Semi-supervised deep learning based 3D analysis of the peripapillary region
Semi-supervised deep learning based 3D analysis of the peripapillary region
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DOI：
10.1364/boe.392648
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发表时间：
2020-07-01
影响因子：
3.4
通讯作者：
Sarunic, Marinko, V
中科院分区：
文献类型：
--
作者：
Heisler, Morgan;Bhalla, Mahadev;Sarunic, Marinko, V
光学相干断层扫描(OCT)已经成为评估青光眼的重要工具，通常是通过分析视网膜神经纤维层在乳突周围扫描中的变化。三维OCT体积可以更全面地分析视神经头(ONH)区域，该区域可能是青光眼视神经损伤的初始位置。这一区域的自动化分析是非常有意义的，尽管巨大的解剖变异和层的终止使得必要的乳头状层和Bruchs膜开口(BMO)分割是一项具有挑战性的任务。已经提出了几种基于机器学习的视网膜层分割方法，其中一些方法是针对ONH区域的，但它们通常依赖于严重平均或预处理的B超或大量的注释数据，这是一项繁琐的任务和资源密集型。我们评估了一种半监督对抗性深度学习方法，用于在OCT B扫描中分割乳头周围视网膜层，以利用未标记的数据。我们表明，使用生成性对抗性网络和未标记数据可以提高分割的性能。此外，我们使用更快的R-CNN架构来自动分割BMO。然后，所提出的方法被用于对对照和青光眼ONH体积的3D形态计量分析，以证明其临床应用的潜力。(C)OSA开放获取出版协议条款下的2020年美国光学学会
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 tbr 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. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement