3D Histomorphometric Reconstruction and Quantification of the Optic Nerve Head Connective Tissues

Yang, Hongli; Reynaud, Juan; Lockwood, Howard; Williams, Galen; Hardin, Christy; Reyes, Luke; Gardiner, Stuart K.; Burgoyne, Claude F.

doi:10.1007/978-1-4939-7407-8_17

Cited by 6 publications

(6 citation statements)

References 49 publications

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“…Our method for 3D HMRN, delineation (Figure 13) and parameterization (Figures 14–15) of the monkey ONH has been described within a series of previous reports (Burgoyne et al, 2004; Downs et al, 2007; Lockwood et al, 2015; Pazos et al, 2016; Reynaud et al, 2016; Yang et al, 2007a; Yang et al, 2009a; Yang et al, 2007b; Yang et al, 2011b) and is summarized in detail in a recent book chapter (Yang et al, Accepted for publication Nov 2016 Forthcoming 2017). After, sacrifice and 3D reconstruction of a 6 mm trephine that contains the ONH and pp-sclera, 40 radial digital section images (4.5° degrees apart) of the reconstruction are manually delineated (Figure 13) and global values for 19 standard 3D HMRN parameters (Figures 14–15) are quantified as previously described (Downs et al, 2007; Yang et al, 2007a; Yang et al, 2009a; Yang et al, 2007b; Yang et al, 2011b).…”

Section: 0 Experimental Evidence Of Onh Connective Tissue Deformatimentioning

confidence: 99%

The connective tissue phenotype of glaucomatous cupping in the monkey eye - Clinical and research implications

Yang

Reynaud

Lockwood

et al. 2017

Progress in Retinal and Eye Research

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In a series of previous publications we have proposed a framework for conceptualizing the optic nerve head (ONH) as a biomechanical structure. That framework proposes important roles for intraocular pressure (IOP), IOP-related stress and strain, cerebrospinal fluid pressure (CSFp), systemic and ocular determinants of blood flow, inflammation, auto-immunity, genetics, and other non-IOP related risk factors in the physiology of ONH aging and the pathophysiology of glaucomatous damage to the ONH. The present report summarizes 20 years of technique development and study results pertinent to the characterization of ONH connective tissue deformation and remodeling in the unilateral monkey experimental glaucoma (EG) model. In it we propose that the defining pathophysiology of a glaucomatous optic neuropathy involves deformation, remodeling, and mechanical failure of the ONH connective tissues. We view this as an active process, driven by astrocyte, microglial, fibroblast and oligodendrocyte mechanobiology. These cells, and the connective tissue phenomena they propagate, have primary and secondary effects on retinal ganglion cell (RGC) axon and laminar beam and retrolaminar capillary homeostasis that may initially be “protective” but eventually lead to RGC axonal injury, repair and/or cell death. The primary goal of this report is to summarize our 3D histomorphometric and optical coherence tomography (OCT)-based evidence for the early onset and progression of ONH connective tissue deformation and remodeling in monkey EG. A second goal is to explain the importance of including ONH connective tissue processes in characterizing the phenotype of a glaucomatous optic neuropathy in all species. A third goal is to summarize our current efforts to move from ONH morphology to the cell biology of connective tissue remodeling and axonal insult early in the disease. A final goal is to facilitate the translation of our findings and ideas into neuroprotective interventions that target these ONH phenomena for therapeutic effect.

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Section: 0 Experimental Evidence Of Onh Connective Tissue Deformatimentioning

confidence: 99%

The connective tissue phenotype of glaucomatous cupping in the monkey eye - Clinical and research implications

Yang

Reynaud

Lockwood

et al. 2017

Progress in Retinal and Eye Research

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“… 6 , 7 A previous histomorphometric study in NHPs showed that beams and pores in the LC ( Figure ) remodeled differently by quadrant, depth, and from the center to periphery. 8 Although LC microstructure 9 and 3D histomorphometry 10 of healthy and early experimental glaucoma (EG) in postmortem specimens from NHPs have been reported, there is a knowledge gap on the 3D microstructure of LC in healthy live monkeys. Considering the postmortem changes in structure, such as elimination of blood pressure and the collapse of blood vessels typically occupying the LC beams, impairment to fluid balance and transportation, and tissue stiffening, in vivo analysis is imperative to determine the actual LC structure that is involved in disease mechanisms.…”

Section: Introductionmentioning

confidence: 99%

3D Microstructure of the Healthy Non-Human Primate Lamina Cribrosa by Optical Coherence Tomography Imaging

Sainulabdeen

Glidai

et al. 2022

Trans. Vis. Sci. Tech.

