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A New Deformation Pose Estimation Algorithm for the Fully Automatic Design of Patient Specific Knee Prosthesis: Preliminary Results

4 pagesPublished: September 26, 2020

Abstract

In the context of automatic landmarks localization with statistical shape models for the design of customized TKA prosthesis, the first step consists of registering a model, represented by the mean mesh of some healthy femoral bones, towards the segmented femur of the patient. The most complex aspect of the mesh-to-mesh correspondence in this case lies in the fact the source (model) and the target mesh can differ largely (partial view of the femur, anatomy that lies away from the mean) which makes common correspondence approaches inefficient. In this paper, we introduce a contribution to an algorithm from the field of object recognition that produces a reliable registration. By adding the concept of global deformability in the algorithm, we are able to improve the precision of the algorithm (mean mesh-to-mesh distance improved from 2.77mm to 0.79mm) and its robustness to anatomy far off the mean (better standard deviation and Hausdorff distance) on synthetic data . The next step will be to assess it in its application field i.e. the automatic localization of knee landmarks for the design of patient-specific knee prosthesis.

Keyphrases: Automatic landmarks localization, custom implant, knee surgery, pose estimation, preoperative planning, registration, Statistical Shape Model

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 82--85

Links:
BibTeX entry
@inproceedings{CAOS2020:New_Deformation_Pose_Estimation,
  author    = {Charles Garraud and Arnaud Clav\textbackslash{}'e and J\textbackslash{}'er\textbackslash{}\textasciicircum{}ome Ogor and Eric Stindel and Guillaume Dardenne},
  title     = {A New Deformation Pose Estimation Algorithm for the  Fully Automatic Design of Patient Specific Knee Prosthesis: Preliminary Results},
  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     = {82--85},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {https://easychair.org/publications/paper/vQFc},
  doi       = {10.29007/v8d7}}
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