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Unsupervised domain adaptation has increasingly gained interest in medicalimage computing, aiming to tackle the performance degradation of deep neuralnetworks when being deployed to unseen data with heterogeneous characteristics.In this work, we present a novel unsupervised domain adaptation framework,named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt asegmentation network to an unlabeled target domain. Our proposed SIFA conductssynergistic alignment of domains from both image and feature perspectives. Inparticular, we simultaneously transform the appearance of images across domainsand enhance domain-invariance of the extracted features by leveragingadversarial learning in multiple aspects and with a deeply supervisedmechanism. The feature encoder is shared between both adaptive perspectives toleverage their mutual benefits via end-to-end learning. We have extensivelyevaluated our method with cardiac substructure segmentation and abdominalmulti-organ segmentation for bidirectional cross-modality adaptation betweenMRI and CT images. Experimental results on two different tasks demonstrate thatour SIFA method is effective in improving segmentation performance on unlabeledtarget images, and outperforms the state-of-the-art domain adaptationapproaches by a large margin.

Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation