scholarly journals AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning

2021 ◽  
Vol 10 (14) ◽  
pp. 22
Author(s):  
Wenxiang Deng ◽  
Adam Hedberg-Buenz ◽  
Dana A. Soukup ◽  
Sima Taghizadeh ◽  
Kai Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optic Nerve ◽  
Nerve Axon

scholarly journals Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma

2021 ◽  
Vol 10 (15) ◽  
pp. 3231
Author(s):  
Marta Gonzalez-Hernandez ◽  
Daniel Gonzalez-Hernandez ◽  
Daniel Perez-Barbudo ◽  
Paloma Rodriguez-Esteve ◽  
Nisamar Betancor-Caro ◽  
...  
Keyword(s):  
Distribution Function ◽  
Deep Learning ◽  
Optic Nerve ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Learning Models ◽  
Colorimetric Analysis ◽  
Cirrus Oct ◽  
And Function ◽  
Usual Pattern

Background: Laguna-ONhE is an application for the colorimetric analysis of optic nerve images, which topographically assesses the cup and the presence of haemoglobin. Its latest version has been fully automated with five deep learning models. In this paper, perimetry in combination with Laguna-ONhE or Cirrus-OCT was evaluated. Methods: The morphology and perfusion estimated by Laguna ONhE were compiled into a “Globin Distribution Function” (GDF). Visual field irregularity was measured with the usual pattern standard deviation (PSD) and the threshold coefficient of variation (TCV), which analyses its harmony without taking into account age-corrected values. In total, 477 normal eyes, 235 confirmed, and 98 suspected glaucoma cases were examined with Cirrus-OCT and different fundus cameras and perimeters. Results: The best Receiver Operating Characteristic (ROC) analysis results for confirmed and suspected glaucoma were obtained with the combination of GDF and TCV (AUC: 0.995 and 0.935, respectively. Sensitivities: 94.5% and 45.9%, respectively, for 99% specificity). The best combination of OCT and perimetry was obtained with the vertical cup/disc ratio and PSD (AUC: 0.988 and 0.847, respectively. Sensitivities: 84.7% and 18.4%, respectively, for 99% specificity). Conclusion: Using Laguna ONhE, morphology, perfusion, and function can be mutually enhanced with the methods described for the purpose of glaucoma assessment, providing early sensitivity.

Estimating Normal Optic Nerve Axon Numbers in Non-Human Primate Eyes

2003 ◽  
Vol 12 (4) ◽  
pp. 301-306 ◽  
Author(s):  
Grant Cull ◽  
George A. Cioffi ◽  
Jin Dong ◽  
Louis Homer ◽  
Lin Wang
Keyword(s):  
Optic Nerve ◽  
Nerve Axon ◽  
Human Primate

scholarly journals DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images

2018 ◽  
Vol 9 (7) ◽  
pp. 3244 ◽  
Author(s):  
Sripad Krishna Devalla ◽  
Prajwal K. Renukanand ◽  
Bharathwaj K. Sreedhar ◽  
Giridhar Subramanian ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Optic Nerve ◽  
Optic Nerve Head ◽  
Optical Coherence ◽  
Learning Network ◽  
Deep Learning Network

scholarly journals Relationship between Orbital Optic Nerve Axon Counts and Retinal Nerve Fiber Layer Thickness Measured by Spectral Domain Optical Coherence Tomography

2012 ◽  
Vol 53 (12) ◽  
pp. 7766 ◽  
Author(s):  
Grant A. Cull ◽  
Juan Reynaud ◽  
Lin Wang ◽  
George A. Cioffi ◽  
Claude F. Burgoyne ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Optic Nerve ◽  
Nerve Fiber ◽  
Layer Thickness ◽  
Retinal Nerve Fiber Layer ◽  
Optical Coherence ◽  
Fiber Layer ◽  
Nerve Fiber Layer ◽  
Nerve Axon ◽  
Nerve Fiber Layer Thickness

scholarly journals Dense Optic Nerve Head Deformation Estimated using CNN as a Structural Biomarker of Glaucoma Progression

2021 ◽  
Author(s):  
Ali Salehi ◽  
Madhusudhanan Balasubramanian
Keyword(s):  
Deep Learning ◽  
Optic Nerve ◽  
Diagnostic Accuracy ◽  
Optic Nerve Head ◽  
Fine Tuning ◽  
Clinical Population ◽  
Change Analysis ◽  
Learning Methods ◽  
Experimental Conditions ◽  
Glaucoma Progression

