Regional Consistency for Semi-Supervised Segmentation of 3D Medical Images

Semi-supervised medical image segmentation (SSMIS) is a research hotspot. However, existing consistency regularization methods do not adequately consider the robustness gains obtained by the model overcoming perturbations in the network structure and the spatial context. To address this problem, we propose a regional consistency strategy for SSMIS. Specifically, we construct network structure perturbations by making the two networks use different downsampling strategies. As for spatial contextual perturbations, we perform two random crops for each 3D medical image and feed different sub-image to different networks. We introduce entropy minimization to encourage both networks to produce consistent, high-confidence predictions for intersecting regions. A weighted combination of supervised and unsupervised losses optimizes the networks. We conducted extensive experiments on two datasets, and the results show that introducing network structure perturbations and spatial environmental perturbations can improve various metrics and demonstrate the effectiveness of our method.