Sila Kurugol is a Research Fellow in Radiology in the Computational Radiology Laboratory (CRL) at Boston Children's Hospital. She is also a Research Fellow at Harvard Medical School since 2011. Before joining CRL, she was a member of Laboratory of Mathematics in Imaging and Applied Chest Imaging Lab at Brigham and Women's Hospital between 2011 and 2014. She received her Ph.D. degree in Electrical and Computer Engineering in 2011 from Northeastern University in Boston, and received her MS degree from Bilkent University and her B.S. degree from Middle East Technical University both in Electrical Engineering.
Sila's previous research focused on development of novel machine learning and model fitting algorithms to solve challenging biomedical image analysis problems. She worked on development of model based segmentation of thoracic structures including aorta and esophagus. She developed a locally deformable shape model integrated into active shape models framework to model tubular esophagus shape. She also developed a hybrid sequence segmentation and spatially smooth SVM classification technique for segmentation and detection of skin layers in 3D confocal reflectance images of skin tissue for early detection of skin cancer at the layer of dermal epidermal junction. She worked on projects to automatically extract imaging phenotypes of Chronic Obstructive Pulmonary Disease and cardiovascular disease in large cohorts of smokers. She developed a novel ranking based emphysema classification technique for quantifying emphysema type and progression. She also developed an automated approach for aorta segmentation, quantification of mural calcification and morphology of aorta. These imaging phenotypes have been shown to be correlated with clinical disease biomarkers and have potential to be a predictor of cardiovascular events.
Sila's current research in the CRL includes development of probabilistic model-fitting algorithms for quantitative body Diffusion-Weighted MR Imaging of inflammatory activity in Crohn's disease with the purpose of improvement of diagnosis and follow up of this chronic disease.