Semi-supervised Medical Image Segmentation with Distribution Calibration and Non-local Semantic Constraint

The available medical images with accurate segmentation masks are usually limited due to the expensive and time-consuming annotation cost. Many semi-supervised approaches tried to exploit a large amount of unlabeled data together with the small number of labeled data. However, their learned segmentation models can easily become overfitted on the biased labeled data, mainly because of the misalignment between the labeled and unlabeled data distribution. To address this challenging problem, we propose a novel semi-supervised model with distribution calibration and non-local semantic constraint for medical image segmentation. In specific, we explicitly calibrate the learned feature distributions of the labeled and unlabeled data to make them aligned. Meanwhile, we add a special nonlocal semantic loss to encourage the learned features to be more discriminative for the segmentation task at the same time. Consequently, our final segmentation networks have the advantage to better generalize on the unlabeled data in both training and test set. Experimental results on three popular medical image segmentation benchmarks demonstrate that our proposed model achieves superior performance over other state-of-the-art methods. We will release our source code in this URL.