Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation

TL;DR

This paper introduces a structure-aware stylization method combining diffusion models and a structure-preserving network to improve the robustness and accuracy of medical image segmentation across diverse domains, especially when target domain data is limited.

Contribution

It presents a novel approach that transforms images into a consistent style while preserving structural details, addressing domain shifts without requiring target domain data during training.

Findings

Enhanced segmentation robustness across different domains.

Superior performance metrics compared to baseline models.

Effective preservation of lesion structure during style transfer.

Abstract

Accurate medical image segmentation is essential for effective diagnosis and treatment planning but is often challenged by domain shifts caused by variations in imaging devices, acquisition conditions, and patient-specific attributes. Traditional domain generalization methods typically require inclusion of parts of the test domain within the training set, which is not always feasible in clinical settings with limited diverse data. Additionally, although diffusion models have demonstrated strong capabilities in image generation and style transfer, they often fail to preserve the critical structural information necessary for precise medical analysis. To address these issues, we propose a novel medical image segmentation method that combines diffusion models and Structure-Preserving Network for structure-aware one-shot image stylization. Our approach effectively mitigates domain shifts by transforming images from various sources into a consistent style while maintaining the location, size, and shape of lesions. This ensures robust and accurate segmentation even when the target domain is absent from the training data. Experimental evaluations on colonoscopy polyp segmentation and skin lesion segmentation datasets show that our method enhances the robustness and accuracy of segmentation models, achieving superior performance metrics compared to baseline models without style transfer. This structure-aware stylization framework offers a practical solution for improving medical image segmentation across diverse domains, facilitating more reliable clinical diagnoses.