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Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification
PURPOSE: Classification of diabetic retinopathy (DR) is traditionally based on severity grading, given by the most advanced lesion, but potentially leaving out relevant information for risk stratification. In this study, we aimed to develop a deep learning model able to individually segment seven di...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9233290/ https://www.ncbi.nlm.nih.gov/pubmed/35731541 http://dx.doi.org/10.1167/tvst.11.6.19 |
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author | Andersen, Jakob K. H. Hubel, Martin S. Rasmussen, Malin L. Grauslund, Jakob Savarimuthu, Thiusius R. |
author_facet | Andersen, Jakob K. H. Hubel, Martin S. Rasmussen, Malin L. Grauslund, Jakob Savarimuthu, Thiusius R. |
author_sort | Andersen, Jakob K. H. |
collection | PubMed |
description | PURPOSE: Classification of diabetic retinopathy (DR) is traditionally based on severity grading, given by the most advanced lesion, but potentially leaving out relevant information for risk stratification. In this study, we aimed to develop a deep learning model able to individually segment seven different DR-lesions, in order to test if this would improve a subsequently developed classification model. METHODS: First, manual segmentation of 34,075 different DR-lesions was used to construct a segmentation model, with performance subsequently compared to another retinal specialist. Second, we constructed a 5-step classification model using a data set of 31,325 expert-annotated retinal 6-field images and evaluated if performance was improved with the integration of presegmentation given by the segmentation model. RESULTS: The segmentation model had higher average sensitivity across all abnormalities compared to the retinal expert (0.68 and 0.62) at a comparable average F1-score (0.60 and 0.62). Model sensitivity for microaneurysms, retinal hemorrhages and intraretinal microvascular abnormalities was higher by 42.5%, 8.8%, and 67.5% and F1-scores by 15.8%, 6.5%, and 12.5%, respectively. When presegmentation was included, grading performance increased by 29.7%, 6.0%, and 4.5% for average per class accuracy, quadratic weighted kappa, and multiclass macro area under the curve, with values of 70.4%, 0.90, and 0.92, respectively. CONCLUSIONS: The segmentation model matched an expert in detecting retinal abnormalities, and presegmentation substantially improved accuracy of the automated classification model. TRANSLATIONAL RELEVANCE: Presegmentation may yield more accurate automated DR grading models and increase interpretability and trust in model decisions. |
format | Online Article Text |
id | pubmed-9233290 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-92332902022-06-26 Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification Andersen, Jakob K. H. Hubel, Martin S. Rasmussen, Malin L. Grauslund, Jakob Savarimuthu, Thiusius R. Transl Vis Sci Technol Article PURPOSE: Classification of diabetic retinopathy (DR) is traditionally based on severity grading, given by the most advanced lesion, but potentially leaving out relevant information for risk stratification. In this study, we aimed to develop a deep learning model able to individually segment seven different DR-lesions, in order to test if this would improve a subsequently developed classification model. METHODS: First, manual segmentation of 34,075 different DR-lesions was used to construct a segmentation model, with performance subsequently compared to another retinal specialist. Second, we constructed a 5-step classification model using a data set of 31,325 expert-annotated retinal 6-field images and evaluated if performance was improved with the integration of presegmentation given by the segmentation model. RESULTS: The segmentation model had higher average sensitivity across all abnormalities compared to the retinal expert (0.68 and 0.62) at a comparable average F1-score (0.60 and 0.62). Model sensitivity for microaneurysms, retinal hemorrhages and intraretinal microvascular abnormalities was higher by 42.5%, 8.8%, and 67.5% and F1-scores by 15.8%, 6.5%, and 12.5%, respectively. When presegmentation was included, grading performance increased by 29.7%, 6.0%, and 4.5% for average per class accuracy, quadratic weighted kappa, and multiclass macro area under the curve, with values of 70.4%, 0.90, and 0.92, respectively. CONCLUSIONS: The segmentation model matched an expert in detecting retinal abnormalities, and presegmentation substantially improved accuracy of the automated classification model. TRANSLATIONAL RELEVANCE: Presegmentation may yield more accurate automated DR grading models and increase interpretability and trust in model decisions. The Association for Research in Vision and Ophthalmology 2022-06-22 /pmc/articles/PMC9233290/ /pubmed/35731541 http://dx.doi.org/10.1167/tvst.11.6.19 Text en Copyright 2022 The Authors https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License. |
spellingShingle | Article Andersen, Jakob K. H. Hubel, Martin S. Rasmussen, Malin L. Grauslund, Jakob Savarimuthu, Thiusius R. Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification |
title | Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification |
title_full | Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification |
title_fullStr | Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification |
title_full_unstemmed | Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification |
title_short | Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification |
title_sort | automatic detection of abnormalities and grading of diabetic retinopathy in 6-field retinal images: integration of segmentation into classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9233290/ https://www.ncbi.nlm.nih.gov/pubmed/35731541 http://dx.doi.org/10.1167/tvst.11.6.19 |
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