AnaCoMT+: Anatomy-Aware Symmetric Contrast and Cross-Modal Transfer for Brain Tumor Segmentation

Considering the clinical significance of brain tumor segmentation and the challenge of annotation scarcity, a self-supervised learning framework combining anatomical symmetry and cross-modal feature transfer (AnaCoMT+) is proposed. AnaCoMT+ innovatively integrates masked autoencoder and contrastive learning, which can extract biological structural features from annotated MRI images through a decoupled training strategy. AnaCoMT+ consists of a representation learning-based encoder, a self-supervised reconstruction head, a self-supervised projection head and a supervised segmentation head. The reconstruction head is employed to conduct cross-modal image reconstruction, thereby extracting the coordinated representations of multimodal images. The projection head is responsible for performing comparative learning of healthy brain regions and tumor regions, thereby enabling the model to focus on the feature expression of lesion areas. The novel design effectively resolves key limitations in current methods: (1) misalignment between pre-training targets and segmentation objectives, and (2) insufficient feature representation in semi-/self-supervised paradigms. Besides, in the construction of AnaCoMT+, in order to integrate and utilize multi-scale features, a multi-scale attention block (MSAB) and a hybrid attention block (HAB) are proposed, further improving the accuracy of brain tumor segmentation. Experimental results on the Brats 2019 dataset show that AnaCoMT+ can obtain an average Dice coefficient of 0.84, which is better than the existing methods.