Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed Tomography Images

arXiv:2307.03137v2 Announce Type: replace-cross
Abstract: Segmentation networks are not explicitly imposed to learn global invariants of an image, such as the shape of an object and the geometry between multiple objects, when they are trained with a standard loss function. On the other hand, incorporating such invariants into network training may help improve performance for various segmentation tasks when they are the intrinsic characteristics of the objects to be segmented. One example is segmentation of aorta and great vessels in …

abstract arxiv cs.cv cs.lg eess.iv function geometry global image images learn loss multiple networks objects segmentation standard topology type