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Related Experiment Video

Updated: Aug 3, 2025

Doppler Optical Coherence Tomography of Retinal Circulation
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CNV-Net: Segmentation, Classification and Activity Score Measurement of Choroidal Neovascularization (CNV) Using

Mahsa Vali1, Behzad Nazari1, Saeed Sadri1

  • 1Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.

Diagnostics (Basel, Switzerland)
|April 13, 2023
PubMed
Summary

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An AI algorithm automates Choroidal Neovascularization (CNV) segmentation and activity assessment in OCTA images. This tool enables reliable detection and objective evaluation of CNV features without contrast agents.

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Choroidal Neovascularization (CNV) is a leading cause of vision loss.
  • Accurate segmentation and activity assessment of CNV are crucial for effective treatment.
  • Current methods may require contrast agents and subjective interpretation.

Purpose of the Study:

  • To develop an AI-based algorithm for automated segmentation of CNV.
  • To identify CNV activity criteria (branching, peripheral arcade, dark halo, shape, loop, anastomoses) in OCTA images.
  • To enable objective and repeatable assessment of CNV features.

Main Methods:

  • A retrospective study of 130 OCTA images from 101 treatment-naïve CNV patients.
  • Development of a two-step AI algorithm: U-Net for segmentation, five binary classifiers for activity criteria.
Keywords:
activity score measurementchoroidal neovascularisationclassificationoptical coherence tomography angiographysegmentation

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  • Utilized modified U-Net and transfer learning for network training.

    • Main Results:

    • The segmentation network achieved an average Dice coefficient of 0.86.
    • Classifiers for activity criteria showed accuracies ranging from 0.81 to 0.86.
    • The AI algorithm demonstrated reliable CNV detection and segmentation from OCTA alone.

    Conclusions:

    • The AI algorithm offers a reliable, contrast-agent-free method for CNV segmentation and activity assessment.
    • The developed tool facilitates objective and repeatable evaluation of CNV characteristics.
    • This AI approach has the potential to improve clinical management of CNV.