Optical coherence tomography imaging provides a crucial clinical measurement for diagnosing and monitoring glaucoma through the two-dimensional retinal nerve fiber layer (RNFL) thickness (RNFLT) map. Researchers have been increasingly using neural models to extract meaningful features from the RNFLT map, aiming to identify biomarkers for glaucoma and its progression. However, accurately representing the RNFLT map features relevant to glaucoma is challenging due to significant variations in retinal anatomy among individuals, which confound the pathological thinning of the RNFL. Moreover, the presence of artifacts in the RNFLT map, caused by segmentation errors in the context of degraded image quality and defective imaging procedures, further complicates the task. In this paper, we propose a general framework called RNFLT2Vec for unsupervised learning of vectorized feature representations from RNFLT maps. Our method includes an artifact correction component that learns to rectify RNFLT values at artifact locations, producing a representation reflecting the RNFLT map without artifacts. Additionally, we incorporate two regularization techniques to encourage discriminative representation learning. Firstly, we introduce a contrastive learning-based regularization to capture the similarities and dissimilarities between RNFLT maps. Secondly, we employ a consistency learning-based regularization to align pairwise distances of RNFLT maps with their corresponding thickness distributions. Through extensive experiments on a large-scale real-world dataset, we demonstrate the superiority of RNFLT2Vec in three different clinical tasks: RNFLT pattern discovery, glaucoma detection, and visual field prediction. Our results validate the effectiveness of our framework and its potential to contribute to a better understanding and diagnosis of glaucoma.

中文翻译：

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RNFLT2Vec：视网膜神经纤维层厚度图的伪影校正表示学习

光学相干断层扫描成像通过二维视网膜神经纤维层 (RNFL) 厚度 (RNFLT) 图为诊断和监测青光眼提供了重要的临床测量。研究人员越来越多地使用神经模型从 RNFLT 图谱中提取有意义的特征，旨在识别青光眼及其进展的生物标志物。然而，由于个体之间视网膜解剖结构的显着差异，准确表示与青光眼相关的 RNFLT 图特征具有挑战性，这混淆了 RNFL 的病理性变薄。此外，由于图像质量下降和成像程序有缺陷的情况下的分割错误，RNFLT 图中存在伪影，这使得任务进一步复杂化。在本文中，我们提出了一个名为 RNFLT2Vec 的通用框架，用于从 RNFLT 映射中无监督学习矢量化特征表示。我们的方法包括一个伪影校正组件，它学习在伪影位置校正 RNFLT 值，生成反映没有伪影的 RNFLT 图的表示。此外，我们结合了两种正则化技术来鼓励判别性表示学习。首先，我们引入基于对比学习的正则化来捕获 RNFLT 图之间的相似点和不同点。其次，我们采用基于一致性学习的正则化来将 RNFLT 图的成对距离与其相应的厚度分布对齐。通过对大规模真实世界数据集的大量实验，我们证明了 RNFLT2Vec 在三个不同临床任务中的优越性：RNFLT 模式发现、青光眼检测和视野预测。我们的结果验证了我们框架的有效性及其有助于更好地理解和诊断青光眼的潜力。