Cargando…
Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network
PURPOSE: Adaptive optics imaging has enabled the visualization of photoreceptors both in health and disease. However, there remains a need for automated accurate cone photoreceptor identification in images of disease. Here, we apply an open-source convolutional neural network (CNN) to automatically...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7424931/ https://www.ncbi.nlm.nih.gov/pubmed/32855844 http://dx.doi.org/10.1167/tvst.9.2.40 |
_version_ | 1783570406422085632 |
---|---|
author | Morgan, Jessica I. W. Chen, Min Huang, Andrew M. Jiang, Yu You Cooper, Robert F. |
author_facet | Morgan, Jessica I. W. Chen, Min Huang, Andrew M. Jiang, Yu You Cooper, Robert F. |
author_sort | Morgan, Jessica I. W. |
collection | PubMed |
description | PURPOSE: Adaptive optics imaging has enabled the visualization of photoreceptors both in health and disease. However, there remains a need for automated accurate cone photoreceptor identification in images of disease. Here, we apply an open-source convolutional neural network (CNN) to automatically identify cones in images of choroideremia (CHM). We further compare the results to the repeatability and reliability of manual cone identifications in CHM. METHODS: We used split-detection adaptive optics scanning laser ophthalmoscopy to image the inner segment cone mosaic of 17 patients with CHM. Cones were manually identified twice by one experienced grader and once by two additional experienced graders in 204 regions of interest (ROIs). An open-source CNN either pre-trained on normal images or trained on CHM images automatically identified cones in the ROIs. True and false positive rates and Dice's coefficient were used to determine the agreement in cone locations between data sets. Interclass correlation coefficient was used to assess agreement in bound cone density. RESULTS: Intra- and intergrader agreement for cone density is high in CHM. CNN performance increased when it was trained on CHM images in comparison to normal, but had lower agreement than manual grading. CONCLUSIONS: Manual cone identifications and cone density measurements are repeatable and reliable for images of CHM. CNNs show promise for automated cone selections, although additional improvements are needed to equal the accuracy of manual measurements. TRANSLATIONAL RELEVANCE: These results are important for designing and interpreting longitudinal studies of cone mosaic metrics in disease progression or treatment intervention in CHM. |
format | Online Article Text |
id | pubmed-7424931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-74249312020-08-26 Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network Morgan, Jessica I. W. Chen, Min Huang, Andrew M. Jiang, Yu You Cooper, Robert F. Transl Vis Sci Technol Special Issue PURPOSE: Adaptive optics imaging has enabled the visualization of photoreceptors both in health and disease. However, there remains a need for automated accurate cone photoreceptor identification in images of disease. Here, we apply an open-source convolutional neural network (CNN) to automatically identify cones in images of choroideremia (CHM). We further compare the results to the repeatability and reliability of manual cone identifications in CHM. METHODS: We used split-detection adaptive optics scanning laser ophthalmoscopy to image the inner segment cone mosaic of 17 patients with CHM. Cones were manually identified twice by one experienced grader and once by two additional experienced graders in 204 regions of interest (ROIs). An open-source CNN either pre-trained on normal images or trained on CHM images automatically identified cones in the ROIs. True and false positive rates and Dice's coefficient were used to determine the agreement in cone locations between data sets. Interclass correlation coefficient was used to assess agreement in bound cone density. RESULTS: Intra- and intergrader agreement for cone density is high in CHM. CNN performance increased when it was trained on CHM images in comparison to normal, but had lower agreement than manual grading. CONCLUSIONS: Manual cone identifications and cone density measurements are repeatable and reliable for images of CHM. CNNs show promise for automated cone selections, although additional improvements are needed to equal the accuracy of manual measurements. TRANSLATIONAL RELEVANCE: These results are important for designing and interpreting longitudinal studies of cone mosaic metrics in disease progression or treatment intervention in CHM. The Association for Research in Vision and Ophthalmology 2020-07-16 /pmc/articles/PMC7424931/ /pubmed/32855844 http://dx.doi.org/10.1167/tvst.9.2.40 Text en Copyright 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. |
spellingShingle | Special Issue Morgan, Jessica I. W. Chen, Min Huang, Andrew M. Jiang, Yu You Cooper, Robert F. Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network |
title | Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network |
title_full | Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network |
title_fullStr | Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network |
title_full_unstemmed | Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network |
title_short | Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network |
title_sort | cone identification in choroideremia: repeatability, reliability, and automation through use of a convolutional neural network |
topic | Special Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7424931/ https://www.ncbi.nlm.nih.gov/pubmed/32855844 http://dx.doi.org/10.1167/tvst.9.2.40 |
work_keys_str_mv | AT morganjessicaiw coneidentificationinchoroideremiarepeatabilityreliabilityandautomationthroughuseofaconvolutionalneuralnetwork AT chenmin coneidentificationinchoroideremiarepeatabilityreliabilityandautomationthroughuseofaconvolutionalneuralnetwork AT huangandrewm coneidentificationinchoroideremiarepeatabilityreliabilityandautomationthroughuseofaconvolutionalneuralnetwork AT jiangyuyou coneidentificationinchoroideremiarepeatabilityreliabilityandautomationthroughuseofaconvolutionalneuralnetwork AT cooperrobertf coneidentificationinchoroideremiarepeatabilityreliabilityandautomationthroughuseofaconvolutionalneuralnetwork |