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VMseg: Using spatial variance to automatically segment retinal non-perfusion on OCT-angiography.

Hugo LE Boite1,2, Aude Couturier1,2, Ramin Tadayoni1,2

  • 1Université Paris Cité, Paris, France.

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|August 7, 2024
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VMseg, a new algorithm, accurately segments retinal non-perfusion in widefield OCT-Angiography images for diabetic patients. This automated method aids in estimating the non-perfusion index, crucial for managing diabetic retinopathy.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Biology

Background:

  • Diabetic retinopathy is a leading cause of vision loss.
  • Retinal non-perfusion is a key indicator of diabetic retinopathy severity.
  • Accurate segmentation of non-perfusion is essential for monitoring disease progression.

Purpose of the Study:

  • To develop and validate VMseg, an automated algorithm for segmenting retinal non-perfusion.
  • To estimate the non-perfusion index in diabetic patients using widefield OCT-Angiography (OCT-A).
  • To assess the performance of VMseg against manual segmentation by a retina expert.

Main Methods:

  • Developed VMseg, a Python algorithm utilizing convolution and morphological operations on variance maps.
  • Trained and fine-tuned VMseg parameters on 70% of the dataset (development set).
  • Evaluated VMseg performance on the remaining 30% (test set) by comparing with expert manual segmentation.

Main Results:

  • VMseg achieved a mean Dice coefficient of 0.683 on the test set.
  • A strong correlation (rs = 0.877) was observed between VMseg and expert-estimated non-perfusion indexes.
  • The algorithm demonstrated higher accuracy on images with larger areas of retinal non-perfusion.

Conclusions:

  • VMseg is an effective, automated algorithm for retinal non-perfusion segmentation in widefield OCT-A images.
  • The algorithm is computationally efficient, segmenting images in full resolution.
  • VMseg provides accurate non-perfusion index estimation for diabetic retinopathy patients.