Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China; Glaucoma Artificial Intelligence Diagnosis and Imaging Analysis Joint Research Lab, Guangzhou & Ningbo, China.
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China; Glaucoma Artificial Intelligence Diagnosis and Imaging Analysis Joint Research Lab, Guangzhou & Ningbo, China.
Med Image Anal. 2021 Apr;69:101956. doi: 10.1016/j.media.2021.101956. Epub 2021 Jan 7.
Precise characterization and analysis of anterior chamber angle (ACA) are of great importance in facilitating clinical examination and diagnosis of angle-closure disease. Currently, the gold standard for diagnostic angle assessment is observation of ACA by gonioscopy. However, gonioscopy requires direct contact between the gonioscope and patients' eye, which is uncomfortable for patients and may deform the ACA, leading to false results. To this end, in this paper, we explore a potential way for grading ACAs into open-, appositional- and synechial angles by Anterior Segment Optical Coherence Tomography (AS-OCT), rather than the conventional gonioscopic examination. The proposed classification schema can be beneficial to clinicians who seek to better understand the progression of the spectrum of angle-closure disease types, so as to further assist the assessment and required treatment at different stages of angle-closure disease. To be more specific, we first use an image alignment method to generate sequences of AS-OCT images. The ACA region is then localized automatically by segmenting an important biomarker - the iris - as this is a primary structural cue in identifying angle-closure disease. Finally, the AS-OCT images acquired in both dark and bright illumination conditions are fed into our Multi-Sequence Deep Network (MSDN) architecture, in which a convolutional neural network (CNN) module is applied to extract feature representations, and a novel ConvLSTM-TC module is employed to study the spatial state of these representations. In addition, a novel time-weighted cross-entropy loss (TC) is proposed to optimize the output of the ConvLSTM, and the extracted features are further aggregated for the purposes of classification. The proposed method is evaluated across 66 eyes, which include 1584 AS-OCT sequences, and a total of 16,896 images. The experimental results show that the proposed method outperforms existing state-of-the-art methods in applicability, effectiveness, and accuracy.
精确描述和分析前房角（ACA）对于促进临床检查和闭角型眼病的诊断非常重要。目前，诊断角度评估的金标准是通过房角镜观察 ACA。然而，房角镜检查需要房角镜直接接触患者的眼睛，这会使患者感到不适，并且可能会使 ACA 变形，导致结果不准确。为此，在本文中，我们探讨了一种通过眼前节光学相干断层扫描（AS-OCT）将 ACA 分为开放角、贴附角和粘连角的潜在方法，而不是传统的房角镜检查。所提出的分类方案对希望更好地了解闭角型疾病类型谱进展的临床医生可能有益，以便在闭角型疾病的不同阶段进一步协助评估和所需的治疗。更具体地说，我们首先使用图像对齐方法生成 AS-OCT 图像序列。然后通过分割虹膜（这是识别闭角型疾病的主要结构线索）自动定位 ACA 区域。最后，将在暗照明和亮照明条件下获取的 AS-OCT 图像输入到我们的多序列深度网络（MSDN）架构中，其中卷积神经网络（CNN）模块用于提取特征表示，并且新的 ConvLSTM-TC 模块用于研究这些表示的空间状态。此外，提出了一种新的时间加权交叉熵损失（TC）来优化 ConvLSTM 的输出，并进一步聚合提取的特征以进行分类。该方法在 66 只眼睛上进行了评估，其中包括 1584 个 AS-OCT 序列，总共有 16896 张图像。实验结果表明，该方法在适用性、有效性和准确性方面均优于现有最先进的方法。
