Information on the datasets used in this study.

In unsupervised transfer learning for medical image segmentation, where existing algorithms face the challenge of error propagation due to inaccessible source domain data. In response to this scenario, source-free domain transfer algorithm with reduced style sensitivity (SFDT-RSS) is designed. SFDT-RSS initially pre-trains the source domain model by using the generalization strategy and subsequently adapts the pre-trained model to target domain without accessing source data. Then, SFDT-RSS conducts interpatch style transfer (ISS) strategy, based on self-training with Transformer architecture, to minimize the pre-trained model’s style sensitivity, enhancing its generalization capability and reducing reliance on a single image style. Simultaneously, the global perception ability of the Transformer architecture enhances semantic representation to improve style generalization effectiveness. In the domain transfer phase, the proposed algorithm utilizes a model-agnostic adaptive confidence regulation (ACR) loss to adjust the source model. Experimental results on five publicly available datasets for unsupervised cross-domain organ segmentation demonstrate that compared to existing algorithms, SFDT-RSS achieves segmentation accuracy improvements of 2.83%, 2.64%, 3.21%, 3.01%, and 3.32% respectively.

在医学图像分割的无监督迁移学习任务中，现有算法因源域数据不可访问而面临误差传播挑战。针对这一研究场景，本研究设计了一种降低风格敏感性的无源域迁移算法（SFDT-RSS）。SFDT-RSS首先利用泛化策略对源域模型进行预训练，随后在无需访问源域数据的前提下，将预训练模型适配至目标域。随后，该算法基于Transformer架构的自训练框架，采用块间风格迁移（ISS）策略以降低预训练模型的风格敏感性，增强其泛化能力并减少对单一图像风格的依赖。同时，Transformer架构的全局感知能力可强化语义表征，提升风格泛化效果。在域迁移阶段，所提算法采用模型无关自适应置信度正则（ACR）损失对源模型进行调整。在5个用于无监督跨域器官分割的公开数据集上开展的实验结果表明，相较于现有算法，SFDT-RSS的分割精度分别提升2.83%、2.64%、3.21%、3.01%及3.32%。