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papago

patch-based gaussian mixture model

https://github.com/adalca/papago

Science Score: 54.0%

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Repository

patch-based gaussian mixture model

Basic Info
  • Host: GitHub
  • Owner: adalca
  • Language: MATLAB
  • Default Branch: master
  • Size: 581 KB
Statistics
  • Stars: 15
  • Watchers: 6
  • Forks: 2
  • Open Issues: 0
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Created about 10 years ago · Last pushed almost 7 years ago
Metadata Files
Readme Citation

README.md

papago

Patch based gaussian mixture modelling of medical imaging

Pre-processing

  • First, we need data to be aligned, and we need the masks of those alignments (perhaps even the interp-matrices?)

Execution

Data Preparation

Preprocess subvolumes, e.g. adniprep.m, get md struct. This involves building the medialDataset structure via restorationmd and processing the images via processmd.

Training On Cluster

  1. Break up dataset into "subvolume columns" via grid, e.g. via md2subvols.m, and save subvolume columns. This can take a long amount of time and/or memory.
  2. Run wgmm on cluster distributed on each subvolumes, e.g. via sgeTrain.sh. This can be done via model0 (isotropic data) of model3 (weighted data).

Testing On Cluster

  1. run mccRecon.m (papago.recon) on all patches on each subvolume, and store the reconstructions!
  2. (unfinished) re-compose volume.

Evaluation for optimal K at each location (On Cluster)

Loop steps for Training and Testing for various K. Choose K based on best patch reconstruction at each location.

Training and Testing On a Single Machine

This is usually done on a subset of the image grid.
Loop over (sub)grid: 1. create/load subvolume column. 1. run wgmm via papago.train 1. run papago.recon to reconstruct all the patches in this subvolume (perhaps for just a subset of subjects)

Quilt patches.

Papers

If you find this library useful, please cite (download bib):

Owner

  • Name: Adrian Dalca
  • Login: adalca
  • Kind: user
  • Location: Cambridge, MA
  • Company: MIT

Professor at Harvard Medical School, Researcher at MIT. Machine Learning in Medical Image Analysis, computer vision.

Citation (citations.bib) 

@article{dalca2019medical,
  title={Medical Image Imputation From Image Collections},
  author={Dalca, Adrian V and Bouman, Katherine L and Freeman, William T and Rost, Natalia S and Sabuncu, Mert R and Golland, Polina},
  journal={IEEE transactions on medical imaging},
  volume={38},
  number={2},
  pages={504--514},
  year={2019},
  publisher={IEEE}
}

@inproceedings{dalca2017population,
  title={Population Based Image Imputation},
  author={Dalca, Adrian V and Bouman, Katherine L and Freeman, William T and Rost, Natalia S and Sabuncu, Mert R and Golland, Polina},
  booktitle={Information Processing in Medical Imaging},
  year={2017},
  pages={1--13},
  organization={Springer}
}

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