Medical Image Segmentation with Auxiliary Points Prediction of Lesion Location and Boundary

Abstract

In order to obtain good medical image segmentation results, existing studies usually extract multi-scale features or design special attention to obtain global and local information of images. However, the above methods are very cumbersome or have a high computational burden. Theoretically, global and local contexts are used to locate objects and refine their contours respectively. Therefore, from a novel points prediction perspective, this work adopts a convolutional model and employs the deep supervision method to assist segmentation by predicting points of the lesion location and boundary. Specifically, we use two points prediction branches to generate location and boundary heatmaps, which implicitly locate lesions and refine contours. Furthermore, in order to train the multi-task model effectively, this work proposes a simple supervision signals design principle to guide deep supervision, so that the training of the auxiliary branch does not conflict with the main task branch. It is worth noting that our simple and end-to-end approach achieves the state-of-the-art results without the need for post-processing. Extensive experiments on three challenging medical image segmentation benchmarks demonstrate the superior performance.