Adaptive consistency representation learning for semi-supervised 3D medical image segmentation.
Authors
Affiliations (4)
Affiliations (4)
- School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China.
- School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China; Tianjin Linghang Technology Development Co., Ltd, 295 Shengli Street, Dagang, Binhai New Area, Tianjin, 300270, China. Electronic address: [email protected].
- Tianjin Linghang Technology Development Co., Ltd, 295 Shengli Street, Dagang, Binhai New Area, Tianjin, 300270, China.
- Tianjin Medical University Cancer Institute & Hospital, Huanhu West Road, Hexi District, Tianjin, 300000, China.
Abstract
Three-dimensional medical image segmentation plays a critical role in clinical diagnosis and surgical planning. Semi-supervised learning provides an effective way to reduce annotation requirements; however, heterogeneous feature perturbations may induce predictions with different levels of local disagreement, making uniformly weighted consistency supervision sub-optimal. This work presents an Adaptive Consistency Representation Learning (ACRL) framework for semi-supervised 3D medical image segmentation. ACRL constructs heterogeneous feature-perturbed auxiliary views and adaptively modulates the voxel-wise consistency strength according to the disagreement between each auxiliary prediction and the feature-unperturbed main prediction. A Multi-scale Feature Aggregation module and a Bottleneck Attention Module are incorporated to enhance structural representation. Experiments on the LA, Pancreas-CT, and BraTS19 datasets achieve Dice scores of 90.54%, 81.85%, and 87.51%, respectively, under the 10% labeled setting. Across the evaluated settings, ACRL shows competitive overlap accuracy and favorable boundary-distance performance relative to representative conventional and foundation-model-assisted semi-supervised baselines. Controlled ablations further show that reliability-adaptive weighting provides additional benefits over uniformly weighted multi-branch consistency.