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ormir-mids

I/O of Medical Image Data Structure (MIDS) for Open and Reproducible Musculoskeletal Imaging Research (ORMIR). Based on the BIDS data structure.

Note

This package is a fork of muscle-bids for muscle MR imaging data.

GitHub license

Tutorial

Run the tutorial on Binder    Binder
Run the tutorial on Colab     Open In Colab

Main contributors

  • Francesco Santini
  • Donnie Cameron
  • Leonardo Barzaghi
  • Judith Cueto Fernandez
  • Jilmen Quintiens
  • Lee Youngjun
  • Jukka Hirvasniemi
  • Gianluca Iori
  • Simone Poncioni
  • Serena Bonaretti

Installation

Dependencies

To install ormir-mids, run the code below, noting this list of dependencies.

It is recommended to install ormir-mids in a separate virtual environment:

conda env create -n ormir-mids
conda activate ormir-mids

Clone the git repository:

git clone https://github.com/ormir-mids/ormir-mids.git

Now we can install the package using pip. This will also install the required dependencies.

cd ormir-mids
pip install .
pip install --upgrade nibabel # the default nibabel has bugs

Usage

ormir-mids can be used in two ways:

  1. Running dcm2omids as an executable to convert DICOM data to the MIDS format.
  2. Importing ormir-mids as a Python module to find, load, and interrogate ORMIR-MIDS-format data.

1. Converting DICOMs to the ORMIR-MIDS structure

The commandline script is called dcm2omids.exe. To view the commandline script help type

dcm2omids -h

To use ormir-mids within Python, import the following modules

from ormir_mids.utils.io import find_bids, load_bids
import nibabel as nib

For a detailed description of how to use ormir-mids see the following notebook

2. Exploring medical volumes with ORMIR-MIDS

ormir-mids can be used within Python to load, manipulate, and visualize medical volume datasets, without having to convert them to the ORMIR-MIDS structure.

  • Load a DICOM file to a MedicalVolume object
from ormir_mids.utils.io import load_dicom
mv = load_dicom('<Path-to-DICOM-file>')
  • Slice the volume. This will create a separate subvolume. Metadata will be sliced appropriately.
mv_subvolume = mv[50:90, 50:90, 30:70]
mv_itk = mv.to_sitk()

Examples of how to use ormir-mids for common data handling, image manipulation and processing tasks within Python can be found in this notebook

Acknowledgement

The development of ORMIR-MIDS specification and package started during the 2nd ORMIR workshop Sharing and Curating Open Data in Musculoskeletal Imaging Research and is currently ongoing. ORMIR-MIDS is an extension of muscle-BIDS, which was partly developed during the 1st ORMIR workshop Building the Jupyter Community in Musculoskeletal Imaging Research.

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Converter and utilities for the ORMIR-MIDS data format

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