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In-vivo bone segmentation approach for Total Knee Arthroplasty

5 pagesPublished: September 26, 2020

Abstract

Perceiving and making sense of the surgical scene during Total Knee Arthroplasty (TKA) surgery is crucial for building assistance and decision support systems for surgeons and their team. However, the need for large volumes of annotated and structured data for AI-based methods hinders the development of such tools. We hereby present a study on the use of transfer learning to train deep neural networks with scarce annotated data to automatically detect bony areas on live images. We provide quantitative evaluation results on in-vivo data, captured during several TKA procedures. We hope that this work will facilitate further developments of smart surgical assistance tools for orthopaedic surgery.

Keyphrases: Bone Segmentation, Computer-assisted orthopaedic surgery, deep learning, in vivo validation, Total knee arthroplasty

In: Ferdinando Rodriguez Y Baena and Fabio Tatti (editors). CAOS 2020. The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 4, pages 183--187

Links:
BibTeX entry
@inproceedings{CAOS2020:In_vivo_bone_segmentation_approach,
  author    = {Nicolas Loy Rodas and Marion Decrouez and Blaise Bleunven and Sophie Cahen},
  title     = {In-vivo bone segmentation approach for Total Knee Arthroplasty},
  booktitle = {CAOS 2020. The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Ferdinando Rodriguez Y Baena and Fabio Tatti},
  series    = {EPiC Series in Health Sciences},
  volume    = {4},
  pages     = {183--187},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {https://easychair.org/publications/paper/86QG},
  doi       = {10.29007/d78d}}
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