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README.md

RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging

Description

This project support RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging, and the dataset used in this project can be downloaded from here.

Dataset Overview

The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvascular system is of primary involvement in such conditions, and the retina is the only anatomical site where the microvasculature can be directly observed. The objective assessment of retinal vessels has long been considered a surrogate biomarker for systemic vascular diseases, and with recent advancements in retinal imaging and computer vision technologies, this topic has become the subject of renewed attention. In this paper, we present a novel dataset, dubbed RAVIR, for the semantic segmentation of Retinal Arteries and Veins in Infrared Reflectance (IR) imaging. It enables the creation of deep learning-based models that distinguish extracted vessel type without extensive post-processing.

Original Statistic Information

Dataset name	Anatomical region	Task type	Modality	Num. Classes	Train/Val/Test Images	Train/Val/Test Labeled	Release Date	License
Ravir	eye	segmentation	infrared reflectance imaging	3	23/-/19	yes/-/-	2022	CC-BY-NC 4.0

Class Name	Num. Train	Pct. Train	Num. Val	Pct. Val	Num. Test	Pct. Test
background	23	87.22	-	-	-	-
artery	23	5.45	-	-	-	-
vein	23	7.33	-	-	-	-

Note:

Pct means percentage of pixels in this category in all pixels.

Visualization

Dataset Citation

@article{hatamizadeh2022ravir,
  title={RAVIR: A dataset and methodology for the semantic segmentation and quantitative analysis of retinal arteries and veins in infrared reflectance imaging},
  author={Hatamizadeh, Ali and Hosseini, Hamid and Patel, Niraj and Choi, Jinseo and Pole, Cameron C and Hoeferlin, Cory M and Schwartz, Steven D and Terzopoulos, Demetri},
  journal={IEEE Journal of Biomedical and Health Informatics},
  volume={26},
  number={7},
  pages={3272--3283},
  year={2022},
  publisher={IEEE}
}

Prerequisites

Python v3.8
PyTorch v1.10.0
pillow(PIL) v9.3.0
scikit-learn(sklearn) v1.2.0
MIM v0.3.4
MMCV v2.0.0rc4
MMEngine v0.2.0 or higher
MMSegmentation v1.0.0rc5

All the commands below rely on the correct configuration of PYTHONPATH, which should point to the project's directory so that Python can locate the module files. In ravir/ root directory, run the following line to add the current directory to PYTHONPATH:

export PYTHONPATH=`pwd`:$PYTHONPATH

Dataset preparing

download dataset from here and decompression data to path 'data/ravir/'.
run script "python tools/prepare_dataset.py" to split dataset and change folder structure as below.
run script "python ../../tools/split_seg_dataset.py --data_root data/ravir" to split dataset and generate train.txt, val.txt and test.txt. If the label of official validation set and test set can't be obtained, we generate train.txt and val.txt from the training set randomly.

  mmsegmentation
  ├── mmseg
  ├── projects
  │   ├── medical
  │   │   ├── 2d_image
  │   │   │   ├── infrared_reflectance_imaging
  │   │   │   │   ├── ravir
  │   │   │   │   │   ├── configs
  │   │   │   │   │   ├── datasets
  │   │   │   │   │   ├── tools
  │   │   │   │   │   ├── data
  │   │   │   │   │   │   ├── train.txt
  │   │   │   │   │   │   ├── val.txt
  │   │   │   │   │   │   ├── images
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png
  │   │   │   │   │   │   │   ├── test
  │   │   │   │   |   │   │   │   ├── yyy.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── yyy.png
  │   │   │   │   │   │   ├── masks
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png

Divided Dataset Information

Note: The table information below is divided by ourselves.

Class Name	Num. Train	Pct. Train	Num. Val	Pct. Val	Num. Test	Pct. Test
background	18	87.41	5	86.53	-	-
artery	18	5.44	5	5.50	-	-
vein	18	7.15	5	7.97	-	-

Training commands

To train models on a single server with one GPU. (default）

mim train mmseg ./configs/${CONFIG_PATH}

Testing commands