Physics Department Seminar
Dr. Eranga Ukwatta
Assistant Professor
Carleton University, Systems & Computing Engineering
Tuesday, March 27, 2018

Image Analysis for Medical Imaging: Towards Translating Personalized Biomarkers into Clinical Care

 With the recent developments in medical imaging devices capable of acquiring high-resolution, multi-dimensional (i.e., 3D + time) images of the human body, automated image analysis methods are becoming increasingly essential for extracting previously inaccessible quantitative biomarkers from medical images. Parallel to this development, recent advancements in machine learning methods have availed a wealth of novel research opportunities in knowledge discovery and analysis of large medical databases. In this talk, I will describe development of novel image analysis methodologies for cardiovascular imaging and histopathological imaging of large intestines and placenta. In particular, I will present novel image segmentation algorithms based on convex max-flow formulations and deep learning methods that were developed for patient-specific analysis and modeling of cardiovascular structure and function. I will also describe image processing pipeline that we developed for building personalized computational models of the heart for simulation of cardiac electrophysiology. These virtual models can be non-invasively interrogated to gain mechanistic insights into electrical activity of the heart, and has potential to be utilized in the clinic for numerous applications, such as cardiac risk stratification and prediction of target locations for cardiac ablations.