An open research initiative · est. 2026

Open data and reproducible benchmarks for spine imaging AI.

The OpenSpineConsortium unifies fragmented public imaging collections into carefully documented, openly licensed datasets — so that anatomy-aware machine learning for the spine and pelvis can be built, audited, and trusted.

Explore the datasets Join the consortium
802
annotated CT records
3
source collections unified
12
abstracts · CNS 2026
19
research contributors

The Project

Spine imaging AI has a quiet reliability problem. Most published models are trained and validated on collections that are small, inconsistently annotated, or closed — and they routinely stumble on the anatomy that matters most clinically: transitional vertebrae, segmentation ambiguity, and rare structural variants.

The OpenSpineConsortium exists to fix the foundation. We take existing public CT collections, harmonize their labels under a single coherent scheme, audit them against widely used segmentation tools, and re-release them as documented, openly licensed datasets with fixed cross-validation splits. The goal is simple: make spine and pelvis imaging research reproducible by default.

  • Harmonize

    One label scheme, many sources

    Fuse spine and pelvis collections under a unified, collision-free labelmap that resolves L5/L6 and lumbarization edge cases.

  • Audit

    Trust, but verify

    Systematically benchmark off-the-shelf tools to surface where they silently disagree with ground truth.

  • Release

    Open and citable

    Permissive licensing, machine-readable metadata, and frozen splits so results can actually be compared.

Lessons