Strict source-free domain adaptation with simple assumptions for cardiac segmentation

This paper proposes a method of source-free domain adaptation (SFDA) with a novel early stopping criterion for cardiac segmentation between CT and MRI. This approach enables stable segmentation when adapting a model trained on one modality to perform well on the other, while eliminating the need for ground-truth labels of the target domain. The proposed criterion evaluates segmentation results by aligning them with expected cardiac features, such as the heart’s near-spherical shape and distinct regions. This enables the model to stop training at an optimal point for accurate segmentation. Experiments using 20 CT and MRI volumes showed that our method achieved results comparable to partly using target domain’s ground-truth.