Breaking the Single-Stage Barrier: Synergistic Data-Model Adaptation at Test-Time for Medical Image Segmentation

Zhou, Wenjuan; Chen, Wei; He, Yulin; Wu, Di; Li, Chen

Breaking the Single-Stage Barrier: Synergistic Data-Model Adaptation at Test-Time for Medical Image Segmentation

Files

pg20251273.pdf (1.92 MB)

Date

2025

Authors

Zhou, Wenjuan
Chen, Wei
He, Yulin
Wu, Di
Li, Chen

Publisher

The Eurographics Association

Abstract

Domain shift, predominantly caused by variations in medical imaging across different institutions, often leads to a decline in the accuracy of medical image segmentation models. While Test-Time Adaptation (TTA) holds promise to address this issue, existing methods exhibit significant limitations: model adaptation is prone to error accumulation and catastrophic forgetting in continuous domain learning. Meanwhile, data adaptation struggles to achieve deep latent alignment due to the inaccessibility of source domain data. To address these challenges, we propose Synergistic Data-Model Adaptation (SDMA), which innovatively leverages Batch Normalization (BN) layers as a bidirectional bridge to enable a two-stage joint adaptation process. In the data adaptation stage, domain-aware prompts dynamically adjust the BN statistics of incoming test data, achieving low-level distribution alignment in the Fourier space. In the model adaptation stage, we dynamically optimize the BN affine parameters based on strong-weak data augmentation and entropy minimization, enabling adaptation to high-level semantic features. Experiments conducted on five retinal fundus image datasets from various medical institutions demonstrate that our method achieves an average Dice improvement of 1.23% over previous state-of-the-art (SOTA) methods, establishing a new SOTA performance.

CCS Concepts: Computing methodologies → Image segmentation

        @inproceedings{10.2312:pg.20251273
,
booktitle = {Pacific Graphics Conference Papers, Posters, and Demos
},
editor = {Christie, Marc and 
Han, Ping-Hsuan and 
Lin, Shih-Syun and 
Pietroni, Nico and 
Schneider, Teseo and 
Tsai, Hsin-Ruey and 
Wang, Yu-Shuen and 
Zhang, Eugene
},
title = {{Breaking the Single-Stage Barrier: Synergistic Data-Model Adaptation at Test-Time for Medical Image Segmentation
}},
author = {Zhou, Wenjuan and 
Chen, Wei and 
He, Yulin and 
Wu, Di and 
Li, Chen
},
year = {2025
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-295-0
},
DOI = {10.2312/pg.20251273
}
}

URI

https://doi.org/10.2312/pg.20251273
https://diglib.eg.org/handle/10.2312/pg20251273

Collections

PG2025 Conference Papers, Posters, and Demos

Full item page