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Published February 7, 2023 | Version 1.1
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Codes for Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images with Low Contrast Sclerocho-roidal Junction Using Deep Learning

Creators

  • 1. Durham University
  • 2. Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  • 3. Department of Medical Physics and Biomedical Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
  • 4. Retina Ward, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.

Description

This project aims to calculate Choroid Vascularity Index (CVI) in optical coherenece tomography (OCT) images, using loss modified U-Net. The method is detailed in "Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images low contrast sclerochoroidal junction Using Deep Learning".

 

You can use or define your network in CVI_net.py. Two baseline network has been provided in CVI_net.py to use for training. For each network, a test file (CVI_net_just test data.py) and two model (saved model for raster data.h5 and saved model for EDI data.h5) have been provided using saved weights for more simplifications.

Notes

Please cite this paper if you use the codes: "Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images low contrast sclerochoroidal junction Using Deep Learning"

Files

OCT-CVI-DeepLearning-main.zip

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