HIPSTA

The HIPpocampal Shape and Thickness Analysis Toolbox (HIPSTA) present a geometry-based method for the analysis of local hippocampal thickness and curvature and constructs an intrinsic coordinate system (unrolling/flattening) for statistical analysis across multiple participants.

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What HIPSTA can do for you

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.

Documentation:

Please see the documentation pages for a general overview, usage information and examples, and output description. Brief usage information is also available here. Some suggestions for running the script can be found in the tutorial.

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.

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 or Singularity versions of the package.

Requirements:

Unless using the Docker or Singularity 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).

  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 or MacOSX . 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.

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.

Keywords
Programming languages
  • Python 99%
  • Dockerfile 1%
License
  • MIT
</>Source code

Participating organisations

German Center for Neurodegenerative Diseases
Harvard Medical School

Mentions

Contributors

KD
Kersten Diers
German Center for Neurodegenerative Diseases

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Updated 8 months ago
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