Style Content Decomposition-based Data Augmentation for Domain Generalizable Medical Image Segmentation

TL;DR

This paper introduces StyCona, a style-content decomposition data augmentation method that enhances domain generalization in medical image segmentation by explicitly modeling and augmenting style and content components.

Contribution

The paper proposes a novel style-content decomposition-based augmentation method, StyCona, which improves medical image segmentation across diverse domains without altering model architecture.

Findings

StyCona significantly improves segmentation performance across multiple domains.

It outperforms existing state-of-the-art domain generalization methods.

The method is simple, effective, and does not increase training complexity.

Abstract

Due to domain shifts across diverse medical imaging modalities, learned segmentation models often suffer significant performance degradation during deployment. We posit that these domain shifts can generally be categorized into two main components: 1) "style" shifts, referring to global disparities in image properties such as illumination, contrast, and color; and 2) "content" shifts, which involve local discrepancies in anatomical structures. To address the domain shifts in medical image segmentation, we first factorize an image into style codes and content maps, explicitly modeling the "style" and "content" components. Building on this, we introduce a Style-Content decomposition-based data augmentation algorithm (StyCona), which performs augmentation on both the global style and local content of source-domain images, enabling the training of a well-generalized model for domain generalizable medical image segmentation. StyCona is a simple yet effective plug-and-play module that substantially improves model generalization without requiring additional training parameters or modifications to segmentation model architectures. Experiments on cardiac magnetic resonance imaging and fundus photography segmentation tasks, with single and multiple target domains respectively, demonstrate the effectiveness of StyCona and its superiority over state-of-the-art domain generalization methods. The code is available at https://github.com/Senyh/StyCona.