A Hard Convex-Shape Constraint In Dnns For Object Segmentation

Abstract

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Biological image segmentation often involves an object of interest that naturally exhibits a specific property about its geometry or shape, e.g., convexity. While typical deep-neural-networks (DNNs) for object segmentation ignore object properties relating to geometry/shape, the DNNs that employ shape information fail to enforce hard constraints on geometry/shape. We design a brand-new DNN framework that guarantees convexity of the output object-segment by leveraging fundamental geometrical insights into the boundaries of convex-shaped objects. Moreover, we design our framework to build on typical existing DNNs for per-pixel segmentation, while adding little overhead during training. Empirical evaluation on two publicly available datasets demonstrates that our framework provides significant improvements in the robust segmentation of convex objects in out-of-distribution images.