Automatic Glenoid Bone Loss Detection and Quantification in Shoulder CT Scans

Avichai Haimi; Ori Safran; Shaul Beyth; Leo Joskowicz

doi:10.29007/hlxd

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Automatic Glenoid Bone Loss Detection and Quantification in Shoulder CT Scans

4 pages•Published: March 8, 2024

Avichai Haimi, Ori Safran, Shaul Beyth and Leo Joskowicz

Abstract

Estimation of glenoid bone loss following shoulder dislocation in a CT scan is often required to determine the appropriate surgery needed to restore shoulder stability [1]. Currently, the best method for measuring glenoid bone loss has not been universally defined, so various methods have been proposed [2,3]. They can be grouped into linear-based (most methods) and area-based measurement methods, without (standalone) or with a comparison with the healthy contralateral glenoid, which is not always included in the CT scan. In all cases, the measurements are performed manually, which is time-consuming, requires expertise, and is subject to observer variability.
This paper presents a novel automatic standalone linear-based method for glenoid bone loss quantification in shoulder CT scans.

Keyphrases: Glenoid bone loss computation, Model-based computation, Shoulder CT scan, Surgical decision support

In: Joshua W Giles (editor). Proceedings of The 22nd Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 6, pages 31--34

Links:	https://easychair.org/publications/paper/g6Fd
	https://doi.org/10.29007/hlxd

BibTeX entry

@inproceedings{CAOS2023:Automatic_Glenoid_Bone_Loss,
  author    = {Avichai Haimi and Ori Safran and Shaul Beyth and Leo Joskowicz},
  title     = {Automatic Glenoid Bone Loss Detection and Quantification in Shoulder CT Scans},
  booktitle = {Proceedings of The 22nd Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Joshua W Giles},
  series    = {EPiC Series in Health Sciences},
  volume    = {6},
  pages     = {31--34},
  year      = {2024},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {https://easychair.org/publications/paper/g6Fd},
  doi       = {10.29007/hlxd}}

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