Abstract
The development of artificial intelligence models for macular edema (ME) analy-sis always relies on expert-annotated pixel-level image datasets which are expen-sive to collect prospectively. While anomaly-detection-based weakly-supervised methods have shown promise in edema area (EA) segmentation task, their per-formance still lags behind fully-supervised approaches. In this paper, we leverage the strong correlation between EA and retinal layers in spectral-domain optical coherence tomography (SD-OCT) images, along with the update characteristics of weakly-supervised learning, to enhance an off-the-shelf adversarial framework for EA segmentation with a novel layer-structure-guided post-processing step and a test-time-adaptation (TTA) strategy. By incorporating additional retinal lay-er information, our framework reframes the dense EA prediction task as one of confirming intersection points between the EA contour and retinal layers, result-ing in predictions that better align with the shape prior of EA. Besides, the TTA framework further helps address discrepancies in the manifestations and presen-tations of EA between training and test sets. Extensive experiments on two pub-licly available datasets demonstrate that these two proposed ingredients can im-prove the accuracy and robustness of EA segmentation, bridging the gap between weakly-supervised and fully-supervised models.
Abstract (translated)
用于黄斑水肿（ME）分析的人工智能模型开发通常依赖于专家标注的像素级图像数据集，这些数据集前瞻性地收集起来成本很高。虽然基于异常检测的弱监督方法在水肿区域（EA）分割任务中显示出潜力，但它们的表现仍落后于完全监督的方法。在这篇论文中，我们利用光谱域光学相干断层扫描（SD-OCT）图像中的EA与视网膜层之间的强相关性，并结合弱监督学习的更新特性，通过一个新颖的层结构引导后处理步骤和测试时间适应（TTA）策略来增强现成的对抗框架，以改进EA分割。通过加入额外的视网膜层信息，我们的框架将密集的EA预测任务重新定义为确认EA轮廓与视网膜层交点的任务，从而产生更符合EA形状先验的预测结果。此外，TTA框架进一步帮助解决训练集和测试集中EA表现形式之间的差异。在两个公开数据集上的广泛实验表明，这两种提出的成分可以提高EA分割的准确性和鲁棒性，缩小了弱监督与完全监督模型之间的差距。
URL
https://arxiv.org/abs/2508.18790
