Boosting Semi-Supervised Medical Image Segmentation Through Inter-Instance Information Complementarity.

Abstract

The acquisition of expert-annotated data remains a critical bottleneck for medical image segmentation, thereby constraining the clinical applicability of highly accurate models. Crucially, despite this scarcity of labeled data, the intrinsic homogeneity in human anatomy across the cohort provides a fundamental basis (or: a promising leverage point) for enhancing model generalization and training efficiency by exploiting inter-instance anatomical complementarity. In this study, we propose a novel semi-supervised approach for medical image segmentation that fully exploits this inter-instance complementarity. The proposed model operates at two levels, integrating a sophisticated copy-paste augmentation module (CPAM) and a trainable region calibration mechanism (TRCM) within the simple mean teacher (MT) framework. Specifically, CPAM is a carefully designed copy-paste strategy that facilitates the exchange of informative regions between samples, thereby enhancing the diversity and robustness of the training data. TRCM leverages the predictions from labeled regions to guide and calibrate the trainable regions in unlabeled data. The calibrated regions typically yield high-quality pseudo-labels, which effectively improve model training. CPAM and TRCM work synergistically, complementing each other to enhance model performance. Experiments on diverse medical image datasets-including LA, ACDC, BraTS2019, and Pancreas-NIH-covering both MRI and CT modalities demonstrate the robust efficacy of our proposed model. In settings with limited annotated data, the model consistently outperforms current state-of-the-art methods across multiple evaluation metrics. The code is available at https://github.com/shuaiaihang/shuaiAIMedcalLab.