Metadata-Version: 2.1
Name: resseg
Version: 0.3.4
Summary: Automatic segmentation of epilepsy neurosurgery resection cavity.
Home-page: https://github.com/fepegar/resseg
Author: Fernando Perez-Garcia
Author-email: fernando.perezgarcia.17@ucl.ac.uk
License: MIT license
Description: # RESSEG
        
        Automatic segmentation of postoperative brain resection cavities from magnetic resonance images (MRI) using a convolutional neural network (CNN) trained with [PyTorch](https://pytorch.org/) 1.7.1.
        
        ## Installation
        
        It's recommended to use [`conda`](https://docs.conda.io/en/latest/miniconda.html) and [install your desired PyTorch version](https://pytorch.org/get-started/locally/) before
        installing `resseg`.
        A 6-GB GPU is large enough to segment an image in the MNI space.
        
        ```shell
        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)](http://nist.mni.mcgill.ca/?page_id=672).
        
        ```shell
        BITE=`resseg-download bite`
        resseg $BITE -o bite_seg.nii.gz
        ```
        
        ![Resection cavity segmented on an image from BITE](screenshots/bite.png)
        
        ### EPISURG
        
        Example using an image from the [EPISURG dataset](https://doi.org/10.5522/04/9996158.v1).
        Segmentation works best when images are in the MNI space, so `resseg` includes a tool
        for this purpose (requires [ANTsPy](https://antspyx.readthedocs.io/en/latest/?badge=latest)).
        
        ```shell
        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
        ```
        
        ![Resection cavity segmented on an image from EPISURG](screenshots/episurg.png)
        
        ## 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*](https://link.springer.com/chapter/10.1007%2F978-3-030-59716-0_12).
        
        [[Preprint on arXiv](https://arxiv.org/abs/2006.15693)]
        
        And the [EPISURG dataset](https://doi.org/10.5522/04/9996158.v1), 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](https://doi.org/10.5522/04/9996158.v1)
        
        ## See also
        
        - [`resector`](https://github.com/fepegar/resector) was used to simulate brain resections during training
        - [TorchIO](http://torchio.rtfd.io/) was also used extensively. Both `resseg` and `resector` require this library.
        
Keywords: resseg
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Requires-Python: >2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*
Description-Content-Type: text/markdown