Purpose: To present a new structural biomarker for detecting glaucoma progression based on structural transformation of the optic nerve head (ONH) region. Methods: A dense ONH deformation was estimated using deep learning methods namely DDCNet-Multires, FlowNet2, and FlowNet-Correlation, and legacy computational methods namely the topographic change analysis (TCA) and proper orthogonal decomposition (POD) methods using longitudinal confocal scans of the ONH for each study eye. A candidate structural biomarker of glaucoma progression in a study eye was estimated as average magnitude of flow velocities within the ONH region. The biomarker was evaluated using longitudinal confocal scans of 12 laser-treated and 12 contralateral normal eyes of 12 primates from the LSU Experimental Glaucoma Study (LEGS); and 36 progressing eyes and 21 longitudinal normal eyes from the UCSD Diagnostic Innovations in Glaucoma Study (DIGS). Area under the ROC curves (AUC) was used to assess the diagnostic accuracy of the candidate biomarker. Results: AUROC (95\% CI) for LEGS were: 0.83 (0.79, 0.88) for DDCNet-Multires; 0.83 (0.78, 0.88) for FlowNet2; 0.83 (0.78, 0.88) for FlowNet-Correlation; 0.94 (0.91, 0.97) for POD; and 0.86 (0.82, 0.91) for TCA methods. For DIGS: 0.89 (0.80, 0.97) for DDCNet-Multires; 0.82 (0.71, 0.93) for FlowNet2; 0.93 (0.86, 0.99) for FlowNet-Correlation; 0.86 (0.76, 0.96) for POD; and 0.86 (0.77, 0.95) for TCA methods. Lower diagnostic accuracy of the learning-based methods for LEG study eyes were due to image alignment errors in confocal sequences. Conclusion: Deep learning methods trained to estimate generic deformation were able to detect ONH deformation from confocal images and provided a higher diagnostic accuracy when compared to the classical optical flow and legacy biomarkers of glaucoma progression. Because it is difficult to validate the estimates of dense ONH deformation in clinical population, our validation using ONH sequences under controlled experimental conditions confirms the diagnostic accuracy of the biomarkers observed in the clinical population. Performance of these deep learning methods can be further improved by fine-tuning these networks using longitudinal ONH sequences instead of training the network to be a general-purpose deformation estimator.

scholarly journals A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Sripad Krishna Devalla ◽  
Giridhar Subramanian ◽  
Tan Hung Pham ◽  
Xiaofei Wang ◽  
Shamira Perera ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Optic Nerve ◽  
Optic Nerve Head ◽  
Similarity Index ◽  
Optical Coherence ◽  
Patient Discomfort ◽  
Single Frame ◽  
The Mean ◽  
Noise Ratio

Abstract Optical coherence tomography (OCT) has become an established clinical routine for the in vivo imaging of the optic nerve head (ONH) tissues, that is crucial in the diagnosis and management of various ocular and neuro-ocular pathologies. However, the presence of speckle noise affects the quality of OCT images and its interpretation. Although recent frame-averaging techniques have shown to enhance OCT image quality, they require longer scanning durations, resulting in patient discomfort. Using a custom deep learning network trained with 2,328 ‘clean B-scans’ (multi-frame B-scans; signal averaged), and their corresponding ‘noisy B-scans’ (clean B-scans + Gaussian noise), we were able to successfully denoise 1,552 unseen single-frame (without signal averaging) B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean signal to noise ratio (SNR) increased from 4.02 ± 0.68 dB (single-frame) to 8.14 ± 1.03 dB (denoised). For all the ONH tissues, the mean contrast to noise ratio (CNR) increased from 3.50 ± 0.56 (single-frame) to 7.63 ± 1.81 (denoised). The mean structural similarity index (MSSIM) increased from 0.13 ± 0.02 (single frame) to 0.65 ± 0.03 (denoised) when compared with the corresponding multi-frame B-scans. Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort.

scholarly journals Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps

Ophthalmology ◽  
2020 ◽  
Vol 127 (3) ◽  
pp. 346-356 ◽  
Author(s):  
Mark Christopher ◽  
Christopher Bowd ◽  
Akram Belghith ◽  
Michael H. Goldbaum ◽  
Robert N. Weinreb ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optic Nerve ◽  
Visual Field ◽  
Optic Nerve Head ◽  
Learning Approaches ◽  
Fiber Layer ◽  
Nerve Fiber Layer Thickness ◽  
Face Images ◽  
En Face ◽  
Visual Field Damage

scholarly journals TRPV1 Tunes Optic Nerve Axon Excitability in Glaucoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Nolan R. McGrady ◽  
Michael L. Risner ◽  
Victoria Vest ◽  
David J. Calkins
Keyword(s):  
Optic Nerve ◽  
Nerve Axon

A Deep Learning System Outperforms Clinicians in Identifying Optic Nerve Head Abnormalities Heralding Vision- and Life-Threatening Conditions

2021 ◽  
Author(s):  
Caroline Vasseneix ◽  
Simon Nusinovici ◽  
Xinxing Xu ◽  
Jeong Min Hwang ◽  
Steffen Hamann ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optic Nerve ◽  
Optic Nerve Head ◽  
Learning System ◽  
Life Threatening
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