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Automated and ImageJ thresholding algorithm-based analysis of macular vessel density in diabetic patients

PURPOSE: To assess the macular vessel density (VD) on optical coherence tomography angiography (OCT-A) using proprietary software (automated) and image processing software (manual) in diabetic patients. METHODS: In a retrospective study, OCT-A images (Triton, TOPCON Inc.) of type 2 diabetics present...

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Autores principales: Kumawat, Devesh, Chawla, Rohan, Shah, Pooja, Sharma, Anu, Sachan, Anusha, Pandey, Veena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9359289/
https://www.ncbi.nlm.nih.gov/pubmed/35647980
http://dx.doi.org/10.4103/ijo.IJO_74_22
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author Kumawat, Devesh
Chawla, Rohan
Shah, Pooja
Sharma, Anu
Sachan, Anusha
Pandey, Veena
author_facet Kumawat, Devesh
Chawla, Rohan
Shah, Pooja
Sharma, Anu
Sachan, Anusha
Pandey, Veena
author_sort Kumawat, Devesh
collection PubMed
description PURPOSE: To assess the macular vessel density (VD) on optical coherence tomography angiography (OCT-A) using proprietary software (automated) and image processing software (manual) in diabetic patients. METHODS: In a retrospective study, OCT-A images (Triton, TOPCON Inc.) of type 2 diabetics presenting to a tertiary eye care center in North India between January 2018 and December 2019 with or without nonproliferative diabetic retinopathy (NPDR) and with no macular edema were analyzed. Macular images of size 3 × 3 mm were binarized with global thresholding algorithms (ImageJ software). Outcome measures were superficial capillary plexus VD (SCP-VD, automated and manual), deep capillary plexus VD (DCP-VD, manual), and correlation between automated and manual SCP-VD. RESULTS: OCT-A images of 89 eyes (55 patients) were analyzed: no diabetic retinopathy (NoDR): 29 eyes, mild NPDR: 29 eyes, and moderate NPDR: 31 eyes. Automated SCP-VD did not differ between NoDR and mild NPDR (P = 0.69), but differed between NoDR and moderate NPDR (P = 0.014) and between mild and moderate NPDR (P = 0.033). Manual SCP-VD (Huang and Otsu methods) did not differ between the groups. Manual DCP-VD differed between NoDR and mild NPDR and between NoDR and moderate NPDR, but not between mild and moderate NPDR with both Huang (P = 0.024, 0.003, and 0.51, respectively) and Otsu (P = 0.021, 0.006, and 0.43, respectively) methods. Automated SCP-VD correlated moderately with manual SCP-VD using Huang method (r = 0.51, P < 0.001) with a mean difference of −0.01% (agreement limits from −6.60% to +6.57%). CONCLUSION: DCP-VD differs consistently between NoDR and NPDR with image processing, while SCP-VD shows variable results. Different thresholding algorithms provide different results, and there is a need to establish consensus on the most suited algorithm.
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spelling pubmed-93592892022-08-10 Automated and ImageJ thresholding algorithm-based analysis of macular vessel density in diabetic patients Kumawat, Devesh Chawla, Rohan Shah, Pooja Sharma, Anu Sachan, Anusha Pandey, Veena Indian J Ophthalmol Special Focus, Retina, Original Article PURPOSE: To assess the macular vessel density (VD) on optical coherence tomography angiography (OCT-A) using proprietary software (automated) and image processing software (manual) in diabetic patients. METHODS: In a retrospective study, OCT-A images (Triton, TOPCON Inc.) of type 2 diabetics presenting to a tertiary eye care center in North India between January 2018 and December 2019 with or without nonproliferative diabetic retinopathy (NPDR) and with no macular edema were analyzed. Macular images of size 3 × 3 mm were binarized with global thresholding algorithms (ImageJ software). Outcome measures were superficial capillary plexus VD (SCP-VD, automated and manual), deep capillary plexus VD (DCP-VD, manual), and correlation between automated and manual SCP-VD. RESULTS: OCT-A images of 89 eyes (55 patients) were analyzed: no diabetic retinopathy (NoDR): 29 eyes, mild NPDR: 29 eyes, and moderate NPDR: 31 eyes. Automated SCP-VD did not differ between NoDR and mild NPDR (P = 0.69), but differed between NoDR and moderate NPDR (P = 0.014) and between mild and moderate NPDR (P = 0.033). Manual SCP-VD (Huang and Otsu methods) did not differ between the groups. Manual DCP-VD differed between NoDR and mild NPDR and between NoDR and moderate NPDR, but not between mild and moderate NPDR with both Huang (P = 0.024, 0.003, and 0.51, respectively) and Otsu (P = 0.021, 0.006, and 0.43, respectively) methods. Automated SCP-VD correlated moderately with manual SCP-VD using Huang method (r = 0.51, P < 0.001) with a mean difference of −0.01% (agreement limits from −6.60% to +6.57%). CONCLUSION: DCP-VD differs consistently between NoDR and NPDR with image processing, while SCP-VD shows variable results. Different thresholding algorithms provide different results, and there is a need to establish consensus on the most suited algorithm. Wolters Kluwer - Medknow 2022-06 2022-05-31 /pmc/articles/PMC9359289/ /pubmed/35647980 http://dx.doi.org/10.4103/ijo.IJO_74_22 Text en Copyright: © 2022 Indian Journal of Ophthalmology https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Special Focus, Retina, Original Article
Kumawat, Devesh
Chawla, Rohan
Shah, Pooja
Sharma, Anu
Sachan, Anusha
Pandey, Veena
Automated and ImageJ thresholding algorithm-based analysis of macular vessel density in diabetic patients
title Automated and ImageJ thresholding algorithm-based analysis of macular vessel density in diabetic patients
title_full Automated and ImageJ thresholding algorithm-based analysis of macular vessel density in diabetic patients
title_fullStr Automated and ImageJ thresholding algorithm-based analysis of macular vessel density in diabetic patients
title_full_unstemmed Automated and ImageJ thresholding algorithm-based analysis of macular vessel density in diabetic patients
title_short Automated and ImageJ thresholding algorithm-based analysis of macular vessel density in diabetic patients
title_sort automated and imagej thresholding algorithm-based analysis of macular vessel density in diabetic patients
topic Special Focus, Retina, Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9359289/
https://www.ncbi.nlm.nih.gov/pubmed/35647980
http://dx.doi.org/10.4103/ijo.IJO_74_22
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