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

• URL: http://arxiv.org/abs/2310.11766v1
• Date: Wed, 18 Oct 2023 07:49:24 GMT
• Title: Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation
• Authors: Yanyu Ye, Zhenxi Zhang, Wei Wei, Chunna Tian
• Abstract summary: Source-free test-time adaptation for medical image segmentation aims to enhance the adaptability of segmentation models to diverse test sets of the target domain. Ensuring consistency between target edges and paired inputs is crucial for test-time adaptation. We propose a multi task consistency guided source-free test-time domain adaptation medical image segmentation method.
• Score: 8.591386126583748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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