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Purpose The lamina cribrosa (LC) has an important role in the pathophysiology of ocular diseases. The purpose of this study is to characterize in vivo, noninvasively, and in 3D the structure of the LC in healthy non-human primates (NHPs). Methods Spectral-domain optical coherence tomography (OCT; Leica, Chicago, IL) scans of the optic nerve head (ONH) were obtained from healthy adult rhesus macaques monkeys. Using a previously reported semi-automated segmentation algorithm, microstructure measurements were assessed in central and peripheral regions of an equal area, in quadrants and depth-wise. Linear mixed-effects models were used to compare parameters among regions, adjusting for visibility, age, analyzable depth, graded scan quality, disc area, and the correlation between eyes. Spearmen's rank correlation coefficients were calculated for assessing the association between the lamina's parameters. Results Sixteen eyes of 10 animals (7 males and 3 females; 9 OD, 7 OS) were analyzed with a mean age of 10.5 ± 2.1 years. The mean analyzable depth was 175 ± 37 µm, with average LC visibility of 25.4 ± 13.0% and average disc area of 2.67 ± 0.45mm 2 . Within this volume, an average of 74.9 ± 39.0 pores per eye were analyzed. The central region showed statistically significantly thicker beams than the periphery. The quadrant-based analysis showed significant differences between the superior and inferior quadrants. The anterior LC had smaller beams and pores than both middle and posterior lamina. Conclusions Our study provides in vivo microstructure details of NHP's LC to be used as the foundation for future studies. We demonstrated mostly small but statistically significant regional variations in LC microstructure that should be considered when comparing LC measurements.

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“…For our glaucoma diagnosis application, the ONH can simply be thought of as a complex 3D structure that can be represented by a cloud of points, as has been routinely performed in 3D histomorphometric and finite element studies. 16 – 18 …”

Section: Introductionmentioning

confidence: 99%

Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma

Thiéry

Braeu

Tun

et al. 2023

Trans. Vis. Sci. Tech.

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Purpose (1) To assess the performance of geometric deep learning in diagnosing glaucoma from a single optical coherence tomography (OCT) scan of the optic nerve head and (2) to compare its performance to that obtained with a three-dimensional (3D) convolutional neural network (CNN), and with a gold-standard parameter, namely, the retinal nerve fiber layer (RNFL) thickness. Methods Scans of the optic nerve head were acquired with OCT for 477 glaucoma and 2296 nonglaucoma subjects. All volumes were automatically segmented using deep learning to identify seven major neural and connective tissues. Each optic nerve head was then represented as a 3D point cloud with approximately 1000 points. Geometric deep learning (PointNet) was then used to provide a glaucoma diagnosis from a single 3D point cloud. The performance of our approach (reported using the area under the curve [AUC]) was compared with that obtained with a 3D CNN, and with the RNFL thickness. Results PointNet was able to provide a robust glaucoma diagnosis solely from a 3D point cloud (AUC = 0.95 ± 0.01).The performance of PointNet was superior to that obtained with a 3D CNN (AUC = 0.87 ± 0.02 [raw OCT images] and 0.91 ± 0.02 [segmented OCT images]) and with that obtained from RNFL thickness alone (AUC = 0.80 ± 0.03). Conclusions We provide a proof of principle for the application of geometric deep learning in glaucoma. Our technique requires significantly less information as input to perform better than a 3D CNN, and with an AUC superior to that obtained from RNFL thickness. Translational Relevance Geometric deep learning may help us to improve and simplify diagnosis and prognosis applications in glaucoma.

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3D Histomorphometric Reconstruction and Quantification of the Optic Nerve Head Connective Tissues

Cited by 6 publications

References 49 publications

The connective tissue phenotype of glaucomatous cupping in the monkey eye - Clinical and research implications

The connective tissue phenotype of glaucomatous cupping in the monkey eye - Clinical and research implications

3D Microstructure of the Healthy Non-Human Primate Lamina Cribrosa by Optical Coherence Tomography Imaging

Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma

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