Network conditioning for synergistic learning on partial annotations.

The robustness and accuracy of multi-organ segmentation networks is limited by the scarcity of labels. A common strategy to alleviate the annotation burden is to use partially labelled datasets, where each image can be annotated for a subset of all organs of interest. Unfortunately, this approach causes inconsistencies in the background class since it can now include target organs. Moreover, we consider the even more relaxed setting of region-based segmentation, where voxels can be labelled for super-regions, thus causing further inconsistencies across annotations. Here we propose CoNeMOS (Conditional Network for Multi-Organ Segmentation), a framework that leverages a label-conditioned network for synergistic learning on partially labelled region-based segmentations. Conditioning is achieved by combining convolutions with expressive Feature-wise Linear Modulation (FiLM) layers, whose parameters are controlled by an auxiliary network. In contrast to other conditioning methods, FiLM layers are stable to train and add negligible computation overhead, which enables us to condition the entire network. As a result, the network can learn where it needs to extract shared or label-specific features, instead of imposing it with the architecture (e.g., with different segmentation heads). By encouraging flexible synergies across labels, our method obtains state-of-the-art results for the segmentation of challenging low-resolution fetal MRI data. Our code is available at https://github.com/BBillot/CoNeMOS.