Heisler Morgan, Bhalla Mahadev, Lo Julian, Mammo Zaid, Lee Sieun, Ju Myeong Jin, Beg Mirza Faisal, Sarunic Marinko V
Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada.
University of British Columbia, Faculty of Medicine, 317-2194 Health Sciences Mall, Vancouver, BC, V6 T 1Z3, Canada.
Biomed Opt Express. 2020 Jun 19;11(7):3843-3856. doi: 10.1364/BOE.392648. eCollection 2020 Jul 1.
Optical coherence tomography (OCT) has become an essential tool in the evaluation of glaucoma, typically through analyzing retinal nerve fiber layer changes in circumpapillary scans. Three-dimensional OCT volumes enable a much more thorough analysis of the optic nerve head (ONH) region, which may be the site of initial glaucomatous optic nerve damage. Automated analysis of this region is of great interest, though large anatomical variations and the termination of layers make the requisite peripapillary layer and Bruch's membrane opening (BMO) segmentation a challenging task. Several machine learning-based segmentation methods have been proposed for retinal layer segmentation, and a few for the ONH region, but they typically depend on either heavily averaged or pre-processed B-scans or a large amount of annotated data, which is a tedious task and resource-intensive. We evaluated a semi-supervised adversarial deep learning method for segmenting peripapillary retinal layers in OCT B-scans to take advantage of unlabeled data. We show that the use of a generative adversarial network and unlabeled data can improve the performance of segmentation. Additionally, we use a Faster R-CNN architecture to automatically segment the BMO. The proposed methods are then used for the 3D morphometric analysis of both control and glaucomatous ONH volumes to demonstrate the potential for clinical utility.
光学相干断层扫描（OCT）已成为评估青光眼的重要工具，通常是通过分析视乳头周围扫描中的视网膜神经纤维层变化来实现。三维OCT容积能够对视神经乳头（ONH）区域进行更全面的分析，该区域可能是青光眼性视神经初始损伤的部位。对该区域进行自动分析具有重要意义，然而，巨大的解剖变异和各层的终止使得对视乳头周围层和布鲁赫膜开口（BMO）进行必要的分割成为一项具有挑战性的任务。已经提出了几种基于机器学习的分割方法用于视网膜层分割，也有一些用于ONH区域，但它们通常依赖于高度平均或预处理的B扫描或大量的标注数据，这是一项繁琐且资源密集的任务。我们评估了一种半监督对抗深度学习方法，用于在OCT B扫描中分割视乳头周围视网膜层，以利用未标记的数据。我们表明，使用生成对抗网络和未标记数据可以提高分割性能。此外，我们使用更快的R-CNN架构自动分割BMO。然后将所提出的方法用于对照和青光眼性ONH容积的三维形态计量分析，以证明其临床应用潜力。
