ORMIR-MIDS — Open Standard for Musculoskeletal Imaging Data

Jun 2023 · 2 min read
projects

Open-source contribution to the ORMIR (Open and Reproducible Musculoskeletal Imaging Research) community: both a specification and a Python package that standardise the Medical Image Data Structure (MIDS) for musculoskeletal imaging, building directly on BIDS (Brain Imaging Data Structure) and muscle-BIDS and extending those ideas to the broader MSK domain.

The problem

Musculoskeletal imaging research suffers from a lack of standardized, machine-readable data organization. Raw clinical data typically arrives as DICOM, where acquisition metadata is scattered, inconsistently labeled, and difficult to query programmatically. This makes data sharing, multi-site collaboration, and reproducible analysis pipelines hard to build and maintain. ORMIR-MIDS addresses this by defining a common, open data structure so that musculoskeletal imaging datasets can be curated, shared, and reused consistently across the research community.

Approach

The package centres on converting source DICOM data into the standardized ORMIR-MIDS layout, and then providing tooling to work with that layout. Once data is converted, ORMIR-MIDS can be used as a Python module to find, load and interrogate ORMIR-MIDS-format data. Because it inherits from the BIDS and muscle-BIDS philosophy, the standard emphasises a predictable on-disk organization with structured, self-describing metadata, making datasets easier to process automatically.

Key features

  • DICOM-to-ORMIR-MIDS conversion from raw clinical imaging data into the standardized format.
  • Command-line interface for batch processing, with built-in anonymization and recursive directory handling (e.g. dcm2omids -anonymize <pseudo_name> -recursive <input_dir> <output_dir>).
  • Python API (import ormir_mids) to find, load and interrogate standardized data programmatically.
  • Anonymization / pseudonymization during conversion — important for sharing clinical imaging data.
  • Demo notebooks illustrating usage, runnable locally or via Binder.
  • Distributed on PyPI (pip install ormir-mids), with a development install via the repository.

Status & citation

A community effort with multiple lead contributors under the broader ORMIR community; released under the Apache 2.0 license and intended for research purposes only (not a certified medical device). The work is described in the open-standard paper “ORMIR-MIDS: an open standard for curating and sharing musculoskeletal imaging data,” JBMR Plus 10(3), ziag013 (2026).

Data-Tools
Maria Monzon
Authors
Maria Monzon (she/her)
Computer Vision & Medical AI Researcher
PhD candidate at ETH Zurich developing robust and trustworthy deep learning for medical image analysis — spine and cardiac MRI, multimodal biomedical data, and uncertainty quantification. Previously a computer-vision researcher at BASF, where I deployed models to production in regulated, GLP-certified environments. I care about efficient code and reproducible research.