Metadata-Version: 2.1
Name: hippunfold
Version: 0.5.11
Summary: Snakemake BIDS app for hippocampal unfolding & subfield segmentation
Home-page: https://github.com/khanlab/hippunfold
Author: Jordan DeKraker & Ali Khan
Author-email: ali.khan@uwo.ca
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
Requires-Dist: snakebids (>=0.3.10)
Requires-Dist: snakemake (>=6.5.2)
Requires-Dist: nnunet-inference-on-cpu-and-gpu (==1.6.6)
Requires-Dist: numpy (>=1.20.2)
Requires-Dist: astropy
Requires-Dist: appdirs
Requires-Dist: pandas
Requires-Dist: nibabel
Requires-Dist: scipy
Requires-Dist: nilearn
Requires-Dist: seaborn
Requires-Dist: jinja2
Requires-Dist: pygraphviz
Requires-Dist: pygments
Requires-Dist: scikit-fmm

Hippunfold
==========

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   :alt: CircleCI



This tool aims to automatically model the topological folding structure of the human hippocampus. It is currently set up to use sub-millimetric T2w MRI data, but may be adapted for other data types. This can then be used to apply the hippocampal unfolding methods presented in `DeKraker et al., 2019 <https://www.sciencedirect.com/science/article/pii/S1053811917309977>`_, and ex-vivo subfield boundaries can be topologically applied from `DeKraker et al., 2020 <https://www.sciencedirect.com/science/article/pii/S105381191930919X?via%3Dihub>`_.

.. image:: https://github.com/khanlab/hippunfold/raw/master/docs/pipeline_overview.png
    :align: center
    :alt: Pipeline Overview

The overall workflow can be summarized in the following steps:

0. Resampling to a 0.3mm isotropic, coronal oblique, cropped hippocampal block

1. Automatic segmentation of hippocampal tissues and surrounding structures via deep convolutional neural network U-net `Li _et al_., 2017 <https://arxiv.org/abs/1707.01992>`_ _OR_ Manual segmentation of hippocampal tissues and surrounding structures using `this <https://ars.els-cdn.com/content/image/1-s2.0-S1053811917309977-mmc1.pdf>`_ protocol

2. Post-processing via fluid label-label registration to a high resolution, topoligically correct averaged template

3. Imposing of coordinates across the anterior-posterior, proximal-distal, and laminar dimensions of hippocampal grey matter via solving the Laplace equation

4. Extraction of a grey matter mid-surface and morpholigical features (thickness, curvature, gyrification index, and, if available, quantitative MRI values sampled along the mid-surface for reduced partial-voluming)

5. Quality assurance via inspection of Laplace gradients, grey matter mid-surface, and flatmapped features

6. Application of subfield boundaries according to predifined topological coordinates




