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🧠 A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation

This repository contains the official PyTorch implementation of:

A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation
📘 IEEE Transactions on Image Processing (TIP), 2025
🔗 https://ieeexplore.ieee.org/document/11311445

🔍 Overview

A3-TTA is a source-free test-time adaptation (TTA) framework for robust image segmentation under domain shift.
It adapts a pretrained segmentation model online at test time, using only unlabeled target-domain data, without access to source images or retraining.

Unlike prior pseudo-label-based TTA methods that rely on perturbation ensembles (e.g., dropout, TTA, noise), A3-TTA introduces anchor-guided alignment, constructing stable and distribution-aware pseudo supervision for reliable adaptation.

A3-TTA is:

  • 🔄 Online & continual (single-pass over test data)
  • ❌ Source-free
  • 🧩 Model-agnostic
  • 🧠 Structure-aware (boundary-sensitive dense supervision)

📦 Installation

We recommend using a conda environment:

conda create -n a3tta python=3.9
conda activate a3tta
pip install -r requirements.txt

📂 Dataset: M&MS

We adopt the M&MS dataset for cardiac MRI segmentation.
Official website: https://www.ub.edu/mnms/

Steps:

  1. Apply for and obtain official permission to use the dataset.

  2. Download the dataset through the official portal.

  3. (Optional) Use our processed M&MS 2D version:

    👉 Google Drive:
    https://drive.google.com/file/d/1jaT2nsbF1-rPoWs6fnF9DsFxTuaYXqW2/view?usp=sharing

  4. Extract the processed data into: A3-TTA/data/mms2d/

📂 Dataset: Prostate

Steps:

  1. Apply for and obtain official permission to use the dataset.

  2. Download the dataset through the official portal.

  3. (Optional) Use our processed Prostate data:

    👉 Google Drive:
    https://drive.google.com/file/d/1MdDJqxqiZ_0vYdPmcZMvVr-jcz8dxaYk/view?usp=drive_link

  4. Extract the processed data into: A3-TTA/data/prostate2d/

🚀 Source Model Training (Using the M&Ms Dataset; Prostate Dataset Follows the Same Procedure)

To train a UNet segmentation model on the source domain:

python train_source_2d.py --cfg cfgs/prostate/source.yaml
  • Checkpoints are saved to: save_model/

🧪 Test-Time Adaptation

We provide evaluation scripts for A3-TTA and other methods. All experiments are configured via YAML files.

# A3-TTA (our method)
python test-time-adaptation.py --cfg cfgs/prostate/a3-tta_prostate.yaml

📚 Citation

If you find A3-TTA useful, please consider citing:

@article{wu2025a3tta,
  author={Wu, Jianghao and Luo, Xiangde and Zhou, Yubo and Wu, Lianming and Wang, Guotai and Zhang, Shaoting},
  journal={IEEE Transactions on Image Processing}, 
  title={A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation}, 
  year={2025},
  volume={34},
  pages={8511--8522},
  doi={10.1109/TIP.2025.3644789}
}

🙋 Acknowledgements

This repo builds upon ideas from:

  • TENT
  • and others, re-implemented for medical segmentation tasks.

📮 Contact

If you have questions, feel free to open an issue or reach out.

Happy Adapting! 🎯