Histogram matching‐enhanced adversarial learning for unsupervised domain adaptation in medical image segmentation

作者：Xiaoxue Qian, Hua‐Chieh Shao, Yunxiang Li, Weiguo Lu, You Zhang · 发表于：Medical Physics · 年份：2025 · DOI：10.1002/mp.17757 · 被引用次数：10 · 研究领域：Domain Adaptation and Few-Shot Learning、Artificial Intelligence in Healthcare and Education、Adversarial Robustness in Machine Learning

BACKGROUND: Unsupervised domain adaptation (UDA) seeks to mitigate the performance degradation of deep neural networks when applied to new, unlabeled domains by leveraging knowledge from source domains. In medical image segmentation, prevailing UDA techniques often utilize adversarial learning to address domain shifts for cross-modality adaptation. Current research on adversarial learning tends to adopt increasingly complex models and loss functions, making the training process highly intricate and less stable/robust. Furthermore, most methods primarily focused on segmentation accuracy while neglecting the associated confidence levels and uncertainties. PURPOSE: To develop a simple yet effective UDA method based on histogram matching-enhanced adversarial learning (HMeAL-UDA), and provide comprehensive uncertainty estimations of the model predictions. METHODS: Aiming to bridge the domain gap while reducing the model complexity, we developed a novel adversarial learning approach to align multi-modality features. The method, termed HMeAL-UDA, integrates a plug-and-play histogram matching strategy to mitigate domain-specific image style biases across modalities. We employed adversarial learning to constrain the model in the prediction space, enabling it to focus on domain-invariant features during segmentation. Moreover, we quantified the model's prediction confidence using Monte Carlo (MC) dropouts to assess two voxel-level uncertainty estimates of the segmentation results, whic...