Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images

Zhengyi Yang, Jurgen Fripp, Shekhar S Chandra, Aleš Neubert, Ying Xia, Mark Strudwick, Anthony Paproki, Craig Engstrom, Stuart Crozier

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)

Abstract

We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.926 ± 0.050 and 0.837 ± 0.059, respectively. The mean DSC values obtained for BCI extraction were 0.806 ± 0.133 for the humerus and 0.795 ± 0.117 for the scapula. The current model-based approach successfully provided automated bone segmentation and BCI extraction from MR images of the shoulder. In future work, this framework appears to provide a promising avenue for automated segmentation and quantitative analysis of cartilage in the glenohumeral joint.

Original languageEnglish
Pages (from-to)1441-1459
Number of pages19
JournalPhysics in Medicine & Biology
Volume60
Issue number4
DOIs
Publication statusPublished - 21 Feb 2015
Externally publishedYes

Keywords

  • active shape model
  • automatic segmentation
  • shoulder joint

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