Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation (1901.08211v4)

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.

Overview of Synergistic Image and Feature Adaptation for Medical Image Segmentation

The paper "Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation" proposes a novel framework termed Synergistic Image and Feature Adaptation (SIFA). This framework specifically addresses the domain adaptation challenge in medical image segmentation, focusing on the cross-modality domain shift, exemplified by training on Magnetic Resonance (MR) images and testing on Computed Tomography (CT) images.

The SIFA framework advances unsupervised domain adaptation by integrating both image and feature-level adaptations within a unified model. This dual approach operates under an end-to-end learning scheme where image adaptation aligns the appearance of images from different domains through generative adversarial networks (GANs), while feature adaptation seeks to enhance domain-invariance by using adversarial losses calculated through discriminators in two compact spaces: the semantic prediction space and the generated image space.

Strong Numerical Results

The experimental validation, conducted on a cardiac segmentation task using the Multi-Modality Whole Heart Segmentation Challenge 2017 dataset, reflects the model's efficacy. The baseline performance on CT images without adaptation is reported at a Dice score of 17.2%. The SIFA model increases this performance metric significantly to a Dice score of 73.0%, surpassing existing state-of-the-art methods by a considerable margin. These results underscore SIFA's capability to recover model performance effectively in the context of severe domain shifts.

Theoretical and Practical Implications

The SIFA framework holds potential implications both theoretically and practically. Theoretically, the integration of synergistic adaptations exemplifies leveraging complementary adaptation strategies to mitigate domain shift, offering a pathway to enhance the robustness and generalization potential of deep learning models in heterogeneous data environments. Practically, the methodology is poised to reduce the annotation burden in medical imaging, providing a feasible option for employing existing annotated data to segment images from differing modalities without necessitating additional ground truth, thereby reducing dependency on expert data labeling.

Speculation on Future Developments

Moving forward, such advancements may drive developments in other complex domains where training and testing data come from divergent sources, such as environmental monitoring and remote sensing. It also poses a call for further exploration into more nuanced adaptation techniques that can synergize different adaptation models and leverage diverse data characteristics, potentially enhancing model transferability across varied and unstructured data landscapes.

In conclusion, the introduction of SIFA presents a robust framework addressing critical limitations in cross-modality domain adaptation in medical imaging, and it posits a strategic direction for leveraging the dual foci of image and feature adaptation together. As AI systems continue to integrate into critical domains, frameworks like SIFA carry the promise of more adaptive and reliable applications.