Cornea Detection and CNV Grading

This work proposes a robust automated approach to grade Cornea NeoVascularization (CNV) disease based on in-growth vessels. The figure describes the whole automated process. The intuition behind our work is to predict the grade of the corresponding cornea using vessel specific features and a regression network. The first part of our algorithm is to separate the cornea region from other parts of the mice image. While the second part is to learn a regression network how to predict a class or a grade of the disease. For the first part, we utilize Mask R-CNN, the state of the art deep learning network in biomedical segmentation, to detect the cornea region. A set of mice images have been selected and annotated to train the Mask R-CNN. As a result, a binary mask is produced, the white region represents the cornea and the black region represents the background that cover all other parts such as eyelid and lashes. Eliminating cornea's outside region, decreases the errors that can affected by texture and color of those parts and produces more robust classifier. However, mask R-CNN binary mask result is not always a proper circle. For this reason, we fitted a circle on our binary mask result to produce an optimal circular mask. The raw image is masked out using the circular binary mask to produce the extracted cornea region. A set of vessel specific features have been generated based on multiscale Hessian eigenvalues, intensity, oriented second derivatives, and multiscale line detector responses along with a random forest classifier. Random forest algorithm is a supervised statistical classifier that needs to be trained first using a set of cornea images with corresponding grades. The images are divided to 5 grades: No CNV Naive (0), No CNV (1), Mild CNV (2), Moderate CNV (3), and Severe CNV (4). We trained a regression network to learn random forest how to grade images based on the generated features. As a result, we utilize the trained random forest regression model to produce the grades of our testing data. The testing data are a set of images that have been kept aside to assess the quality of our automated learning.

How to use CNV

There are Three main folders in our repository:

Src: contains the algorithm scripts. Data: contains input images. Output: contains results, intermediate results and some mat files that are needed by our algorithm.

There are two parts for this software in Src folder, you can skip Part 1 (Cornea Detection) if you already have your cornea extracted in 400x400 image size dimention.

Part 1 --> Cornea Detection: Extract the cornea from mice raw images using Mask R-CNN

Part 2 --> RF CNV Grading: Run the classifier to grade the CNV disease.

In both parts, there are readme file that describes the needed steps. The description is also placed here

Part 1 : Cornea detection

To get cornea detection

1. Put your raw images in a folder called input, the images should be placed as this example:

input\Extreme\ET_101_Day 21_04.16.2015\image1.png

2. Run Main_cornea_data_preparation.m

This script will prepare your input for Mask R-CNN detection.

3. Setup Mask R-CNN using this website: Mask_RCNN

Put the mosaic_cornea_weights in log folder, and put your Output\stage_test folder in the same folder with the nucleus example and run nucleus.py

4. Take the detection masks and place them in

Output/Output_from_MaskRCNN_masks

To treat the results generated from mask R-CNN by fitting a circle on mask R-CNN results, you should run 5, and the results will be ready in Output/Classify_me_circles folder for RF CNV Grading

5.fit_circles_to_maskrcnn_masks_results.m

This script uses Pratt method to fit the cicle

Part 2 : RF CNV Grading

1. Put your output from Cornea detection which is located in Output\Classify_me_circles in a folder called images as this example:

Output\images\test\ET_101_Day 21_04.16.2015\ET_101_Day 21_04.16.2015_Image1_Ex

If you already have your cornea images extracted, you can directly put them inside the folder and run 2

2. Run Grade_cornea_run_me.m

Project Collaborators and Contact

Author: Yasmin M. Kassim, Suneel Gupta, Rajiv Mohan and Kannappan Palaniappan

Copyright © 2022-2024. Yasmin Kassim, Prof. K. Palaniappan and Curators of the University of Missouri, a public corporation. All Rights Reserved.

Created by: Yasmin Kassim
Department of Electrical Engineering and Computer Science,
University of Missouri-Columbia

For more information, contact:

• Yasmin Kassim
  226 Naka Hall (EBW)
  University of Missouri-Columbia
  Columbia, MO 65211
  ymkgz8@mail.missouri.edu

• Dr. K. Palaniappan
  205 Naka Hall (EBW)
  University of Missouri-Columbia
  Columbia, MO 65211
  palaniappank@missouri.edu