Mesh Regression Based Shape Enhancement Operator Designed for Organ Segmentation

Organ delineation is critical for diagnosis and treatment planning so as to attract a lot of attention. Recently, neural network based methods yield accurate segmentation metrics like dice coefficient. However, they have to face the problem of indistinct boundaries since segmentation is usually modeled as a pixel classification task ignoring anatomical priors. Inspired by the fact that anatomical information is an essential prior for doctors in organ segmentation, this paper proposes a mesh regression-based shape enhancement operator. This operator innovatively models the refinement of segmentation masks as a mesh vertex regression task, enabling the model to refine the segmentation contours from the perspective of segmentation targets rather than purely from a pixel perspective. The proposed operator starts from the coarse segmentation masks produced by any segmentation model. By representing mesh with the fast point feature histogram of mesh vertexes, the displacement of each vertex is predicted by a graph convolutional neural network. Once the coordinate displacements are obtained, the mesh will be evolved through vertex moving. The operator is plug-and-play, and could co-operate with any backbone segmentation model. The constructed two-stage segmentation pipeline is capable of refining organ segmentation results based on geometrical characteristics of target appearance. Validation has been performed on two public accessible datasets to delineate pancreas and liver. Results have shown that the proposed shape enhancement operator could significantly improve segmentation performance, which have also demonstrated its effectiveness and application prospects.