in the University of  North Carolina at Chapel Hill (UNC-CH). He is currently directing the  Center for Image Analysis and Informatics, the Image Display,  Enhancement, and Analysis (IDEA) Lab in the Department of Radiology,  and also the medical image analysis core in the BRIC. He was a  tenure-track assistant professor in the University of Pennsylvanian  (UPenn), and a faculty member in the Johns Hopkins University. Dr.  Shen’s research interests include medical image analysis, computer  vision, and pattern recognition. He has published more than 800 papers  in the international journals and conference proceedings, with H-index  84. He serves as an editorial board member for eight international  journals. He has also served in the Board of Directors, The Medical  Image Computing and Computer Assisted Intervention (MICCAI) Society,  in 2012-2015. He will be General Chair for MICCAI 2019. He is Fellow  of IEEE, Fellow of The American Institute for Medical and Biological  Engineering (AIMBE), and Fellow of The International Association for  Pattern Recognition (IAPR).