Case study of estimating the weights of valuable meat cuts from the CT images of rabbits ====

About ----

This repository implements the analysis and application described in the paper

@article{Csoka2021,
    author={\'Ad\'am Cs\'oka and Gy\"orgy Kov\'acs and Vir\'ag \'Acs and Zsolt Matics and Zsolt Gerencs\'er and Zsolt Szendr\"o and \"Ors Petneh\'azy and Imre Repa and Mariann Moizs and Tam\'as Donk\'o},
    title={Multi-atlas segmentation based estimation of weights from CT scans in farm animal imaging and its applications to rabbit breeding programs},
    year={2021}
}

Preprint:

Contents ----

Jupyter notebooks ****

1. `000_extract_training_features.ipynb` - segmentation by registration and feature extraction.
2. `001_training.ipynb` - feature subset and regressor parameter selection.
3. `002_analysis.ipynb` - the statistical analysis of the results, reproducing all the tables in the paper.
4. `003_orchestration.ipynb` - executes all notebooks

Other files ****

1. `config.py` - high level configuration parameters.
2. `requirements.txt` - package requirements.
3. `results.csv` - raw results of the regression analysis with feature selection.
4. `results.pickle` - raw results of the regression analysis with feature selection in pickle format.
5. `results.csv` - raw results of the regression analysis without feature selection.
6. `results.pickle` - raw results of the regression analysis without feature selection in pickle format.
7. `06_20180109-CT-cut.xlsx` - results of the dissection study.

Reproducing the results of the paper ----

Installation ****

Clone the `maweight` Python package (https://github.com/gykovacs/maweight):

> git clone git@github.com:gykovacs/maweight.git

Navigate into the root directory of the `maweight` repository and issue

> pip install .

Navigate into the root directory of this package, and issue

> pip install -r requirements.txt

Download the raw data ****

Download the CT images corresponding to the dissection study and the manual annotations from the link https://drive.google.com/file/d/1GT75IEw28MTwFImJZUgJbUIL2AaPYzDM/view?usp=sharing and extract its contents to the `data` directory.

Update the paths ****

Update the paths in the file `config.py` to match the environment the code is running in.

Execute the notebooks ****

Start a jupyter server in the active environment by issuing

> jupyter notebook

And run the notebook `003_orchestration.ipynb` to carry out all steps of the analysis.

Note that due to the large number of CT images and registered masks, the execution requires about 150Gb free space on the disk.