https://github.com/koriavinash1/optic-disk-cup-segmentation
Optic Disc and Optic Cup Segmentation using 57 layered deep convolutional neural network
https://github.com/koriavinash1/optic-disk-cup-segmentation
ai artificial-intelligence cdr-prediction deep-convolutional-neural-networks deep-learning fundus-image-analysis glaucoma-detection medical-image-analysis opticdisk-segmentation segmentation
Last synced: 2 months ago
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Optic Disc and Optic Cup Segmentation using 57 layered deep convolutional neural network
- Host: GitHub
- URL: https://github.com/koriavinash1/optic-disk-cup-segmentation
- Owner: koriavinash1
- License: mit
- Created: 2018-07-22T16:59:03.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-12-12T14:19:00.000Z (almost 7 years ago)
- Last Synced: 2025-04-19T07:47:02.359Z (6 months ago)
- Topics: ai, artificial-intelligence, cdr-prediction, deep-convolutional-neural-networks, deep-learning, fundus-image-analysis, glaucoma-detection, medical-image-analysis, opticdisk-segmentation, segmentation
- Language: Jupyter Notebook
- Homepage:
- Size: 1.09 MB
- Stars: 52
- Watchers: 4
- Forks: 23
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Optic-Disk-Cup-Segmentation and Glaucoma Screening
## Introduction
This repository contains the implementation of convolutional neural network for optic disk and cup segmentation and glaucoma screening from given fundus images
# Segmentation Network
## Preprocesing
Images were cropped to nearest square size and re-sized to a dimension of (512, 512). The different lighting conditions and intensity variations among images across various databases were circumvented by perform-ing normalization of the histogram using Contrast Limited Adaptive HistogramEqualization (CLAHE). 2 different images were generated by varying parameters such as clip value & window level while performing CLAHE. Along with CLAHE, spatial co-ordinates information were also provided to thenetwork. This additional information aided in learning relative features (i.e. disklocation with respect to fovea)
## Network Architecture
57 layered deep network was used for segmentation of optic disk and cup. Network architecture is illustrated in figure below...
## Results
### Model predictions
Mask generation used for reducing false positives predicted by network...
Image on left shows raw data and image on left shows model predictions...
# Classification Network
## Preprocessing
The pixel level segmentation of the optic disk and
optic cup was utilized to generate images of dimension (550, 550) centered around the optic disk. 6 different images were generated by varying parameters such as clip value & window level while performing CLAHE.
## Network Architecture
A DenseNet201 & ResNet18 pre-trained on natural images forms the ensemble. The hindmost layer in the network i.e. the classification layer was modified to have 2 neurons. An additional convolutional layer was appended before both the pre-trained models to convert out 21 channel input to 3 channels. To make the network images accept inputs of variable dimension, the global average pooling layer was substituted with an adaptive average pooling layer.
## Results
The proposed classification network achieved a sensitivity of 0.75 at a specificity of 0.85 and 0.856 area under the ROC curve.
## How to use?
~~~~
git clone https://github.com/koriavinash1/Optic-Disk-Cup-Segmentation.git
cd Optic-Disk-Cup-Segmentation
pip install -r requirements.txt
~~~~
## Folder structure
> ./src consists all source codes
> > ./src/segmentation code for all segmentation work
> > ./src/classification code for glaucoma screening
> > Tune parameters and run Main.py for executing task
## Publication
Our paper is available on arXiv(https://arxiv.org/pdf/1809.05216.pdf)
Please cite with the following Bibtex code:
~~~
@article{agrawal2018enhanced,
title={Enhanced Optic Disk and Cup Segmentation with Glaucoma Screening from Fundus Images using Position encoded CNNs},
author={Agrawal, Vismay and Kori, Avinash and Alex, Varghese and Krishnamurthi, Ganapathy},
journal={arXiv preprint arXiv:1809.05216},
year={2018}
}
~~~
If any comments or issues, pull requests/issues are Welcomed....
Thankyou
### Contact
* Avinash Kori (avinashgkori@smail.iitm.ac.in)
* Vismay Agrawal (vismay.iitm@gmail.com)