Automatic segmentation of epilepsy neurosurgery resection cavity.
Automatic segmentation of postoperative brain resection cavities from magnetic resonance images (MRI) using a convolutional neural network (CNN) trained with PyTorch 1.7.1.
It's recommended to use
A 6-GB GPU is large enough to segment an image in an MNI space of size 193 × 229 × 193.
conda create -n resseg python=3.8 -y conda activate resseg pip install light-the-torch ltt install torch pip install resseg resseg --help
Below are two examples of cavity segmentation for tumor and epilepsy surgery. The epilepsy example includes registration to the MNI space. Both examples can be run online using Google Colab:
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
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
The trained model can be used without installing
resseg, but you'll need to install
pip install unet==0.7.7
Then, in Python:
import torch repo = 'fepegar/resseg' model_name = 'ressegnet' model = torch.hub.load(repo, model_name, pretrained=True)
Graphical user interface using 3D Slicer
There is an experimental graphical user interface (GUI) built on top of 3D Slicer.
Visit this repository for additional information and installation instructions.
Plotting resected structures
from resseg.parcellation import GIFParcellation, FreeSurferParcellation parcellation_path = 't1_seg_gif.nii.gz' cavity_seg_on_preop_path = 'cavity_on_preop.nii.gz' parcellation = GIFParcellation(parcellation_path)
Percentage of each resected structure: 100% of Left vessel 83% of Left Inf Lat Vent 59% of Left Amygdala 58% of Left Hippocampus 26% of Left PIns posterior insula 24% of Left PP planum polare 21% of Left Basal Forebrain 18% of Left Claustrum 16% of Left PHG parahippocampal gyrus 15% of Left Pallidum 15% of Left Ent entorhinal area 13% of Left FuG fusiform gyrus 13% of Left Temporal White Matter 11% of Left Putamen 10% of Left Insula White Matter 5% of Left ITG inferior temporal gyrus 5% of Left periventricular white matter 5% of Left Ventral DC The resection volume is composed of: 30% is Left Temporal White Matter 12% is Left Hippocampus 10% is Left Insula White Matter 7% is Left FuG fusiform gyrus 6% is Left Amygdala 4% is Left ITG inferior temporal gyrus 4% is Left PP planum polare 3% is Left Putamen 3% is Left Claustrum 3% is Left PIns posterior insula 3% is Left PHG parahippocampal gyrus 2% is [Unkown label: 4] 1% is Left Ent entorhinal area 1% is Left Pallidum 1% is Left Inf Lat Vent 1% is Left Ventral DC
If you use this library for your research, please cite the following publications:
If you use the EPISURG dataset, which was used to train the model, please cite the following publication:
F. Pérez-García et al., 2020, EPISURG: a dataset of postoperative magnetic resonance images (MRI) for quantitative analysis of resection neurosurgery for refractory epilepsy. University College London. Dataset.
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