Download PDFOpen PDF in browser

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

4 pagesPublished: December 13, 2022

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:
BibTeX entry
@inproceedings{CAOS2022:Tibial_and_femoral_bones,
  author    = {Ludivine Maintier and Ehouarn Maguet and Arnaud Clav\textbackslash{}'e and Eric Stindel and Val\textbackslash{}'erie 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},
  pages     = {144--147},
  year      = {2022},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {https://easychair.org/publications/paper/nZNs},
  doi       = {10.29007/6jqc}}
Download PDFOpen PDF in browser