Automatic angle-closure assessment in Anterior Segment OCT (AS-OCT) images is an important task for the screening and diagnosis of glaucoma, and the most recent computer-aided models focus on a binary classification of anterior chamber angles (ACA) in AS-OCT, i.e., open-angle and angle-closure. In order to assist clinicians who seek better to understand the development of the spectrum of glaucoma types, a more discriminating three-class classification scheme was suggested, i.e., the classification of ACA was expended to include open-, appositional- and synechial angles. However, appositional and synechial angles display similar appearances in an AS-OCT image, which makes classification models struggle to differentiate angle-closure subtypes based on static AS-OCT images. In order to tackle this issue, we propose a 2D-3D Hybrid Variation-aware Network (HV-Net) for open-appositional-synechial ACA classification from AS-OCT imagery. Specifically, taking into account clinical priors, we first reconstruct the 3D iris surface from an AS-OCT sequence, and obtain the geometrical characteristics necessary to provide global shape information. 2D AS-OCT slices and 3D iris representations are then fed into our HV-Net to extract cross-sectional appearance features and iris morphological features, respectively. To achieve similar results to those of dynamic gonioscopy examination, which is the current gold standard for diagnostic angle assessment, the paired AS-OCT images acquired in dark and light illumination conditions are used to obtain an accurate characterization of configurational changes in ACAs and iris shapes, using a Variation-aware Block. In addition, an annealing loss function was introduced to optimize our model, so as to encourage the sub-networks to map the inputs into the more conducive spaces to extract dark-to-light variation representations, while retaining the discriminative power of the learned features. The proposed model is evaluated across 1584 paired AS-OCT samples, and it has demonstrated its superiority in classifying open-, appositional- and synechial angles.

中文翻译：

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用于 AS-OCT 角度闭合评估的混合变化感知网络


眼前段 OCT (AS-OCT) 图像中的自动闭角评估是青光眼筛查和诊断的一项重要任务，最新的计算机辅助模型侧重于 AS-OCT 中前房角 (ACA) 的二元分类。 OCT，即开角和闭角。为了帮助临床医生更好地了解青光眼类型谱的发展，提出了一种更具区分性的三级分类方案，即ACA的分类扩展到包括开角、对位角和粘连角。然而，对位角和连联角在 AS-OCT 图像中显示出相似的外观，这使得分类模型难以区分基于静态 AS-OCT 图像的闭角亚型。为了解决这个问题，我们提出了一种 2D-3D 混合变化感知网络 (HV-Net)，用于从 AS-OCT 图像中进行开放对位联联 ACA 分类。具体来说，考虑到临床先验，我们首先从 AS-OCT 序列重建 3D 虹膜表面，并获得提供全局形状信息所需的几何特征。然后将 2D AS-OCT 切片和 3D 虹膜表示输入到我们的 HV-Net 中，分别提取横截面外观特征和虹膜形态特征。为了获得与动态前房角镜检查（当前诊断角度评估的黄金标准）类似的结果，使用在暗光和光亮照明条件下采集的成对 AS-OCT 图像来准确表征 ACA 和虹膜形状的配置变化，使用变化感知块。 此外，引入了退火损失函数来优化我们的模型，以鼓励子网络将输入映射到更有利于提取暗到亮变化表示的空间，同时保留所学习特征的判别能力。所提出的模型在 1584 个配对的 AS-OCT 样本中进行了评估，并证明了其在分类开角、对位角和联角方面的优越性。