Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation

TL;DR

This paper introduces a novel source-free test-time domain adaptation method for medical image segmentation that enhances model robustness by enforcing local boundary and global feature consistency, leading to improved segmentation accuracy.

Contribution

It proposes a multi-task consistency guided approach that ensures local boundary and global prototype consistency without source data, advancing domain adaptation techniques in medical imaging.

Findings

Improved Dice score by 6.27% on RIM-ONE-r3 dataset.

Achieved better performance than existing domain adaptation methods.

Enhanced intra-class compactness through global feature consistency.

Abstract

Source-free test-time adaptation for medical image segmentation aims to enhance the adaptability of segmentation models to diverse and previously unseen test sets of the target domain, which contributes to the generalizability and robustness of medical image segmentation models without access to the source domain. Ensuring consistency between target edges and paired inputs is crucial for test-time adaptation. To improve the performance of test-time domain adaptation, we propose a multi task consistency guided source-free test-time domain adaptation medical image segmentation method which ensures the consistency of the local boundary predictions and the global prototype representation. Specifically, we introduce a local boundary consistency constraint method that explores the relationship between tissue region segmentation and tissue boundary localization tasks. Additionally, we propose a global feature consistency constraint toto enhance the intra-class compactness. We conduct extensive experiments on the segmentation of benchmark fundus images. Compared to prediction directly by the source domain model, the segmentation Dice score is improved by 6.27\% and 0.96\% in RIM-ONE-r3 and Drishti GS datasets, respectively. Additionally, the results of experiments demonstrate that our proposed method outperforms existing competitive domain adaptation segmentation algorithms.