A Closer Look at Edema Area Segmentation in SD-OCT Images Using Adversarial Framework

• URL: http://arxiv.org/abs/2508.18790v1
• Date: Tue, 26 Aug 2025 08:16:48 GMT
• Title: A Closer Look at Edema Area Segmentation in SD-OCT Images Using Adversarial Framework
• Authors: Yuhui Tao, Yizhe Zhang, Qiang Chen,
• Abstract summary: We propose an off-the-shelf adversarial framework for edema area (EA) segmentation with a novel layer-structure-guided post-processing step and a test-time-adaptation strategy.<n>Our framework reframes the dense EA prediction task as one of confirming points between the EA contour and retinal layers, result-ing in predictions that better align with the shape prior to EA.
• Score: 9.612454002801242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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