A benchmark for Deep Learning-based approaches for In-vivo segmentation of 2D images in Total Knee Arthroplasty

6 pages•Published: December 13, 2022

Baptiste Dehaine, Marion Decrouez and Nicolas Loy Rodas

Abstract

Progress in machine learning and artificial intelligence (AI) opens the way to the devel- opment of smart clinical-assistance systems and decision-support tools for the operating room (OR). Yet, before deploying these algorithms in the OR, assessment of their perfor- mances in real clinical conditions is necessary. Gathering intraoperative data for training and testing is hard, and robustness to the challenging conditions of the OR is not always demonstrated. In this paper we introduce a unique multi-patient dataset of images cap- tured during Total Knee Arthroplasty (TKA) surgery. We use this dataset to compare five deep learning-based image segmentation approaches and provide quantitative and qualita- tive results. We hope that this work will help bringing light on the performances of AI in a real surgical environment.

Keyphrases: bone segmentation, computer assisted orthopaedic surgery, deep learning, in vivo validation, total knee arthroplasty

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 45-50.

Links:	https://easychair.org/publications/paper/KxR8
	https://doi.org/10.29007/bcs4

BibTeX entry

@inproceedings{CAOS2022:benchmark_Deep_Learning_based,
  author    = {Baptiste Dehaine and Marion Decrouez and Nicolas Loy Rodas},
  title     = {A benchmark for Deep Learning-based approaches for In-vivo segmentation of 2D images in Total Knee Arthroplasty},
  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},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {/publications/paper/KxR8},
  doi       = {10.29007/bcs4},
  pages     = {45-50},
  year      = {2022}}

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