As musculoskeletal (MSK) imaging scientists, we extract quantitative information from medical images of bones, muscles, and joints to investigate diseases such as osteoporosis, osteoarthritis, and muscular dystrophies

Our approach involve workflows with a common structure, including:

• Acquisition of medical images using magnetic resonance or computed tomography
• Segmentation (i.e. labeling) of images to extract the organs of interest
• Quantification of organ or tissue morphology, structure (e.g. cartilage thickness), chemical composition (e.g. calcium content), and mechanical response (e.g. strains and stresses)

Our ultimate goal is to create computational tools to investigate diseases in a reliable and robust way

The computational tools in our labs are traditionally composed of fragmented code, resulting from a combination of proprietary software, in-house implementations, and occasionally open-source code. This approach presents several challenges:

• When using proprietary software, it is impossible to verify and modify parameters and implementations, limiting our awareness of implementations and ability to adapt the code for new image modalities or anatomies
• The life of in-house code is often tied to the employment of the code creators (mainly MSc students, PhD students, or PostDocs), restricting code reuse and expansion by newer lab members
• Existing open source software is often difficult to reuse because of insufficient documentation and variability of programming languages

To overcome these challenges, we established the ORMIR community. Our goal is to develop open, reproducible, well-tested, and well-documented software to analyze musculoskeletal (MSK) images

The ideas of code sharing, openness, and reproducibility as a solution to code fragmentation began circulating in our scientific community during two satellite events of the Quantitative Musculoskeletal Imaging (QMSKI) Workshop in 2019: Hands-on Transparent QMSKI research: Open data, reproducible workflows, and interactive publications, and Working group on standardization of quantitative metrics for 3D imaging

A few months later, a group of researchers successfully applied for funds to hold a Jupyter Community Workshop entitled Building the Jupyter Community in MSK Imaging Research. We consider the day of acceptance of the proposal, 24 January 2020, as the birthdate of our community. You can read more about our beginnings in our manifesto paper

Since 2020, we have grown to include more and more MSK researchers and contributors from academia and industry. We have developed Python packages, written publications, applied for funds, and hosted workshops

We are divided in working groups, based on our research interests. Each working group has one or more coordinators that support contributors in finding their way to participate in projects

We also have a Community Advisory Board and a Technical Advisory Board, who coordinate the community outreach and provide technical support

We share common goals and values and we are always open to welcoming new contributors