Medical Image Segmentation Based on Semi-Supervised Multi-Scale Consistency Learning

Abstract

Deep supervised learning has made remarkable achievements in medical image segmentation. However, it is heavily dependent on a large amount of high-quality, labeled medical image data, which are difficult to obtain. To address this issue, this paper proposes a Semi-Supervised Multi-scale Consistency Network (SSMC-Net) for medical image lesion segmentation. The network architecture of SSMC-Net is built upon a joint training framework, learning from both labeled and unlabeled data. Moreover, to alleviate the loss of details during the down-sampling and up-sampling processes, a Multi-scale Subtraction (MS) module is incorporated to capture a broader spectrum of differential features, including the Subtraction Unit (SU) and Multiple Feature Fusion Unit (MFFU). The SU is responsible for extracting differential information from the multi-scale encoding outputs, and the MFFU selectively merges the most correlated features to provide more precise interactive representations for the decoder. Finally, the loss function is redesigned. The supervised part comprehensively calculates the pixel-level information outputs at various resolutions, whereas the unsupervised part introduces a multi-scale joint consistency loss and designs a distance function to diminish the impact of unreliable samples. Ablation and comparative experiments on the CPD, ATLAS, and ACDC datasets demonstrate that the proposed method achieves superior performance in terms of the Dice Similarity Coefficient (DSC) and F2 value compared to existing semi-supervised segmentation methods, even with only 50% labeled data.