Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

TL;DR

This paper introduces SIFA, a novel unsupervised domain adaptation framework that synergistically adapts both images and features to improve cross-modality medical image segmentation without target domain annotations.

Contribution

The paper proposes SIFA, a unified end-to-end model that combines image appearance transformation and feature invariance enhancement for domain adaptation in medical imaging.

Findings

SIFA significantly improves segmentation accuracy from 17.2% to 73.0%.

Outperforms existing state-of-the-art methods.

Effective in cross-modality cardiac image segmentation.

Abstract

This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Domain adaptation has become an important and hot topic in recent studies on deep learning, aiming to recover performance degradation when applying the neural networks to new testing domains. Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features towards the segmentation task. The feature encoder layers are shared by both perspectives to grasp their mutual benefits during the end-to-end learning procedure. Without using any annotation from the target domain, the learning of our unified model is guided by adversarial losses, with multiple discriminators employed from various aspects. We have extensively validated our method with a challenging application of cross-modality medical image segmentation of cardiac structures. Experimental results demonstrate that our SIFA model recovers the degraded performance from 17.2% to 73.0%, and outperforms the state-of-the-art methods by a significant margin.