resector 0.2.6

Algorithm to simulate resections osurgery on brain MRI scans.

resector

Implementation of a TorchIO transform used to simulate a resection cavity from a T1-weighted brain MRI and a corresponding geodesic information flows (GIF) brain parcellation (version 3.0).

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.

Bibtex:

@InProceedings{10.1007/978-3-030-59716-0_12,
    author="P{\'e}rez-Garc{\'i}a, Fernando
    and Rodionov, Roman
    and Alim-Marvasti, Ali
    and Sparks, Rachel
    and Duncan, John S.
    and Ourselin, S{\'e}bastien",
    title="Simulation of Brain Resection for Cavity Segmentation Using Self-supervised and Semi-supervised Learning",
    booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020",
    year="2020",
    publisher="Springer International Publishing",
    address="Cham",
    pages="115--125",
    isbn="978-3-030-59716-0"
}

[Preprint on arXiv]

Installation

$ git clone https://github.com/fepegar/resector.git
$ pip install --editable ./resector

Usage

$ resect t1.nii.gz gif_parcellation.nii.gz t1_resected.nii.gz t1_resection_label.nii.gz

Run resect --help for more options.