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 ([email protected])
• Vismay Agrawal ([email protected])