Devalla Sripad Krishna, Renukanand Prajwal K, Sreedhar Bharathwaj K, Subramanian Giridhar, Zhang Liang, Perera Shamira, Mari Jean-Martial, Chin Khai Sing, Tun Tin A, Strouthidis Nicholas G, Aung Tin, Thiéry Alexandre H, Girard Michaël J A
Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore.
Duke-NUS, Graduate Medical School, Singapore.
Biomed Opt Express. 2018 Jun 25;9(7):3244-3265. doi: 10.1364/BOE.9.003244. eCollection 2018 Jul 1.
Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm (custom U-NET) was designed and trained to segment 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall Dice coefficient (mean of all tissues) was 0.91 ± 0.05 when assessed against manual segmentations performed by an expert observer. Further, we automatically extracted six clinically relevant neural and connective tissue structural parameters from the segmented tissues. We offer here a robust segmentation framework that could also be extended to the 3D segmentation of the ONH tissues.
鉴于视神经乳头（ONH）的神经组织和结缔组织会随着青光眼的发展和进展呈现出复杂的形态变化，从光学相干断层扫描（OCT）图像中同时分离出这些组织，对于这种疾病的临床诊断和管理可能具有重大意义。设计并训练了一种深度学习算法（定制U-NET），通过捕捉局部（组织纹理）和上下文信息（组织的空间排列）来分割6层ONH组织。与专家观察者进行的手动分割相比，总体Dice系数（所有组织的平均值）为0.91±0.05。此外，我们从分割后的组织中自动提取了六个临床相关的神经和结缔组织结构参数。我们在此提供了一个强大的分割框架，该框架也可扩展到ONH组织的三维分割。
