Moti Freiman (CS, Hebrew University of Jerusalm)
Segmentation of organs and vascular structures from clinical Computed Tomography (CT) images is a crucial task in many clinical applications including diagnosis, patient specific training simulations, and intra-operative navigation. The segmentation is a challenging task due to the unclear distinction between the required structure and its surrounding tissue, artifacts in the CT images, and the presence of pathologies.
We present a shape constrained graph min-cut approach for the segmentation. Discrete energy functions are defined with respect to fixed or latent shape models and optimized using the graph min-cut algorithm to obtain an accurate segmentation. Extensive evaluation of our approach for different tasks, including carotid artery bifurcation segmentation, patient specific modeling of the entire carotid arteries system for simulation, abdominal aortic aneurysms segmentation, and kidney and liver segmentation shows that our method is accurate, robust, and practical for clinical use.