Prof. Scott T. Acton of University of Virginia

The talk will focus on recent advances in image analysis that impact the study of the brain. Connections between the immune system and the brain made by way of image analysis will be highlighted. The talk will focus on segmentation of neurons, glia and meningeal lymphatics and bacteria. A graph theoretic model that incorporates elasticity via a shape manifold will be introduced. A post-processing method for machine learning-based segmentation of cells will be described. More recent applications, including a transformer model for image enhancement in the presence of signal-dependent noise and a planning method for transcranial focused ultrasound, will be presented.

Biography:

Scott T. Acton is the Lawrence R. Quarles Professor and Chair of Electrical & Computer Engineering at the University of Virginia as well as the AI Advisor to the Provost. He is also appointed in Biomedical Engineering. He was Program Director (and then acting Deputy Division Director) in the Computer and Information Sciences and Engineering directorate of the National Science Foundation where he was given the Director’s Award for Superior Accomplishment. He received the M.S. and Ph.D. degrees at the University of Texas at Austin and the B.S. degree from Virginia Tech. Professor Acton is a Fellow of the IEEE “for contributions to biomedical image analysis.” He is also Fellow of American Institute for Medical and Biological Engineering, Fellow of the Asia-Pacific Artificial Intelligence Association, and Fellow of Artificial Intelligence Industry Alliance. Professor Acton’s laboratory at UVA is called VIVA - Virginia Image and Video Analysis. They specialize in biological/biomedical image analysis problems. Professor Acton has over 400 publications in the image analysis area including the books Biomedical Image Analysis: Tracking and Biomedical Image Analysis: Segmentation. He was the 2018 Co-Chair of the IEEE International Symposium on Biomedical Imaging. Professor Acton was Editor-in-Chief of the IEEE Transactions on Image Processing (2014-2018).