Feature-based training and evaluation of deep learning models for shoulder bone 3D reconstruction

5 pages•Published: December 17, 2024

Clément Daviller, François Boux de Casson, Fabrice Bertrand and Lhoussein Axel Mabrouk

Abstract

Introduction of deep-learning (DL) in the computer assisted surgery requires to train and evaluate segmentation models by maximizing the control and knowledge of data. In this study, we highlight the incomes of data mastery through the examples of shoulder bone segmentations.

Keyphrases: bone segmentation, deep learning, image reconstruction, total shoulder arthroplasty

In: Joshua W Giles and Aziliz Guezou-Philippe (editors). Proceedings of The 24th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 7, pages 53-57.

Links:	https://easychair.org/publications/paper/5Q4w
	https://doi.org/10.29007/2tks

BibTeX entry

@inproceedings{CAOS2024:Feature_based_training_evaluation,
  author    = {Clément Daviller and François Boux de Casson and Fabrice Bertrand and Lhoussein Axel Mabrouk},
  title     = {Feature-based training and evaluation of deep learning models for shoulder bone 3D reconstruction},
  booktitle = {Proceedings of The 24th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Joshua W Giles and Aziliz Guezou-Philippe},
  series    = {EPiC Series in Health Sciences},
  volume    = {7},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {/publications/paper/5Q4w},
  doi       = {10.29007/2tks},
  pages     = {53-57},
  year      = {2024}}

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