Segmentation is one of the most primary tasks in deep learning for medical imaging, owing to its multiple downstream clinical applications. However, generating manual annotations for medical images is time-consuming, requires high skill, and is an expensive effort, especially for 3D images. One potential solution is to aggregate knowledge from partially annotated datasets from multiple groups to collaboratively train global models using Federated Learning. To this end, we propose SegViz, a federated learning-based framework to train a segmentation model from distributed non-i.i.d datasets with partial annotations. The performance of SegViz was compared against training individual models separately on each dataset as well as centrally aggregating all the datasets in one place and training a single model. The SegViz framework using FedBN as the aggregation strategy demonstrated excellent performance on the external BTCV set with dice scores of 0.93, 0.83, 0.55, and 0.75 for segmentation of liver, spleen, pancreas, and kidneys, respectively, significantly ($p<0.05$) better (except spleen) than the dice scores of 0.87, 0.83, 0.42, and 0.48 for the baseline models. In contrast, the central aggregation model significantly ($p<0.05$) performed poorly on the test dataset with dice scores of 0.65, 0, 0.55, and 0.68. Our results demonstrate the potential of the SegViz framework to train multi-task models from distributed datasets with partial labels. All our implementations are open-source and available at https://anonymous.4open.science/r/SegViz-B746

翻译：分割是医学影像深度学习中最基础的任务之一，因其在临床中具有多种下游应用。然而，对医学图像进行手动标注耗时、技术要求高且成本昂贵，尤其是三维图像。一种潜在解决方案是整合来自多个小组的部分标注数据集的知识，利用联邦学习协同训练全局模型。为此，我们提出SegViz，一种基于联邦学习的框架，用于从分布式的非独立同分布数据集中训练具有部分标注的分割模型。我们将SegViz的性能与在各数据集上单独训练模型以及将所有数据集中聚合到一处并训练单一模型的方法进行了比较。采用FedBN作为聚合策略的SegViz框架在外部BTCV数据集上展现出卓越性能，肝脏、脾脏、胰腺和肾脏分割的Dice得分分别为0.93、0.83、0.55和0.75，显著优于基线模型的0.87、0.83、0.42和0.48（除脾脏外，p<0.05）。相比之下，中央聚合模型在测试数据集上表现显著较差（p<0.05），Dice得分分别为0.65、0、0.55和0.68。我们的结果证明了SegViz框架在利用分布式数据集和部分标注训练多任务模型方面的潜力。所有实现均开源，可于https://anonymous.4open.science/r/SegViz-B746获取。