Objective and Impact Statement: Accurate organ segmentation is critical for many clinical applications at different clinical sites, which may have their specific application requirements that concern different organs. Introduction: However, learning high-quality, site-specific organ segmentation models is challenging as it often needs on-site curation of a large number of annotated images. Security concerns further complicate the matter. Methods: The paper aims to tackle these challenges via a two-phase aggregation-then-adaptation approach. The first phase of federated aggregation learns a single multi-organ segmentation model by leveraging the strength of 'bigger data', which are formed by (i) aggregating together datasets from multiple sites that with different organ labels to provide partial supervision, and (ii) conducting partially supervised learning without data breach. The second phase of site adaptation is to transfer the federated multi-organ segmentation model to site-specific organ segmentation models, one model per site, in order to further improve the performance of each site's organ segmentation task. Furthermore, improved marginal loss and exclusion loss functions are used to avoid 'knowledge conflict' problem in a partially supervision mechanism. Results and Conclusion: Extensive experiments on five organ segmentation datasets demonstrate the effectiveness of our multi-site approach, significantly outperforming the site-per-se learned models and achieving the performance comparable to the centrally learned models.

翻译：目标与影响声明：精准的器官分割对于不同临床场所的多种临床应用至关重要，这些场所可能针对不同器官具有特定的应用需求。引言：然而，学习高质量、场所特定的器官分割模型极具挑战性，因为这通常需要在现场人工标注大量图像，而安全顾虑进一步增加了问题的复杂性。方法：本文旨在通过一种两阶段聚合-自适应方法应对这些挑战。第一阶段为联邦聚合，通过利用"更大数据"的规模优势学习单一的多器官分割模型——该模型由以下方式构建：（i）聚合来自多个场所、标注不同器官的数据集以提供部分监督；（ii）在确保数据不泄露的前提下进行部分监督学习。第二阶段为场所自适应，将联邦多器官分割模型迁移至各场所特定的器官分割模型（每个场所一个模型），以进一步提升各场所器官分割任务的性能。此外，本文采用改进的边缘损失函数和排除损失函数，以避免部分监督机制中的"知识冲突"问题。结果与结论：在五个器官分割数据集上的大量实验表明，我们提出的多场所方法显著优于各场所独立训练的模型，且性能可与集中式学习模型相媲美。