VMCC-NET: Uncovering Challenging Regions in Semi-Supervised Medical Image Segmentation with Voxel Mask Based Cyclic-Consistency Network

Semi-supervised learning has been widely used to train models by utilizing a small amount of labeled data and a large amount of unlabeled data, especially in medical image analysis. However, existing methods still do not fully reveal the complex regions (e.g., small branches or fuzzy edges). These complex regions contain more critical information, and by focusing more on complex region information, the predictions of model can be more comprehensive. To address the above problems, we propose a new voxel mask-based cyclic consistency network for semi-supervised medical image segmentation. First, our model consists of two identical encoders and three different decoders. Learning from the masked image, the two encoders enhance perceptual analysis and learning of details while retaining high-level semantic knowledge. Second, a sharpening function transforms the probabilistic results into soft pseudo labels and the cyclic consistency regularization training strategy achieves a consistency constraint between the probabilistic outputs of the different decoders and the soft pseudo labels. We evaluate our network on two public 3D medical datasets. Experiments demonstrate the superior performance of our proposed model over other methods. Our code is available here.