resseg 0.3.6

Automatic segmentation of epilepsy neurosurgery resection cavity.

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

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:

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

Trained model

The trained model can be used without installing resseg, but you'll need to install unet first:

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)

Credit

If you use this library for your research, please cite our MICCAI 2020 paper:

F. Pérez-García, R. Rodionov, A. Alim-Marvasti, R. Sparks, J. S. Duncan and S. Ourselin. Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning.

[Preprint on arXiv]

And the EPISURG dataset, which was used to train the model:

Pérez-García, Fernando; Rodionov, Roman; Alim-Marvasti, Ali; Sparks, Rachel; Duncan, John; Ourselin, Sebastien (2020): EPISURG: a dataset of postoperative magnetic resonance images (MRI) for quantitative analysis of resection neurosurgery for refractory epilepsy. University College London. Dataset. https://doi.org/10.5522/04/9996158.v1

See also

• resector was used to simulate brain resections during training
• TorchIO was also used extensively. Both resseg and resector require this library.