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1
Enhancements to a computer-
assisted screening technology
for diabetic retinopathy: system
redesign based on our pilot
s...
2
Authors
 Sheila John, Kulasekaran S, Supriti M,
Keerthi Ram, Mohanasankar S, Rajiv Raman,
Badrinath S.S
 Sankara Nethr...
3
Diabetic Retinopathy
(DR) in India
 More than 60 million diabetic people in India
 Prevalence of DR is 18% in diabetic...
4
Diabetic Retinopathy
Screening Model
Ophthalmologist - Based Model Ophthalmologist - Led Model
5
Screening technology for DR
Existing computer-assisted DR screening solutions
 Europe:
UK: iGrading, Portugal: Retmarke...
6
Screening technology for DR
 India Academic research activities at IIT-KGP,
IIIT-Hyd, IIT-Madras, few Engg. Colleges
 ...
7
IITM DR screening system
Normal anatomy detection Clinical signs detection
Red lesion
detectionOptic disc and
macula det...
8
IIT Madras DR screening
system
 Consists of modules for detecting disease signs,
and analytics for providing a referabl...
9
Pilot retrospective study
and observations
 Observed performance on subset of 200 images
of SN-DREAMS Retrospective stu...
10
Pilot retrospective study
and observations
 Needs to handle image gradability and non-
mydriatic imaging
 Separate an...
11
Module for image gradability
Image
preprocessing
Enhanced image
Structure
distribution
Colour
distribution
Contrast Ill...
12
Redesign: module for CSME
 Includes module for accurate localization of
macula and optic disc resilient to presence of...
13
Process flow of the proposed DME grading method
14
Evaluated on 587 images: Sensitivity of 90%
International Clinical Diabetic Macular
Edema Disease Severity Scale
15
Redesign: Module for
Proliferative DR
Diabetes
Proliferative
DR (PDR)
Non-proliferative
DR (NPDR)
• Neovascularization
...
16
Module for proliferative
Diabetic Retinopathy
Module developed with heat map to identify
new vessels /Proliferative vas...
17
Module developed for PDR
identification
Image-level decision of PDR presence
Divide image into
non-overlapping
uniform ...
18
Proliferative Diabetic
Retinopathy
Evaluated on 1052 images
Sensitivity: 85.6% at specificity of 97.3%
19
Summary
 Since diabetic macular edema accounts for more
than 40% of all DR related vision loss, special
module was dev...
20
 Intelligent identification of image gradability
is necessary for the other modules to be
effective, so gradability mo...
21 THANK YOU
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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 by Sheila John, Sankaranetralaya, India

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Enhancements to a Computer : Assisted Screening Technology for Diabetic Retinopathy by Sheila John

  1. 1. 1 Enhancements to a computer- assisted screening technology for diabetic retinopathy: system redesign based on our pilot study in indian setting
  2. 2. 2 Authors  Sheila John, Kulasekaran S, Supriti M, Keerthi Ram, Mohanasankar S, Rajiv Raman, Badrinath S.S  Sankara Nethralaya  Healthcare Technology Innovation Centre, IIT Madras
  3. 3. 3 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 ophthalmologists 1 per 100,000 population  Need: Preventive eye-care through early identification
  4. 4. 4 Diabetic Retinopathy Screening Model Ophthalmologist - Based Model Ophthalmologist - Led Model
  5. 5. 5 Screening technology for DR Existing computer-assisted DR screening solutions  Europe: UK: iGrading, Portugal: Retmarker  Americas: US: IDx-DR, Canada: CARA  State of the art performance: sensitivity 97% at 47% specificity † † Retinal imaging and image analysis, Abramoff et al, IEEE rev. Biomed. Engg, 2010
  6. 6. 6 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
  7. 7. 7 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
  8. 8. 8 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 settings Refinements to algorithms for working in Indian settings : 85.9% sensitivity at 83% specificity
  9. 9. 9 Pilot retrospective study and observations  Observed performance on subset of 200 images of SN-DREAMS Retrospective study  Mydriatic, 45 degree Retinal images  Includes images with media opacity, severe pathology, and lower quality of image capture, for observing performance  Grading by ophthalmologist following ICDR guideline – 5 severity levels
  10. 10. 10 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
  11. 11. 11 Module for image gradability Image 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
  12. 12. 12 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
  13. 13. 13 Process flow of the proposed DME grading method
  14. 14. 14 Evaluated on 587 images: Sensitivity of 90% International Clinical Diabetic Macular Edema Disease Severity Scale
  15. 15. 15 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, …
  16. 16. 16 Module for proliferative Diabetic Retinopathy Module developed with heat map to identify new vessels /Proliferative vascular abnormality and retinal detachment
  17. 17. 17 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 1 2 3 4 5
  18. 18. 18 Proliferative Diabetic Retinopathy Evaluated on 1052 images Sensitivity: 85.6% at specificity of 97.3%
  19. 19. 19 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
  20. 20. 20  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
  21. 21. 21 THANK YOU

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