Enhancements to a Computer : Assisted Screening Technology for Diabetic Retinopathy by Sheila John
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Transcript of Enhancements to a Computer : Assisted Screening Technology for Diabetic Retinopathy by Sheila John
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Enhancements to a computer-assisted screening technology for diabetic retinopathy: system redesign based on our pilot study in indian setting
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Authors
Sheila John, Kulasekaran S, Supriti M, Keerthi Ram, Mohanasankar S, Rajiv Raman,
Badrinath S.S
Sankara Nethralaya
Healthcare Technology Innovation Centre, IIT Madras
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Diabetic Retinopathy (DR) in India
More than 60 million diabetic people in India Prevalence of DR is 18% in diabetic population Significant prevalence in both rural and urban population
Acute shortfall of ophthalmologists1 per 100,000 population
Need: Preventive eye-care through early identification
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Diabetic Retinopathy Screening Model
Ophthalmologist - Based Model Ophthalmologist - Led Model
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Screening technology for DR
Existing computer-assisted DR screening solutionsEurope: UK: iGrading, Portugal: RetmarkerAmericas:US: IDx-DR, Canada: CARAState of the art performance: sensitivity 97% at
47% specificity †
† Retinal imaging and image analysis, Abramoff et al, IEEE rev. Biomed. Engg, 2010
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Screening technology for DR
India Academic research activities at IIT-KGP, IIIT-Hyd, IIT-Madras, few Engg. Colleges
DR screening research activities world-over more than 200 peer-reviewed publications since 2003
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IITM DR screening system
Normal anatomy detection Clinical signs detection
Red lesion detectionOptic disc and
macula detection
Blood vessel segmentation
Bright lesion detection
Small red dots detection
Image gradabilityInput image
Analytics
DR Referral decision
Grading system based on International Clinical Diabetic retinopathy Disease Severity Scale (ICDR) 5 severity levels
Normal anatomy detection Clinical signs detection
Red lesion detectionOptic disc and
macula detection
Blood vessel segmentation
Bright lesion detection
Small red dots detection
Image gradabilityInput image
Analytics
DR Referral decision
Grading system based on International Clinical Diabetic retinopathy Disease Severity Scale (ICDR) 5 severity levels
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IIT Madras DR screening system
Consists of modules for detecting disease signs, and analytics for providing a referable vs non-referable decision
Developed and benchmarked using 2000 publicly available fundus images acquired in clinical settingsRefinements to algorithms for working in Indian settings : 85.9% sensitivity at 83% specificity
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Pilot retrospective study and observations
Observed performance on subset of 200 images of SN-DREAMS Retrospective study
Mydriatic, 45 degree Retinal imagesIncludes images with media opacity, severe
pathology, and lower quality of image capture, for observing performance
Grading by ophthalmologist following ICDR guideline – 5 severity levels
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Pilot retrospective study and observations
Needs to handle image gradability and non-mydriatic imaging
Separate analytics for diabetic macular edema and Proliferative diabetic retinopathy
Designed to find new cases of DR, but also Laser treated cases.
Evaluation of inter-observer variability and consensus should be carried out
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Module for image gradabilityImage
preprocessing
Enhanced image
Structure distribution
Colour distribution
Contrast Illumination SNRHomogeneity Moments
Quality prediction
Gradability score
Reference images for good gradability
Reference images for poor gradability
Quality parameters
Retinal image
Evaluated on 240 images: 82% sensitivity at 80% specificity
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Redesign: module for CSME
Includes module for accurate localization of macula and optic disc resilient to presence of disease signs
Detection of Hard exudates, cotton-wool spots, and identification of circinate
clusters
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Process flow of the proposed DME grading method
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Evaluated on 587 images: Sensitivity of 90%
International Clinical Diabetic Macular Edema Disease Severity Scale
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Redesign: Module for Proliferative DR
Diabetes Proliferative
DR (PDR)Non-proliferative DR
(NPDR)
• Neovascularization • Vitreous hemorrhage• Retinal detachment
Pre-proliferative DR / Severe NPDR
Vision loss
• Microaneurysms• Small hemorrhages• Exudates
• Macular edema • Ischemic regions, …
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Module for proliferative Diabetic Retinopathy
Module developed with heat map to identify new vessels /Proliferative vascular abnormality and retinal detachment
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Module developed for PDR identification
Image-level decision of PDR presence
Divide image into non-overlapping
uniform size patches
Characterize local texture
Derive vascular morphometric
features
Compute a sensitive
vessel map
For each patch
Dense descriptor of
patch
Recognize neovascularity
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Proliferative Diabetic Retinopathy
Evaluated on 1052 imagesSensitivity: 85.6% at specificity of 97.3%
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Summary
Since diabetic macular edema accounts for more than 40% of all DR related vision loss, special
module was developed and evaluated
Misdiagnosis of late stage DR is highly unfavorable and associated vision loss, so detection of PDR was developed, identifying NVE, NVD, Fibro vascular proliferation and retinal
detachment
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Intelligent identification of image gradability is necessary for the other modules to be effective, so gradability module was developed
Algorithm - good sensitivity and specificity to detect presence or absence of DR
Cost effective large scale screening of diabetic patients to prevent blindness in the
population
Summary
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21 THANK YOU