Towards Automatic Generation of Patient-Specific Knee Models

3 pages•Published: December 13, 2022

Elaheh Elyasi, Marek Bucki, Boubaker Asaadi, Daniel Elizondo and Antoine Perrier

Abstract

The objective of the current paper is to present a pipeline designed to reduce the pre-processing time required to build subject-specific finite element knee models and facilitate their clinical integration. The pipeline involves development and validation of an atlas model of the knee joint and features of the TwInsight software suit that use novel methodologies such as: 1) deep learning for automatic segmentation of the bones from computed tomography scans, 2) automatic generation of finite element meshes with hexahedral elements, and 3) anatomical inference algorithm to adapt the atlas model to the morphology of a subject and result in the subject’s personalized biomechanical model.

Keyphrases: finite element method, knee joint, patient specific simulation

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 66-68.

Links:	https://easychair.org/publications/paper/RSwg
	https://doi.org/10.29007/5r88

BibTeX entry

@inproceedings{CAOS2022:Towards_Automatic_Generation_Patient,
  author    = {Elaheh Elyasi and Marek Bucki and Boubaker Asaadi and Daniel Elizondo and Antoine Perrier},
  title     = {Towards Automatic Generation of Patient-Specific Knee Models},
  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/RSwg},
  doi       = {10.29007/5r88},
  pages     = {66-68},
  year      = {2022}}

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