AUCseg:An automatically unsupervised clustering toolbox for 3D-segmentation of High-grade gliomas on multi-parametric MR images

1. Introduction

This is an automatically unsupervised clustering toolbox for 3D-segmentation of High-grade gliomas on multi-parametric MR images.

2. Preprocess

2.1 Skull stripping.

Skull will impact the segmentation result.You can do this step by FSL using the folowing command.

bet <input> <output> [options]

2.2 Registration

Our method will use the multi-parametric MR images, so we need register them. In theory, linear registration is enough.

3. Requirements

You can install the external package by the folowing command.

cd project_path
pip install -r requirements.txt

4. How to use

you can use the folowing command to get the user guide.

seg_all_labels.py --help

You can run the examples by the folowing command to segment the whole tumor, tumor core, and enhancing tumor.

python seg_all_labels.py -t1ce ./data/Brats18_TCIA02_151_1/Brats18_TCIA02_151_1_t1ce.nii.gz -flair ./data/Brats18_TCIA02_151_1/Brats18_TCIA02_151_1_flair.nii.gz -s ./data/Brats18_TCIA02_151_1/seg_all.nii.gz -fc 4 -ec 3 -nc_seg_mode t2 -t2 ./data/Brats18_TCIA02_151_1/Brats18_TCIA02_151_1_t2.nii.gz -t2_n 5

python seg_all_labels.py -t1ce ./data/Brats18_TCIA02_171_1/Brats18_TCIA02_171_1_t1ce.nii.gz -flair ./data/Brats18_TCIA02_171_1/Brats18_TCIA02_171_1_flair.nii.gz -s ./data/Brats18_TCIA02_171_1/seg_all.nii.gz -fc 9 -ec 3 -nc_seg_mode cc

python seg_all_labels.py -t1ce ./data/Brats18_TCIA01_231_1/Brats18_TCIA01_231_1_t1ce.nii.gz -flair ./data/Brats18_TCIA01_231_1/Brats18_TCIA01_231_1_flair.nii.gz -s ./data/Brats18_TCIA01_231_1/seg_all.nii.gz -fc 3 -ec 3 -nc_seg_mode cc

if you just need the whole tumor

python seg_wt.py -flair ./data/Brats18_TCIA02_151_1/Brats18_TCIA02_151_1_flair.nii.gz -s ./data/Brats18_TCIA02_151_1/wt.nii.gz -fc 4

5. How to cite

Zhao B, Ren Y, Yu Z, et al. AUCseg: An automatically unsupervised clustering toolbox for 3D-segmentation of high-grade gliomas in multi-parametric MR images[J]. Frontiers in Oncology, 2021, 11: 2244.