IEEE J Biomed Health Inform. 2024 Sep;28(9):5497-5508. doi: 10.1109/JBHI.2024.3406447. Epub 2024 Sep 5.
Cross-domain joint segmentation of optic disc and optic cup on fundus images is essential, yet challenging, for effective glaucoma screening. Although many unsupervised domain adaptation (UDA) methods have been proposed, these methods can hardly achieve complete domain alignment, leading to suboptimal performance. In this paper, we propose a triple-level alignment (TriLA) model to address this issue by aligning the source and target domains at the input level, feature level, and output level simultaneously. At the input level, a learnable Fourier domain adaptation (LFDA) module is developed to learn the cut-off frequency adaptively for frequency-domain translation. At the feature level, we disentangle the style and content features and align them in the corresponding feature spaces using consistency constraints. At the output level, we design a segmentation consistency constraint to emphasize the segmentation consistency across domains. The proposed model is trained on the RIGA+ dataset and widely evaluated on six different UDA scenarios. Our comprehensive results not only demonstrate that the proposed TriLA substantially outperforms other state-of-the-art UDA methods in joint segmentation of optic disc and optic cup, but also suggest the effectiveness of the triple-level alignment strategy.
跨领域眼底图像视盘和视杯联合分割对于有效的青光眼筛查至关重要，但具有挑战性。尽管已经提出了许多无监督领域自适应（UDA）方法，但这些方法很难实现完全的域对齐，导致性能不佳。在本文中，我们提出了一种三级对齐（TriLA）模型，通过在输入级、特征级和输出级同时对齐源域和目标域来解决这个问题。在输入级，开发了一个可学习的傅里叶域自适应（LFDA）模块，以自适应地学习用于频域转换的截止频率。在特征级，我们解耦样式和内容特征，并使用一致性约束在相应的特征空间中对齐它们。在输出级，我们设计了一个分割一致性约束来强调跨域的分割一致性。所提出的模型在 RIGA+数据集上进行训练，并在六个不同的 UDA 场景中进行了广泛的评估。我们的综合结果不仅表明，所提出的 TriLA 在视盘和视杯的联合分割方面明显优于其他最先进的 UDA 方法，而且还表明了三级对齐策略的有效性。
