[A semi-supervised MRI image segmentation dual-network model for regions with ambiguous boundaries and heterogeneous regions].

Abstract

Objectives: To construct a high-value region-guided dual-network semi-supervised segmentation method (HVASS) under extremely low labeling rates for addressing the challenges of heterogeneity and blurred boundaries in MRI tumor subregions and improving the accuracy and reliability of complex boundary segmentation for the heart and glioma.

Methods: HVASS employs a dual-network collaborative learning architecture that leverages prediction inconsistency to automatically identify two clinically significant high-risk regions: the high-confidence ambiguity zones, where predictions between the two networks diverge despite at least one network exhibiting high confidence; and the low-confidence stable zones, where predictions agree but with consistently low confidence across both networks. To refine pseudo-label quality, an adaptive dual-teacher mutual distillation mechanism was introduced for dynamically leveraging complementary knowledge from both networks. A high-value region-aware convolutional module was integrated to strengthen feature representation at the tumor margins and the heterogeneous areas, while a wavelet-based frequency-domain refinement module was incorporated to preserve the fine-grained edge details. The framework was evaluated on two publicly available datasets: BraTS2019 for brain glioma MRI and ACDC for cardiac MRI.

Results: On the ACDC dataset, HVASS achieved a mean Dice score of 90.34% and a 95th percentile Hausdorff Distance (95HD) of 2.46 mm, representing a 3.24% improvement in Dice and a 2.68 mm reduction in 95HD compared to the state-of-the-art model. On BraTS2019, the model attained a Dice score of 85.07% and a 95HD of 7.68 mm, with a 3.8% increase in Dice for enhancing tumor sub-region and a substantial reduction of boundary localization error.

Conclusions: HVASS demonstrates superior segmentation performance under minimal annotation settings and allows effective capture of fuzzy boundaries and heterogeneous tumor regions in MRI. The method shows particular strength in segmenting small lesions and ill-defined edges and thus lessens the annotation burden of the radiologists.