mmsegmentation/projects/medical/2d_image/ct/cranium

Brain CT Images with Intracranial Hemorrhage Masks (Cranium)

Description

This project supports Brain CT Images with Intracranial Hemorrhage Masks (Cranium), which can be downloaded from here.

Dataset Overview

This dataset consists of head CT (Computed Thomography) images in jpg format. There are 2500 brain window images and 2500 bone window images, for 82 patients. There are approximately 30 image slices per patient. 318 images have associated intracranial image masks. Also included are csv files containing hemorrhage diagnosis data and patient data. This is version 1.0.0 of this dataset. A full description of this dataset as well as updated versions can be found here: https://physionet.org/content/ct-ich/1.0.0/

Statistic Information

Dataset Name	Anatomical Region	Task Type	Modality	Num. Classes	Train/Val/Test Images	Train/Val/Test Labeled	Release Date	License
Cranium	head_and_neck	segmentation	ct	2	2501/-/-	yes/-/-	2020	CC-BY 4.0

Class Name	Num. Train	Pct. Train	Num. Val	Pct. Val	Num. Test	Pct. Test
background	2501	99.93	-	-	-	-
hemorrhage	318	0.07	-	-	-	-

Note:

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

Visualization

Dataset Citation

@article{hssayeni2020computed,
	title={Computed tomography images for intracranial hemorrhage detection and segmentation},
	author={Hssayeni, Murtadha and Croock, MS and Salman, AD and Al-khafaji, HF and Yahya, ZA and Ghoraani, B},
	journal={Intracranial Hemorrhage Segmentation Using A Deep Convolutional Model. Data},
	volume={5},
	number={1},
	pages={179},
	year={2020}
}

Prerequisites

• Python v3.8
• PyTorch v1.10.0
• pillow(PIL) v9.3.0 9.3.0
• scikit-learn(sklearn) v1.2.0 1.2.0
• 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 cranium/ 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
  │   │   │   ├── ct
  │   │   │   │   ├── cranium
  │   │   │   │   │   ├── 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	2000	99.93	501	99.92	-	-
hemorrhage	260	0.07	260	0.08	-	-

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.