Open-Source Machine Learning Computed Tomography Scan Segmentation for Spine Osteoporosis Diagnostics
ML-driven CT segmentation correlates with DXA BMD stratifications and can provide a robust, consistent, and efficient assessment of HU density of critical vertebral structures.
Authors
- Joseph S. Hudson (ORCID: https://orcid.org/0000-0001-8963-9676)
- Nicolás M. Kass (ORCID: https://orcid.org/0009-0009-2968-1253)
- Gerszten Pc
- Aimé Parra Legarreta (ORCID: https://orcid.org/0000-0002-7929-3955)
- Shovan Bhatia (ORCID: https://orcid.org/0000-0001-8090-7208)
- Jacob T. Biehl (ORCID: https://orcid.org/0000-0002-3878-5208)
- Regan M. Shanahan (ORCID: https://orcid.org/0000-0002-5846-9966)
- Jhair Alejandro Colán
- David J. McCarthy (ORCID: https://orcid.org/0000-0002-8734-9795)
- Michael R. Kann (ORCID: https://orcid.org/0009-0000-6362-2083)
- Edward G. Andrews
- G. J. Hurt (ORCID: https://orcid.org/0000-0002-7355-7825)
- Nikhil Sharma
- Nitin Agarwal
- Akshay Sankar
- Samuel Adida
Institutions
- University of Pittsburgh (US)
- University of Pittsburgh Medical Center (US)
- Neurological Surgery (US)
Publication Details
- Journal
- Neurosurgery
- Published
- 2026-02-05
- DOI
- https://doi.org/10.1227/neu.0000000000003928
- Primary Topic
- Bone health and osteoporosis research
- Type
- article
- Field-Weighted Citation Impact
- 0.00