  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.
