Personalized federated learning (PFL) for surgical instrument segmentation (SIS) is a promising approach. It enables multiple clinical sites to collaboratively train a series of models in privacy, with each model tailored to the individual distribution of each site. Existing PFL methods rarely consider the personalization of multi-headed self-attention, and do not account for appearance diversity and instrument shape similarity, both inherent in surgical scenes. We thus propose PFedSIS, a novel PFL method with visual trait priors for SIS, incorporating global-personalized disentanglement (GPD), appearance-regulation personalized enhancement (APE), and shape-similarity global enhancement (SGE), to boost SIS performance in each site. GPD represents the first attempt at head-wise assignment for multi-headed self-attention personalization. To preserve the unique appearance representation of each site and gradually leverage the inter-site difference, APE introduces appearance regulation and provides customized layer-wise aggregation solutions via hypernetworks for each site's personalized parameters. The mutual shape information of instruments is maintained and shared via SGE, which enhances the cross-style shape consistency on the image level and computes the shape-similarity contribution of each site on the prediction level for updating the global parameters. PFedSIS outperforms state-of-the-art methods with +1.51% Dice, +2.11% IoU, -2.79 ASSD, -15.55 HD95 performance gains. The corresponding code and models will be released at https://github.com/wzjialang/PFedSIS.

翻译：面向手术器械分割（SIS）的个性化联邦学习（PFL）是一种前景广阔的方法。它允许多个临床站点在保护隐私的前提下协作训练一系列模型，每个模型均可适配各站点的个体数据分布。现有PFL方法鲜少考虑多头自注意力机制的个性化设计，亦未充分考虑手术场景中固有的外观多样性与器械形状相似性。为此，我们提出PFedSIS——一种融合视觉特征先验的新型SIS个性化联邦学习方法，通过全局-个性化解耦（GPD）、外观调节个性化增强（APE）及形状相似性全局增强（SGE）三大模块，全面提升各站点的SIS性能。GPD首次实现了多头自注意力机制在注意力头维度的个性化分配。为保持各站点独特的外观表征并渐进式利用站点间差异，APE引入外观调节机制，并通过超网络为各站点的个性化参数提供定制化的分层聚合方案。SGE模块则维护并共享器械的共有形状信息，在图像层面增强跨风格形状一致性，并在预测层面计算各站点对形状相似性的贡献度以更新全局参数。实验表明，PFedSIS以Dice系数提升1.51%、交并比提升2.11%、平均对称表面距离降低2.79、95%豪斯多夫距离降低15.55的显著优势超越现有最优方法。相关代码与模型将在https://github.com/wzjialang/PFedSIS发布。