“Fast and Efficient Models for Image Registration and Anatomical Shape Analysis”
Abstract: Investigating clinical hypotheses of diseases and their potential therapeutic implications based on large medical image collections is an important research area in medical imaging. Characterization and quantification of the anatomical changes poses computational and statistical challenges due to the high-dimensional and nonlinear nature of the data. In this talk, I will present fast and efficient methods to address these problems by developing novel machine learning methods for large-scale image data sets. Applications include image registration/segmentation for computer-assisted neurosurgery and statistical shape analysis to quantify anatomical changes induced by Alzheimer’s diseases.
For inquiries contact Cathy Gezella.