Tibial and femoral bones segmentation on CT-scans: a deep learning approach

4 pages•Published: December 13, 2022

Ludivine Maintier, Ehouarn Maguet, Arnaud Clavé, Eric Stindel, Valérie Burdin and Guillaume Dardenne

Abstract

Custom implants in Total Knee Arthroplasty (TKA) could improve prosthesis’ durability and patient’s comfort, but designing such personalized implants requires a simplified and thus automatic workflow to be easily integrated in the clinical routine. A good knowledge of the shape of the patient's femur and tibia is necessary to design it, but segmentation is still today a key issue. We present here an automatic segmentation approach of the three joints of the lower limb: hip, knee and ankle, using convolutional neural networks (CNNs) on successive transverse views from CT images. Our three 2D CNNs are built on the U-net model, and their specialization each on one joint allowed us to achieve promising results presented here. This could be integrated in a TKA planning software allowing the automatic design of TKA custom implants.

Keyphrases: bone segmentation, ct scans, custom implants, deep learning, knee joint replacement, u net

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 144-147.

Links:	https://easychair.org/publications/paper/nZNs
	https://doi.org/10.29007/6jqc

BibTeX entry

@inproceedings{CAOS2022:Tibial_femoral_bones_segmentation,
  author    = {Ludivine Maintier and Ehouarn Maguet and Arnaud Clavé and Eric Stindel and Valérie Burdin and Guillaume Dardenne},
  title     = {Tibial and femoral bones segmentation on CT-scans: a deep learning approach},
  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/nZNs},
  doi       = {10.29007/6jqc},
  pages     = {144-147},
  year      = {2022}}

Download PDF Open PDF in browser