Crossover-Net: Leveraging vertical-horizontal crossover relation for robust medical image segmentation

Abstract

Abstract Accurate boundary segmentation in medical images is significant yet challenging due to large variation of shape, size and appearance within intra- and inter- samples. In this paper, we present a novel deep model termed as Crossover-Net for robust segmentation in medical images. The proposed model is inspired by an interesting observation – the features learned from horizontal and vertical directions can provide informative and complement contextual information to enhance discriminative ability between different tissues. Specifically, we first originally propose a cross-shaped patch, namely crossover-patch which consists of a pair of (orthogonal and overlapping) vertical and horizontal patches. Then, we develop our Crossover-Net to learn the vertical and horizontal crossover relation according to the proposed crossover-patches. To train our model end-to-end, we design a novel loss function to (1) impose the consistency on overlapping region of vertical and horizontal patches and (2) preserve the diversity on their non-overlapping regions. We have extensively evaluated our method on CT kidney tumor, MR cardiac, and X-ray breast mass segmentation tasks, showing promising results compared with the current state-of-the-art methods. The code is available at https://github.com/Qianyu1226/Crossover-Net .