ormir-mids 0.1.1

Library for standardized data input/output for musculoskeletal imaging, based on BIDS

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.

Tutorial

Run the tutorial on Binder   
Run the tutorial on 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

ormir-mids usage: dcm2mbids

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]

• Convert volume to ITK image with SimpleITK

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

ormir-mids usage: MedicalVolume class

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.