FAZSeg: A New Software for Quantification of the Foveal Avascular Zone
Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the...
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Published in | Clinical ophthalmology (Auckland, N.Z.) Vol. 15; pp. 4817 - 4827 |
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ISSN | 1177-5483 1177-5467 1177-5483 |
DOI | 10.2147/OPTH.S346145 |
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Abstract | Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ's deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation).
Ninety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6mm x 6mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420×420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth.
The age distribution for all 93 subjects was 28.02 ± 10.79 (range, 10.0-66.0) years. For normal subjects mean ± SD age distribution was 32.13 ± 16.27 years. Similarly, the myopia age distribution was 26.06 ± 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98.
A clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device's inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online. |
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AbstractList | VK Viekash,1 Janarthanam Jothi Balaji,2 Vasudevan Lakshminarayanan3 1Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India; 2Department of Optometry, Medical Research Foundation, Chennai, Tamil Nadu, 600 006, India; 3Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON, N2L 3G1, CanadaCorrespondence: Janarthanam Jothi Balaji Tel +91 44-42271500Email jothibalaji@gmail.comIntroduction: Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ’s deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation).Methods: Ninety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6mm x 6mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420× 420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth.Results: The age distribution for all 93 subjects was 28.02 ± 10.79 (range, 10.0– 66.0) years. For normal subjects mean ± SD age distribution was 32.13 ± 16.27 years. Similarly, the myopia age distribution was 26.06 ± 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98.Conclusion: A clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device’s inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online.Keywords: image processing, foveal avascular zone, optical coherence tomography angiography, superficial retinal layer, deep retinal layer Introduction: Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ's deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation). Methods: Ninety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6 mm x 6 mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420x420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth. Results: The age distribution for all 93 subjects was 28.02 [+ or -] 10.79 (range, 10.0-66.0) years. For normal subjects mean [+ or -] SD age distribution was 32.13 [+ or -] 16.27 years. Similarly, the myopia age distribution was 26.06 [+ or -] 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98. Conclusion: A clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device's inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online. Keywords: image processing, foveal avascular zone, optical coherence tomography angiography, superficial retinal layer, deep retinal layer Introduction: Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ’s deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation). Methods: Ninety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6mm x 6mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420× 420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth. Results: The age distribution for all 93 subjects was 28.02 ± 10.79 (range, 10.0– 66.0) years. For normal subjects mean ± SD age distribution was 32.13 ± 16.27 years. Similarly, the myopia age distribution was 26.06 ± 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98. Conclusion: A clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device’s inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online. Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ's deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation).INTRODUCTIONVarious ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ's deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation).Ninety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6mm x 6mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420×420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth.METHODSNinety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6mm x 6mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420×420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth.The age distribution for all 93 subjects was 28.02 ± 10.79 (range, 10.0-66.0) years. For normal subjects mean ± SD age distribution was 32.13 ± 16.27 years. Similarly, the myopia age distribution was 26.06 ± 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98.RESULTSThe age distribution for all 93 subjects was 28.02 ± 10.79 (range, 10.0-66.0) years. For normal subjects mean ± SD age distribution was 32.13 ± 16.27 years. Similarly, the myopia age distribution was 26.06 ± 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98.A clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device's inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online.CONCLUSIONA clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device's inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online. Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ's deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation). Ninety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6mm x 6mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420×420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth. The age distribution for all 93 subjects was 28.02 ± 10.79 (range, 10.0-66.0) years. For normal subjects mean ± SD age distribution was 32.13 ± 16.27 years. Similarly, the myopia age distribution was 26.06 ± 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98. A clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device's inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online. |
Audience | Academic |
Author | Jothi Balaji, Janarthanam Lakshminarayanan, Vasudevan Viekash, VK |
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Cites_doi | 10.1371/journal.pone.0184948 10.1117/12.2544817 10.1515/chilat-2016-0014 10.1177/2474126418778492 10.1117/12.2543906 10.1111/j.1475-1313.2007.00475.x 10.1038/s41433-019-0438-7 10.1167/iovs.18-25957 10.1167/iovs.11-8488 10.1167/tvst.6.3.16 10.1097/IIO.0000000000000249 10.1117/12.2610713 10.1371/journal.pone.0219785 10.1016/j.ajo.2014.10.032 10.1038/s41433-020-0824-1 10.1001/jamaophthalmol.2019.4821 10.1097/OPX.0b013e3182504227 10.1371/journal.pone.0212364 10.1016/S0161-6420(99)00732-0 10.4103/njcp.njcp_183_16 10.1167/iovs.15-18869 10.1117/12.2594152 |
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Keywords | image processing deep retinal layer foveal avascular zone superficial retinal layer optical coherence tomography angiography |
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Snippet | Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment... Introduction: Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is... VK Viekash,1 Janarthanam Jothi Balaji,2 Vasudevan Lakshminarayanan3 1Department of Instrumentation and Control Engineering, National Institute of Technology,... |
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StartPage | 4817 |
SubjectTerms | Algorithms Automation Biometrics Cornea Diabetic retinopathy Eye diseases foveal avascular zone Image processing Medical imaging optical coherence tomography angiography Original Research Public software Software superficial retinal layer and deep retinal layer Tomography |
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Title | FAZSeg: A New Software for Quantification of the Foveal Avascular Zone |
URI | https://www.ncbi.nlm.nih.gov/pubmed/34992342 https://www.proquest.com/docview/2620222626 https://www.proquest.com/docview/2618230787 https://pubmed.ncbi.nlm.nih.gov/PMC8714006 https://doaj.org/article/632251418b404b5aa459d054b65939e4 |
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