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Geographic Atrophy Segmentation Using Multimodal Deep Learning
PURPOSE: To examine deep learning (DL)–based methods for accurate segmentation of geographic atrophy (GA) lesions using fundus autofluorescence (FAF) and near-infrared (NIR) images. METHODS: This retrospective analysis utilized imaging data from study eyes of patients enrolled in Proxima A and B (NC...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10341294/ https://www.ncbi.nlm.nih.gov/pubmed/37428131 http://dx.doi.org/10.1167/tvst.12.7.10 |
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author | Spaide, Theodore Jiang, Jiaxiang Patil, Jasmine Anegondi, Neha Steffen, Verena Kawczynski, Michael G. Newton, Elizabeth M. Rabe, Christina Gao, Simon S. Lee, Aaron Y. Holz, Frank G. Sadda, SriniVas Schmitz-Valckenberg, Steffen Ferrara, Daniela |
author_facet | Spaide, Theodore Jiang, Jiaxiang Patil, Jasmine Anegondi, Neha Steffen, Verena Kawczynski, Michael G. Newton, Elizabeth M. Rabe, Christina Gao, Simon S. Lee, Aaron Y. Holz, Frank G. Sadda, SriniVas Schmitz-Valckenberg, Steffen Ferrara, Daniela |
author_sort | Spaide, Theodore |
collection | PubMed |
description | PURPOSE: To examine deep learning (DL)–based methods for accurate segmentation of geographic atrophy (GA) lesions using fundus autofluorescence (FAF) and near-infrared (NIR) images. METHODS: This retrospective analysis utilized imaging data from study eyes of patients enrolled in Proxima A and B (NCT02479386; NCT02399072) natural history studies of GA. Two multimodal DL networks (UNet and YNet) were used to automatically segment GA lesions on FAF; segmentation accuracy was compared with annotations by experienced graders. The training data set comprised 940 image pairs (FAF and NIR) from 183 patients in Proxima B; the test data set comprised 497 image pairs from 154 patients in Proxima A. Dice coefficient scores, Bland–Altman plots, and Pearson correlation coefficient (r) were used to assess performance. RESULTS: On the test set, Dice scores for the DL network to grader comparison ranged from 0.89 to 0.92 for screening visit; Dice score between graders was 0.94. GA lesion area correlations (r) for YNet versus grader, UNet versus grader, and between graders were 0.981, 0.959, and 0.995, respectively. Longitudinal GA lesion area enlargement correlations (r) for screening to 12 months (n = 53) were lower (0.741, 0.622, and 0.890, respectively) compared with the cross-sectional results at screening. Longitudinal correlations (r) from screening to 6 months (n = 77) were even lower (0.294, 0.248, and 0.686, respectively). CONCLUSIONS: Multimodal DL networks to segment GA lesions can produce accurate results comparable with expert graders. TRANSLATIONAL RELEVANCE: DL-based tools may support efficient and individualized assessment of patients with GA in clinical research and practice. |
format | Online Article Text |
id | pubmed-10341294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-103412942023-07-14 Geographic Atrophy Segmentation Using Multimodal Deep Learning Spaide, Theodore Jiang, Jiaxiang Patil, Jasmine Anegondi, Neha Steffen, Verena Kawczynski, Michael G. Newton, Elizabeth M. Rabe, Christina Gao, Simon S. Lee, Aaron Y. Holz, Frank G. Sadda, SriniVas Schmitz-Valckenberg, Steffen Ferrara, Daniela Transl Vis Sci Technol Retina PURPOSE: To examine deep learning (DL)–based methods for accurate segmentation of geographic atrophy (GA) lesions using fundus autofluorescence (FAF) and near-infrared (NIR) images. METHODS: This retrospective analysis utilized imaging data from study eyes of patients enrolled in Proxima A and B (NCT02479386; NCT02399072) natural history studies of GA. Two multimodal DL networks (UNet and YNet) were used to automatically segment GA lesions on FAF; segmentation accuracy was compared with annotations by experienced graders. The training data set comprised 940 image pairs (FAF and NIR) from 183 patients in Proxima B; the test data set comprised 497 image pairs from 154 patients in Proxima A. Dice coefficient scores, Bland–Altman plots, and Pearson correlation coefficient (r) were used to assess performance. RESULTS: On the test set, Dice scores for the DL network to grader comparison ranged from 0.89 to 0.92 for screening visit; Dice score between graders was 0.94. GA lesion area correlations (r) for YNet versus grader, UNet versus grader, and between graders were 0.981, 0.959, and 0.995, respectively. Longitudinal GA lesion area enlargement correlations (r) for screening to 12 months (n = 53) were lower (0.741, 0.622, and 0.890, respectively) compared with the cross-sectional results at screening. Longitudinal correlations (r) from screening to 6 months (n = 77) were even lower (0.294, 0.248, and 0.686, respectively). CONCLUSIONS: Multimodal DL networks to segment GA lesions can produce accurate results comparable with expert graders. TRANSLATIONAL RELEVANCE: DL-based tools may support efficient and individualized assessment of patients with GA in clinical research and practice. The Association for Research in Vision and Ophthalmology 2023-07-10 /pmc/articles/PMC10341294/ /pubmed/37428131 http://dx.doi.org/10.1167/tvst.12.7.10 Text en Copyright 2023 The Authors https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License. |
spellingShingle | Retina Spaide, Theodore Jiang, Jiaxiang Patil, Jasmine Anegondi, Neha Steffen, Verena Kawczynski, Michael G. Newton, Elizabeth M. Rabe, Christina Gao, Simon S. Lee, Aaron Y. Holz, Frank G. Sadda, SriniVas Schmitz-Valckenberg, Steffen Ferrara, Daniela Geographic Atrophy Segmentation Using Multimodal Deep Learning |
title | Geographic Atrophy Segmentation Using Multimodal Deep Learning |
title_full | Geographic Atrophy Segmentation Using Multimodal Deep Learning |
title_fullStr | Geographic Atrophy Segmentation Using Multimodal Deep Learning |
title_full_unstemmed | Geographic Atrophy Segmentation Using Multimodal Deep Learning |
title_short | Geographic Atrophy Segmentation Using Multimodal Deep Learning |
title_sort | geographic atrophy segmentation using multimodal deep learning |
topic | Retina |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10341294/ https://www.ncbi.nlm.nih.gov/pubmed/37428131 http://dx.doi.org/10.1167/tvst.12.7.10 |
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