Automatic segmentation of epilepsy neurosurgery resection cavity.
Project description
RESSEG
Automatic segmentation of postoperative brain resection cavities from magnetic resonance images (MRI) using a convolutional neural network (CNN) trained with PyTorch 1.7.1.
Installation
It's recommended to use conda
and install your desired PyTorch version before
installing resseg
.
A 6-GB GPU is large enough to segment an image in the MNI space.
conda create -n resseg python=3.8 ipython -y && conda activate resseg # recommended
pip install resseg
Usage
BITE
Example using an image from the Brain Images of Tumors for Evaluation database (BITE).
BITE=`resseg-download bite`
resseg $BITE -o bite_seg.nii.gz
EPISURG
Example using an image from the EPISURG dataset.
Segmentation works best when images are in the MNI space, so resseg
includes a tool
for this purpose (requires ANTsPy).
pip install antspyx
EPISURG=`resseg-download episurg`
resseg-mni $EPISURG -t episurg_to_mni.tfm
resseg $EPISURG -o episurg_seg.nii.gz -t episurg_to_mni.tfm
Credit
If you use this library for your research, please cite our MICCAI 2020 paper:
And the EPISURG dataset, which was used to train the model:
See also
Project details
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