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Image analysis approaches to analyze the OAI magnetic resonance images

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@HastingsGreer wip, todo: generate the new sub mesh based on the face identifiers and https://itk.org/Doxygen/html/classitk_1_1AutomaticTopologyMeshSource.html

The original code validated against known good thickness measurements here: https://github.com/uncbiag/OAI_analysis/blob/master/validate_thickness_results.py We should do the same.

Update the README.md section on how to use and install the code. Will likely also need to change once dask support is fully included (ie., running via script vs Jupyter...

Available on longleaf, along with: cuda: cuda/8.0, cuda/9.0, cuda/9.2, cuda/10.0, cuda/10.1, cuda/11.2, cuda/11.3, cuda/11.4, cuda/11.5, cuda/11.8, cuda/12.2

There is a “T2_analysis” branch in “OAI_analysis_2" that Boqi wrote. As well as a “notebooks/T2Demo.py” for the demo and here’s the affine registration model: https://drive.google.com/file/d/156LnBZqv3lEQUdcmzYYPHqBKyjoTVrr3/view?usp=share_link

The repository is currently 753 MB, and takes 17 minutes to clone from GitHub. There are some large files in Git history and large files in the current working directory...

Old order: warp segmentation of cartilage extract mesh compute thickness New order: extract mesh compute thickness warp mesh, bringing metadata with it move mesh to atlas cartilage mesh move mesh...