Learning Geodesic Active Contours for Embedding Object Global Information in Segmentation CNNs

Abstract

Most existing CNNs-based segmentation methods rely on local appearances learned on the regular image grid, without consideration of the object global information. This article aims to embed the object global geometric information into a learning framework via the classical geodesic active contours (GAC). We propose a level set function (LSF) regression network, supervised by the segmentation ground truth, LSF ground truth and geodesic active contours, to not only generate the segmentation probabilistic map but also directly minimize the GAC energy functional in an end-to-end manner. With the help of geodesic active contours, the segmentation contour, embedded in the level set function, can be globally driven towards the image boundary to obtain lower energy, and the geodesic constraint can lead the segmentation result to have fewer outliers. Extensive experiments on four public datasets show that (1) compared with state-of-the-art (SOTA) learning active contour methods, our method can achieve significantly better performance; (2) compared with recent SOTA methods that are designed for reducing boundary errors, our method also outperforms them with more accurate boundaries; (3) compared with SOTA methods on two popular multi-class segmentation challenge datasets, our method can still obtain superior or competitive results in both organ and tumor segmentation tasks. Our study demonstrates that introducing global information by GAC can significantly improve segmentation performance, especially on reducing the boundary errors and outliers, which is very useful in applications such as organ transplantation surgical planning and multi-modality image registration where boundary errors can be very harmful.

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