IEEE Trans Med Imaging. 2017 Sep;36(9):1930-1938. doi: 10.1109/TMI.2017.2703147. Epub 2017 May 10.
Angle-closure glaucoma is a major cause of irreversible visual impairment and can be identified by measuring the anterior chamber angle (ACA) of the eye. The ACA can be viewed clearly through anterior segment optical coherence tomography (AS-OCT), but the imaging characteristics and the shapes and locations of major ocular structures can vary significantly among different AS-OCT modalities, thus complicating image analysis. To address this problem, we propose a data-driven approach for automatic AS-OCT structure segmentation, measurement, and screening. Our technique first estimates initial markers in the eye through label transfer from a hand-labeled exemplar data set, whose images are collected over different patients and AS-OCT modalities. These initial markers are then refined by using a graph-based smoothing method that is guided by AS-OCT structural information. These markers facilitate segmentation of major clinical structures, which are used to recover standard clinical parameters. These parameters can be used not only to support clinicians in making anatomical assessments, but also to serve as features for detecting anterior angle closure in automatic glaucoma screening algorithms. Experiments on Visante AS-OCT and Cirrus high-definition-OCT data sets demonstrate the effectiveness of our approach.
闭角型青光眼是不可逆视力损害的主要原因，可以通过测量眼睛的前房角（ACA）来确定。前节光学相干断层扫描（AS-OCT）可以清晰地观察到 ACA，但不同 AS-OCT 模式的成像特征以及主要眼部结构的形状和位置可能有很大差异，从而使图像分析变得复杂。为了解决这个问题，我们提出了一种基于数据驱动的自动 AS-OCT 结构分割、测量和筛查方法。我们的技术首先通过从手标记的示例数据集进行标签传递来估计眼睛中的初始标记，这些图像是在不同的患者和 AS-OCT 模式下采集的。然后，通过使用基于图形的平滑方法来细化这些初始标记，该方法由 AS-OCT 结构信息指导。这些标记有助于分割主要的临床结构，这些结构用于恢复标准的临床参数。这些参数不仅可以帮助临床医生进行解剖评估，还可以作为自动青光眼筛查算法中检测前角关闭的特征。在 Visante AS-OCT 和 Cirrus 高清-OCT 数据集上的实验证明了我们方法的有效性。
