Semi-Supervised Medical Image Semantic Segmentation With Multi-Scale Graph Cut Loss

Most semantic segmentation methods are based on supervised convolutional neural networks which require large amounts of labeled data. However, the acquisition of a large number of high-quality labels is time-consuming and of high annotation cost for medical images. In this paper, we propose a semi-supervised learning framework based on a novel multi-scale graph cut loss function. Firstly, the multi-scale features obtained from the segmentation network are utilized to construct the graph in non-Euclidean space. Then the long-distance information between voxels at different scales can be captured through the graph embedding module. After that, the graph cut loss is calculated according to the final latent features. Only a few labeled data is needed in our proposed method, which is of significance in the practical clinic. The experiments on the BrainWeb20 dataset and the IBSR18 dataset demonstrate the effectiveness of the proposed method compared to the well-known state-of-the-art methods.