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Automated medical image segmentation using deep neural networks typicallyrequires substantial supervised training. However, these models fail togeneralize well across different imaging modalities. This shortcoming,amplified by the limited availability of expert annotated data, has beenhampering the deployment of such methods at a larger scale across modalities.To address these issues, we propose M-GenSeg, a new semi-supervised generativetraining strategy for cross-modality tumor segmentation on unpaired bi-modaldatasets. With the addition of known healthy images, an unsupervised objectiveencourages the model to disentangling tumors from the background, whichparallels the segmentation task. Then, by teaching the model to convert imagesacross modalities, we leverage available pixel-level annotations from thesource modality to enable segmentation in the unannotated target modality. Weevaluated the performance on a brain tumor segmentation dataset composed offour different contrast sequences from the public BraTS 2020 challenge data. Wereport consistent improvement in Dice scores over state-of-the-artdomain-adaptive baselines on the unannotated target modality. Unlike the priorart, M-GenSeg also introduces the ability to train with a partially annotatedsource modality.

M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision