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

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.