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Automatic quantification of fatty infiltration of the supraspinatus from MRI

4 pagesPublished: December 13, 2022

Abstract

Fat fraction of the rotator cuff muscles has been shown to be a predictor of rotator cuff repair failure. In clinical diagnosis, fat fraction of the affected muscle is typically assessed visually on the oblique 2D Y-view and categorized according to the Goutallier scale on T1 weighted MRI. To enable a quantitative fat fraction measure of the rotator cuff muscles, an automated analysis of the whole muscle and Y-view slice was developed utilizing 2-point Dixon MRI. 3D nn-Unet were trained on water only 2-point Dixon data and corresponding annotations for the automatic segmentation of the supraspinatus, humerus and scapula and the detection of 3 anatomical landmarks for the automatic reconstruction of the Y-view slice. The supraspinatus was segmented with a Dice coefficient of 90% (N=24) and automatic fat fraction measurements with a difference from manual measurements of 1.5 % for whole muscle and 0.6% for Y-view evaluation (N=21) were observed. The presented automatic analysis demonstrates the feasibility of a 3D quantification of fat fraction of the rotator cuff muscles for the investigation of more accurate predictors of rotator cuff repair outcome.

Keyphrases: deep learning, MRI, Muscle fat fraction, rotator cuff, shoulder

In: Ferdinando Rodriguez Y Baena, Joshua W Giles and Eric Stindel (editors). Proceedings of The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 5, pages 107--110

Links:
BibTeX entry
@inproceedings{CAOS2022:Automatic_quantification_of_fatty,
  author    = {Hanspeter Hess and Michael Herren and Nicolas Gerber and Olivier Scheidegger and Michael Sch\textbackslash{}"ar and Keivan Daneshvar and Matthias A. Zumstein and Kate Gerber},
  title     = {Automatic quantification of fatty infiltration of the supraspinatus from MRI},
  booktitle = {Proceedings of The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Ferdinando Rodriguez Y Baena and Joshua W Giles and Eric Stindel},
  series    = {EPiC Series in Health Sciences},
  volume    = {5},
  pages     = {107--110},
  year      = {2022},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {https://easychair.org/publications/paper/NBqj},
  doi       = {10.29007/xq8m}}
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