Semi-automatic segmentation of major aorto-pulmonary collateral arteries (MAPCAs) for image guided procedures

Abstract

Manual segmentation of pre-operative volumetric dataset is generally time consuming and results are subject to large inter-user variabilities. Level-set methods have been proposed to improve segmentation consistency by finding interactively the segmentation boundaries with respect to some priors. However, in thin and elongated structures, such as major aorto-pulmonary collateral arteries (MAPCAs), edge-based level set methods might be subject to flooding whereas region-based level set methods may not be selective enough. The main contribution of this work is to propose a novel expert-guided technique for the segmentation of the aorta and of the attached MAPCAs that is resilient to flooding while keeping the localization properties of an edge-based level set method. In practice, a two stages approach is used. First, the aorta is delineated by using manually inserted seed points at key locations and an automatic segmentation algorithm. The latter includes an intensity likelihood term that prevents leakage of the contour in regions of weak image gradients. Second, the origins of the MAPCAs are identified by using another set of seed points, then the MAPCAs' segmentation boundaries are evolved while being constrained by the aorta segmentation. This prevents the aorta to interfere with the segmentation of the MAPCAs. Our preliminary results are promising and constitute an indication that an accurate segmentation of the aorta and MAPCAs can be obtained with reasonable amount of effort.