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.

翻译：卷积神经网络（CNN）在测试数据与训练（源）数据分布不同时，往往性能不佳，尤其在医学影像应用中，不同临床中心和扫描设备的成像协议差异会导致图像外观的显著变化。然而，由于隐私问题和标注成本高昂，重新访问源训练数据进行无监督域适应或标注额外测试数据以微调模型均存在困难。为解决该问题，我们提出一种新颖的图谱引导测试时自适应（TTA）方法——AdaAtlas，用于鲁棒的三维医学图像分割。AdaAtlas仅以单个无标签测试样本为输入，通过最小化基于图谱的损失函数来适配分割网络。具体而言，网络经适配后，其经过配准的预测结果与图谱空间中的学习图谱对齐，从而在测试阶段减少解剖结构分割错误。此外，与现有大多数仅限制在分割网络批归一化模块进行自适应的TTA方法不同，我们进一步利用通道注意力和空间注意力模块来提升测试时的自适应能力。在多个不同站点数据集上的大量实验表明，采用注意力模块适配的AdaAtlas（AdaAtlas-Attention）在性能提升上显著优于其他竞争性TTA方法。