Abstract: Medical image segmentation plays an important role in disease diagnosis, treatment planning, and surgical navigation. Traditional methods rely on large amounts of annotated data, which cannot meet privacy protection requirements. Source-free domain adaptation (SFDA) becomes a research focus because it does not require source data and fits privacy constraints. However, SFDA still faces some challenges, such as lack of spatial consistency, insufficient multi-scale feature representation, and significant pseudo-label noise. To address these issues, we propose a source-free domain adaptation segmentation network based on Multi-branch Collaborative Calibration and Reliable Weighted Consistency (MCC-RWC). MCC-RWC includes two key advantages. First, we design a multi-branch collaborative calibration module, which utilizes multi-decoder prediction and inverse transform calibration to generate high-quality spatially consistent probability predictions, and captures detailed anatomical structures and long-range feature dependencies through an embedded hierarchical feature aggregation module to enhance multi-scale feature representations. Second, we design a reliable weighted consistency module to generate high-quality pseudo labels through three rounds of differential forward propagation and confidence selection, and to suppress noise using weighted loss and consistency constraints to enhance the robustness of the model. Experiments on multi-center cardiac MRI and polyp datasets demonstrate that MCC-RWC outperforms some existing popular methods. MCC-RWC provides an efficient and privacy-preserving solution for cross-center clinical segmentation tasks.

摘要： ：医学图像分割在疾病诊断、治疗规划与手术导航中发挥着关键作用。但传统方法多依赖大量标注数据，难以满足医疗隐私保护需求。无源域适应因无需源域原始数据、契合医疗隐私保护需求成为研究焦点，但仍面临空间一致性缺失、多尺度特征表征不足及伪标签噪声显著等问题。针对以上问题，提出了一种基于多分支协同校准与可靠加权一致性的无源域适应分割网络（MCC-RWC）。MCC-RWC具有两个优势：首先，设计了一个多分支协同校准模块，利用多解码器预测与逆变换校准生成高质量且空间一致的概率预测，并通过内嵌层级特征聚合模块捕捉细节解剖结构和长距离特征依赖，以增强多尺度特征的表达能力。其次，设计了一个可靠加权一致性模块，通过三次差异化的前向传播与置信度筛选生成高质量伪标签，并利用加权损失与一致性约束抑制噪声，来提升模型的鲁棒性。在多中心心脏MRI和息肉分割数据集上的实验表明，MCC-RWC的性能显著优于现有方法，为临床跨中心分割任务提供了高效且隐私安全的解决方案。