STRUCTURAL PRIOR MODELS FOR 3-D DEEP VESSEL SEGMENTATION

We address the problem of 3-D blood vessel segmentation with a deep learning method that incorporates domain information via priors and regularizers on vessel structure and morphology. Inspired by the observation that 3-D vessel structures project onto 2-D image slices with distinctive edges that can aid 3-D vessel segmentation, we propose a novel multi-task learning architecture comprising a shared encoder and two decoders that respectively predict vessel segmentation maps and edge profiles. 3-D features from the two branches are concatenated to facilitate edge-guidance when learning segmentation maps. We introduce new regularization terms that encourage local homogeneity of 3-D blood vessel volumes brought about by biomarkers, as well as sparsity of edge pixels. Experiments on benchmark datasets demonstrate superior performance of our method over the state-of-the-art, especially when training data is limited.