Distance Transform Guided Medical Image Segmentation With Channel Information Exchange

Han Yang, Minxia Xu, Bin Cai, Pengpeng Liang, Zhiyong Sun, Bo Song, Erkang Cheng

Abstract: Medical image segmentation plays an important role in the computer-aided screening of many diseases. Different from popular deep learning-based segmentation methods that mainly focus on the whole target, recent studies pay more attention to target contour by making use of the distance transform of the object boundary. In this paper, to learn both the object content and the boundary information, we incorporate a regression task that estimates a distance map of target contour into a CNN-based segmentation network. The segmentation task and the regression task share most parts of the backbone network. Moreover, we introduce a channel information exchange module that dynamically exchanges feature maps between the segmentation and the regression tasks. The proposed information exchange module is parameter-free and controlled by the batch normalization scaling factor. In addition, the output feature maps of the two tasks are further integrated to provide fused segmentation results.We evaluate our method on three popular datasets, including the 2D ultrasound thyroid nodule segmentation dataset, the 3D MRI left atrial segmentation dataset and 2017 MM-WHS whole heart segmentation Challenge datasets. Experimental results show that our method achieves promising performance compared to the state-of-the-art approaches.