Purpose: To compare serial optic nerve head (ONH) histology with interpolated B-scans generated from a three-dimensional (3-D) spectral domain (SD)-OCT ONH volume acquired in vivo from the same normal monkey eye.

Methods: A 15 degrees ONH SD-OCT volume was acquired in a normal monkey eye, with IOP manometrically controlled at 10 mm Hg. After perfusion fixation at 10 mm Hg, the ONH was trephined, the specimen embedded in a paraffin block, and serial sagittal sections cut at 4-mum intervals. The location of each histologic section was identified within the optic disc photograph by matching the position of the retinal vessels and of Bruch's membrane opening. By altering the angles of rotation and incidence, interpolated B-scans matching the location of the histologic sections were generated with custom software. Structures identified in the histologic sections were compared with signals identified in the matched B-scans.

Results: Close matches between histologic sections and interpolated B-scans were identified throughout the extent of the ONH. SD-OCT identified the neural canal opening as the termination of the Bruch's membrane-retinal pigment complex and border tissue as the innermost termination of the choroidal signal. The anterior lamina cribrosa and its continuity with the prelaminar glial columns were also detected by SD-OCT.

Conclusions: Volumetric SD-OCT imaging of the ONH generates interpolated B-scans that accurately match serial histologic sections. SD-OCT captures the anterior laminar surface, which is likely to be a key structure in the detection of early ONH damage in ocular hypertension and glaucoma.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2829380PMC
http://dx.doi.org/10.1167/iovs.09-3984DOI Listing

Publication Analysis

Top Keywords

interpolated b-scans
16
histologic sections
16
optic nerve
8
nerve head
8
spectral domain
8
volume acquired
8
normal monkey
8
onh sd-oct
8
location histologic
8
onh
7

Similar Publications

Aims: To develop a generative adversarial network (GAN) capable of generating realistic high-resolution anterior segment optical coherence tomography (AS-OCT) images.

Methods: This study included 142 628 AS-OCT B-scans from the American University of Beirut Medical Center. The Style and WAvelet based GAN architecture was trained to generate realistic AS-OCT images and was evaluated through the Fréchet Inception Distance (FID) Score and a blinded assessment by three refractive surgeons who were asked to distinguish between real and generated images.

View Article and Find Full Text PDF

Deep Learning-Assisted Measurements of Photoreceptor Ellipsoid Zone Area and Outer Segment Volume as Biomarkers for Retinitis Pigmentosa.

Bioengineering (Basel)

December 2023

Retina Foundation of the Southwest, 9600 North Central Expressway, Suite 200, Dallas, TX 75231, USA.

The manual segmentation of retinal layers from OCT scan images is time-consuming and costly. The deep learning approach has potential for the automatic delineation of retinal layers to significantly reduce the burden of human graders. In this study, we compared deep learning model (DLM) segmentation with manual correction (DLM-MC) to conventional manual grading (MG) for the measurements of the photoreceptor ellipsoid zone (EZ) area and outer segment (OS) volume in retinitis pigmentosa (RP) to assess whether DLM-MC can be a new gold standard for retinal layer segmentation and for the measurement of retinal layer metrics.

View Article and Find Full Text PDF

Purpose: Previously, we have shown the capability of a hybrid deep learning (DL) model that combines a U-Net and a sliding-window (SW) convolutional neural network (CNN) for automatic segmentation of retinal layers from OCT scan images in retinitis pigmentosa (RP). We found that one of the shortcomings of the hybrid model is that it tends to underestimate ellipsoid zone (EZ) width or area, especially when EZ extends toward or beyond the edge of the macula. In this study, we trained the model with additional data which included more OCT scans having extended EZ.

View Article and Find Full Text PDF

Intraretinal Layer Segmentation Using Cascaded Compressed U-Nets.

J Imaging

May 2022

Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13125 Berlin, Germany.

Article Synopsis
  • Reliable biomarkers for conditions like Multiple Sclerosis, Alzheimer's, and Parkinson's are necessary but lacking, and intraretinal layer thickness from OCT images shows promise despite challenges in subtle changes and weak tissue gradients.
  • The proposed solution is a two-stage network called CCU-INSEG that effectively segments retinal tissues and eight intraretinal layers, using a refined method for post-processing and improved training techniques to address data imbalance.
  • The CCU-INSEG method demonstrates significant accuracy, achieving mean absolute errors of 2.3 μm and 2.6 μm compared to manual segmentation, and it outperforms existing methods, providing a more reliable approach for future research and clinical applications.
View Article and Find Full Text PDF

Artifact Rates for 2D Retinal Nerve Fiber Layer Thickness Versus 3D Neuroretinal Rim Thickness Using Spectral-Domain Optical Coherence Tomography.

Transl Vis Sci Technol

September 2020

Department of Ophthalmology, Glaucoma Service, Massachusetts Eye and Ear Infirmary, Boston, MA, USA.

Purpose: To compare the rates of clinically significant artifacts for two-dimensional peripapillary retinal nerve fiber layer (RNFL) thickness versus three-dimensional (3D) neuroretinal rim thickness using spectral-domain optical coherence tomography (SD-OCT).

Methods: Only one eye per patient was used for analysis of 120 glaucoma patients and 114 normal patients. For RNFL scans and optic nerve scans, 15 artifact types were calculated per B-scan and per eye.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!