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Chest Radiograph Anatomical Structure Segmentation (CRASS)

Description

This project supports Chest Radiograph Anatomical Structure Segmentation (CRASS) , which can be downloaded from here.

Dataset Overview

A set of consecutively obtained posterior-anterior chest radiograph were selected from a database containing images acquired at two sites in sub Saharan Africa with a high tuberculosis incidence. All subjects were 15 years or older. Images from digital chest radiography units were used (Delft Imaging Systems, The Netherlands) of varying resolutions, with a typical resolution of 1800--2000 pixels, the pixel size was 250 lm isotropic. From the total set of images, 225 were considered to be normal by an expert radiologist, while 333 of the images contained abnormalities. Of the abnormal images, 220 contained abnormalities in the upper area of the lung where the clavicle is located. The data was divided into a training and a test set. The training set consisted of 299 images, the test set of 249 images. The current data is still incomplete and to be added later.

Information Statistics

Dataset Name	Anatomical Region	Task Type	Modality	Num. Classes	Train/Val/Test Images	Train/Val/Test Labeled	Release Date	License
crass	pulmonary	segmentation	x_ray	2	299/-/234	yes/-/no	2021	CC0 1.0

Class Name	Num. Train	Pct. Train	Num. Val	Pct. Val	Num. Test	Pct. Test
background	299	98.38	-	-	-	-
clavicles	299	1.62	-	-	-	-

Note:

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

Visualization

Dataset Citation

@article{HOGEWEG20121490,
	title={Clavicle segmentation in chest radiographs},
	journal={Medical Image Analysis},
	volume={16},
	number={8},
	pages={1490-1502},
	year={2012}
}

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 crass/ root directory, run the following line to add the current directory to PYTHONPATH:

export PYTHONPATH=`pwd`:$PYTHONPATH

Dataset Preparing

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

  mmsegmentation
  ├── mmseg
  ├── projects
  │   ├── medical
  │   │   ├── 2d_image
  │   │   │   ├── x_ray
  │   │   │   │   ├── crass
  │   │   │   │   │   ├── configs
  │   │   │   │   │   ├── datasets
  │   │   │   │   │   ├── tools
  │   │   │   │   │   ├── data
  │   │   │   │   │   │   ├── train.txt
  │   │   │   │   │   │   ├── val.txt
  │   │   │   │   │   │   ├── images
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.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	227	98.38	57	98.39	-	-
clavicles	227	1.62	57	1.61	-	-

Training commands

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

mim train mmseg ./configs/${CONFIG_FILE}

Testing commands

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

mim test mmseg ./configs/${CONFIG_FILE}  --checkpoint ${CHECKPOINT_PATH}

Checklist

Milestone 1: PR-ready, and acceptable to be one of the projects/.
- Finish the code
- Basic docstrings & proper citation
- Test-time correctness
- A full README
Milestone 2: Indicates a successful model implementation.
- Training-time correctness
Milestone 3: Good to be a part of our core package!
- Type hints and docstrings
- Unit tests
- Code polishing
- Metafile.yml
Move your modules into the core package following the codebase's file hierarchy structure.
Refactor your modules into the core package following the codebase's file hierarchy structure.

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