SegViz: A Federated Learning Framework for Medical Image Segmentation from Distributed Datasets with Different and Incomplete Annotations

Segmentation is one of the primary tasks in the application of deep learning in medical imaging, owing to its multiple downstream clinical applications. As a result, many large-scale segmentation datasets have been curated and released for the segmentation of different anatomical structures. However, these datasets focus on the segmentation of a subset of anatomical structures in the body, therefore, training a model for each dataset would potentially result in hundreds of models and thus limit their clinical translational utility. Furthermore, many of these datasets share the same field of view but have different subsets of annotations, thus making individual dataset annotations incomplete. To that end, we developed SegViz, a federated learning framework for aggregating knowledge from distributed medical image segmentation datasets with different and incomplete annotations into a `global` meta-model. The SegViz framework was trained to build a single model capable of segmenting both liver and spleen aggregating knowledge from both these nodes by aggregating the weights after every 10 epochs. The global SegViz model was tested on an external dataset, Beyond the Cranial Vault (BTCV), comprising both liver and spleen annotations using the dice similarity (DS) metric. The baseline individual segmentation models for spleen and liver trained on their respective datasets produced a DS score of 0.834 and 0.878 on the BTCV test set. In comparison, the SegViz model produced comparable mean DS scores of 0.829 and 0.899 for the segmentation of the spleen and liver respectively. Our results demonstrate SegViz as an essential first step towards training clinically translatable multi-task segmentation models from distributed datasets with disjoint incomplete annotations with excellent performance.