Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation

TL;DR

This paper introduces AdaAtlas, a novel test-time adaptation method for 3D medical image segmentation that uses an atlas-guided approach and attention mechanisms to improve robustness across different data sources without needing source data or labels.

Contribution

The paper proposes AdaAtlas, a new atlas-guided test-time adaptation method utilizing attention blocks, which enhances segmentation robustness in medical imaging without source data or additional labels.

Findings

AdaAtlas significantly outperforms existing TTA methods.

Using attention blocks improves adaptation effectiveness.

The method is effective across multiple datasets and sites.

Abstract

Convolutional neural networks (CNNs) often suffer from poor performance when tested on target data that differs from the training (source) data distribution, particularly in medical imaging applications where variations in imaging protocols across different clinical sites and scanners lead to different imaging appearances. However, re-accessing source training data for unsupervised domain adaptation or labeling additional test data for model fine-tuning can be difficult due to privacy issues and high labeling costs, respectively. To solve this problem, we propose a novel atlas-guided test-time adaptation (TTA) method for robust 3D medical image segmentation, called AdaAtlas. AdaAtlas only takes one single unlabeled test sample as input and adapts the segmentation network by minimizing an atlas-based loss. Specifically, the network is adapted so that its prediction after registration is aligned with the learned atlas in the atlas space, which helps to reduce anatomical segmentation errors at test time. In addition, different from most existing TTA methods which restrict the adaptation to batch normalization blocks in the segmentation network only, we further exploit the use of channel and spatial attention blocks for improved adaptability at test time. Extensive experiments on multiple datasets from different sites show that AdaAtlas with attention blocks adapted (AdaAtlas-Attention) achieves superior performance improvements, greatly outperforming other competitive TTA methods.