| Name | hipsta JSON |
| Version |
0.9.1
JSON |
| download |
| home_page | |
| Summary | A python package for hippocampal shape and thickness analysis |
| upload_time | 2024-03-06 13:31:09 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2019 Image Analysis, DZNE e.V. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
hippocampus
shape
thickness
geometry
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Hippocampal Shape and Thickness Analysis
## Purpose:
This repository contains the Hipsta package, a collection of scripts for
hippocampal shape and thickness analysis as described in our recent [publication](https://doi.org/10.1016/j.neuroimage.2023.120182).
## Documentation:
Please see the documentation pages for a general overview, usage information and
examples, and output description. Brief usage information is also available [here](hipsta/doc/DOCUMENTATION.md).
Some suggestions for running the script can be found in the [tutorial](TUTORIAL.md).
## Current status:
The hippocampal shape and thickness analysis package is currently in its beta
stage, which means that it's open for testing, but may still contain unresolved
issues. Future changes with regard to the algorithms, interfaces, and package
structure are to be expected.
## Feedback:
Questions, suggestions, and feedback are welcome, and should preferably be
submitted as an [issue](https://github.com/Deep-MI/Hipsta/issues).
## Installation:
It is recommended to run this pipeline within its own virtual environment. A
virtual environment can, for example, be created using Python's `virtualenv`
command:
`virtualenv /path/to/a/new/directory/of/your/choice`
Activate the virtual environment as follows:
`source /path/to/a/new/directory/of/your/choice/bin/activate`
The package is available on `pypi.org`, and can be installed as follows
(including all required dependencies):
`pip install hipsta`
Alternatively, the following code can be used to download this package from its
GitHub repository (this will create a 'Hipsta' directory within the current
working directory):
`git clone https://github.com/Deep-MI/Hipsta.git`
Use the following code to install the downloaded files as a Python package
(after changing into the 'Hipsta' directory). It will also install all required
dependencies:
`pip install .`
The above steps are not necessary when running the [Docker](docker/Docker.md) or
[Singularity](singularity/Singularity.md) versions of the package.
## Requirements:
Unless using the [Docker](docker/Docker.md) or [Singularity](singularity/Singularity.md)
versions of the package, the following conditions need to be met for running an
analysis:
1. A FreeSurfer version (6.x or 7.x) must be sourced, i.e. FREESURFER_HOME must
exist as an environment variable and point to a valid FreeSurfer installation.
2. A hippocampal subfield segmentation created by FreeSurfer 7.11 or later
or the ASHS software. A custom segmentation is also permissible (some restrictions
and settings apply; see [Supported Segmentations](https://github.com/Deep-MI/Hipsta#supported-segmentations)).
3. Python 3.8 or higher including the lapy, numpy, scipy, nibabel, pyvista, and
pyacvd libraries, among others. See `requirements.txt` for a full list, and use
`pip install -r requirements.txt` to install.
4. The gmsh package (version 2.x; http://gmsh.info) must be installed. Can be
downloaded e.g. as binaries for [linux](https://gmsh.info/bin/Linux/gmsh-2.16.0-Linux64.tgz) or
[MacOSX](https://gmsh.info/bin/MacOSX/gmsh-2.16.0-MacOSX.dmg) . The 'gmsh' binary must
be on the $PATH:
`export PATH=${PATH}:/path/to/gmsh-directory/bin`
## References:
Please cite the following publications if you use these scripts in your work:
- Diers, K., Baumeister, H., Jessen, F., Düzel, E., Berron, D., & Reuter, M. (2023). An automated, geometry-based method for hippocampal shape and thickness analysis. Neuroimage, 276:120182. doi: [10.1016/j.neuroimage.2023.120182](https://doi.org/10.1016/j.neuroimage.2023.120182).
Please also consider citing the these publications:
- Geuzaine, C., & Remacle, J.-F. (2009). Gmsh: a three-dimensional finite element mesh generator with built-in pre- and post-processing facilities. International Journal for Numerical Methods in Engineering, 79, 1309-1331.
- Andreux, M., Rodola, E., Aubry, M., & Cremers, D. (2014). Anisotropic Laplace-Beltrami operators for shape analysis. In European Conference on Computer Vision (pp. 299-312). Springer, Cham.
- Iglesias, J. E., Augustinack, J. C., Nguyen, K., Player, C. M., Player, A., Wright, M., ... & Fischl, B. (2015). A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. Neuroimage, 115, 117-137.
- Yushkevich, P. A., Pluta, J., Wang, H., Ding, S.L., Xie, L., Gertje, E., Mancuso, L., Kliot, D., Das, S. R., & Wolk, D.A. (2015). Automated Volumetry and Regional Thickness Analysis of Hippocampal Subfields and Medial Temporal Cortical Structures in Mild Cognitive Impairment. Human Brain Mapping, 36, 258-287.
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