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Quantitative analysis of functional filtering bleb size using Mask R-CNN.

Tao Wang1, Lei Zhong1, Jing Yuan2

  • 1Department of Ophthalmology, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.

Annals of Translational Medicine
|July 4, 2020
PubMed
Summary

Deep learning with Mask R-CNN accurately quantifies functional filtering bleb size after trabeculectomy. Smaller bleb size strongly correlates with higher intraocular pressure (IOP), aiding post-surgical monitoring.

Keywords:
GlaucomaMask R-CNNdeep learningfiltering blebtrabeculectomy

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Deep learning is increasingly impactful in medical fields, including ophthalmology.
  • Quantitative analysis of functional filtering blebs is crucial for post-surgical assessment.

Purpose of the Study:

  • To quantitatively analyze functional filtering bleb size using Mask R-CNN.
  • To evaluate the correlation between functional filtering bleb area and intraocular pressure (IOP).

Main Methods:

  • An observational study used 83 images of post-trabeculectomy functional filtering blebs.
  • A Mask R-CNN model was trained on 70 images and evaluated on 13 images.
  • Data augmentation included image-flipping; IBAGS scoring was used.

Main Results:

  • The Mask R-CNN model achieved over 93% IoU on the test set.
  • Functional filtering blebs showed high incidence of E1/E2, H1/H2, and V0/V1 based on IBAGS.
  • A significant negative correlation (r=-0.757, P<0.05) was found between bleb area and IOP.

Conclusions:

  • Deep learning, specifically Mask R-CNN, is effective for quantitative analysis of functional filtering bleb size.
  • This technique shows promise for monitoring post-trabeculectomy filtering blebs.