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

内容摘要

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. 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. 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. 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. 目的: 构建在极低标注率条件下的高价值区域引导双网络半监督分割方法（HVASS），针对MRI肿瘤亚区异质性及边界模糊难题，提升心脏和脑胶质瘤复杂边界分割的准确性和可靠性。方法: 提出一种双网络协同学习框架，通过预测一致性差异自动识别两类临床高风险区域:双网络预测不一致但至少一方置信度较高的“高置信歧义区”;双网络预测一致但整体置信度较低的“低置信稳定区”。针对上述区域构建自适应双教师互导机制以优化伪标签质量。同时，引入高价值区域感知卷积模块加强对肿瘤边界模糊和异质性区域的结构捕获，并结合小波频域特征精炼模块强化边缘细节。模型在BraTS2019脑肿瘤MRI数据集及ACDC心脏MRI数据集上进行验证。结果: 在ACDC数据集HVASS平均相似系数（Dice）达90.34%、95HD2.46 mm，较当前最佳方法提升Dice3.24%、95HD下降2.68 mm;在BraTS2019Dice达85.07%、95HD7.68 mm，增强肿瘤区Dice提升约3.8%，边界误差显著减小。结论: HVASS在极少标注下显著提升心脏和脑胶质瘤MRI分割精度，能够有效识别MRI肿瘤中的边界模糊与异质性区域，尤其对临床最关注的模糊边界和小病灶效果突出，可有效减轻医师标注负担，具有较高临床应用价值。.