Corpus callosum's shape signature for segmentation error detection in large datasets

The detection of incorrect segmentations is important for performing quality control over large datasets, avoiding introduce errors along the processing pipeline. Specially in large datasets, manual detection results unfeasible and automatic algorithms are the only way to perform quality control.

Reproducible paper

This Readme file holds instructions for reproduction of the work Corpus callosum's shape signature for segmentation error detection in large datasets

This work was developed as part of a final project in the Tools for reproducible research course. It is licensed under GNU v3 license, please refer to LICENSE.md for more details.

Environment, used libraries and dependencies

• Python 2.7.6 (Be sure you have installed Python 2.7.x - Version Python 3.x presents some problems)
• Numpy 1.12.1
• Scipy 0.18.1
• Matplotlib 2.0.0
• bib_mri library (My library avaliable on: https://github.com/wilomaku/IA369Z/tree/master/dev)

Workflow

This project receives a binary mask, in .npy format, and return a binary value depending if it is erroneous segmentation (1) or correct segmentation (0). For constructing model (mean curvature generation and signature configuration), binary masks were passed (in .pny format).

Files structure

This structure is located in the root of the git repository wilomaku/IA369Z:

• Deliver: Directory with main notebook 'date'-WJGH-art_struc.ipynb for execution. The 'date' part in file name is formatted as day/month/year (last two digits). Please make sure you are using the last released notebook. Old versions of released notebooks are availables too, but they are not updated.
• Dev: It contains two elements: 1) bib_mri directory with Python script functions needed for execution of the reproducible paper and 2) Some old stuff under development (Not necessary).
• Images: Necessary images for notebook visualization. The user should not require to do anything here.

Instructions to execute notebook

Please, pay attention to these instructions and follow carefully. Besides Jupyter notebook installed, you must have a work directory with three elements: dataset directory, ipyhton script and library directory with the necessary functions.

1. Make sure you have installed Jupyter Notebook with Python 2.7 (Instructions at http://jupyter.readthedocs.io/en/latest/install.html)
2. Create a local directory on location of your preference. This will be your rep_directory. If you already have a directory for repositories use it.
3. Clone the project's repository (available on https://github.com/wilomaku/IA369Z) directly on your rep_directory. Follow the next steps from your shell terminal or your prefered command line interface:

• cd rep_directory
• git clone https://github.com/wilomaku/IA369Z.git

4. Write a email to wjgarciah@unal.edu.co requesting dataset link. You will get back the Dropbox link to download dataset (dataset.zip).
5. Download dataset.zip, copy it at rep_directory/IA369Z/ and extract there. dataset directory has three directories (Seg_pixel/, Seg_ROQS/, Seg_Watershed/) containing binary masks (on .npy) and their labels (on .csv) for 153 subjects. For each directory, masks are named as mask_'method'_'number'.npy where 'method' is the segmentation method that generate the mask and 'number' is the number assigned to that subject. The csv file, for each directory, has two columns: subject number and segmentation label (0 for correct segmentation and 1 for erroneous segmentation).
6. Run jupyter notebook from your rep_directory/IA369Z/ directory.
7. From Jupyter browser interface open main notebook (the most recent one) in the deliver directory and run cells in order. You can check the intermediate results.

Questions? Suggestions? Please write to wjgarciah@unal.edu.co