Computational Medical Imaging
Medical imaging provides a way to probe the human body in vivo, and opens new venues for understanding and evaluating anatomy, its development, disease onset and progression, extracting and modeling of organs and anatomical structures. To exemplify our studies in mathematical modeling of medical images, I will present our recent work on two important problems, first of which is in modeling of coronary arterial surfaces, which provides a way to study atherosclerosis disease that eventually leads to heart attacks. I will present our vessel tractography idea to 3D modeling of coronary arteries and bifurcations from Computed Tomography Angiography. The second problem I will talk about is on modeling tumor surfaces and changes in brain tumor MR images, which are important for radio-therapy planning and follow-up assessment of the cancer disease.
I will present our ideas in estimation of tumor surfaces and deformation in brain images, and how we utilize those to compute tumor response measures.
Short biography of the speaker:
Dr. Gozde Unal received her BSc degree from METU EE in 1996, her MSc from Bilkent University EE in 1998, and her PhD in ECE with a minor in mathematics from North Carolina State University, NC, USA, in 2002. Later, she was a postdoctoral fellow at Georgia Institute of Technology for one year. She worked as a research scientist at Siemens Corporate Research, Princeton, NJ, USA between August 2003-2007. She then joined Sabancı University, Faculty of Engineering and Natural Sciences as a faculty member, where she is currently an associate professor. Her research interests include mathematical and computational problems in medical image analysis and computer vision. Particularly, her work is focused on mathematical solutions to segmentation and registration problems applied to clinical imaging with various modalities (MRI, diffusion MRI, and CT) in clinical problems of neuroimaging and cardiovascular imaging.