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Quantitative Assessment of Retinopathy Using Multi-parameter Image Analysis.

Zahra Ghanian1, Kevin Staniszewski1, Nasim Jamali2

  • 1Biophotonics Laboratory, University of Wisconsin Milwaukee, Department of Electrical Engineering and Computer Science, 3200 N Cramer St., Milwaukee, WI 53211-3029, USA.

Journal of Medical Signals and Sensors
|May 18, 2016
PubMed
Summary
This summary is machine-generated.

Diabetic retinopathy detection is improved using a new multi-parameter method to quantify retinal vascular injuries. This approach monitors early structural changes and disease progression in microscopic images.

Keywords:
Classificationfluorescence microscopyfractalsimage cytometryretinopathysegmentation

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Biology

Background:

  • Diabetic retinopathy is a leading cause of vision loss.
  • Early detection and monitoring of retinal vascular changes are crucial for managing diabetic retinopathy.
  • Existing methods for quantifying retinal vascular injuries can be limited in scope and throughput.

Purpose of the Study:

  • To develop and validate a multi-parameter quantification method for retinal vascular injuries in microscopic images.
  • To apply this method to assess early structural changes and disease progression in diabetic retinopathy models.
  • To provide a high-throughput and reproducible tool for evaluating retinopathy.

Main Methods:

  • A multi-parameter quantification method was developed.
  • The method was applied to wholemount retinal trypsin digest images from diabetic Akita/+ and bcl-2 knockout mice models.
  • Five unique features of retinal vasculature were extracted: cell count, endothelial cell to pericyte ratio, fractal dimension, vessel coverage, and acellular capillaries.
  • The approach was validated using retinal image simulations.

Main Results:

  • Diabetic retinas exhibited significantly fewer cells (P = 5.1205e-05) compared to normal retinas.
  • A greater population ratio of endothelial cells to pericytes (P = 5.1772e-04) was observed, indicating pericyte loss.
  • Higher fractal dimension (P = 8.2202e-05) and a greater number of acellular capillaries (P = 7.0414e-04) were found in diabetic retinas.
  • Smaller vessel coverage (P = 1.4214e-05) was also a characteristic of diabetic retinas.

Conclusions:

  • The developed multi-parameter quantification method accurately assesses retinal vascular injuries.
  • This method effectively monitors early structural changes and disease progression in diabetic retinopathy.
  • The approach offers a high-throughput and reproducible means for evaluating physiological and pathological retinopathy.