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Augmented Active Shape Model Search – towards 3D Ultrasound-based Bone Surface Reconstruction

5 pagesPublished: September 25, 2020

Abstract

Patient-specific instrumentation in total knee arthroplasty (TKA), among other medical indications, requires a three-dimensional model of the bones involved. Currently, these are typically segmented from computer tomography images. Ultrasound offers a cheap as well as radiation-less imaging alternative, but suffers from a low signal-to-noise ratio as well as several other image artifacts. The interleaved partial active shape models search (IPASM) adapts a general physiological model to a set of images of a single patient, but suffers from false correspondences being soft tissue interfaces that are interpreted as bone surface. In order to counter this problem, a convolutional neural network (CNN) is applied to preprocess ultrasound images into bone confidence maps. This reduces the average surface distance error in an in-vivo evaluation by 0.7 to 1.3 mm.

Keyphrases: Convolutional Neural Network, registration, Segmentation, Statistical Shape Model, TKA, Ultrasound

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 117--121

Links:
BibTeX entry
@inproceedings{CAOS2020:Augmented_Active_Shape_Model,
  author    = {Benjamin Hohlmann and Klaus Radermacher},
  title     = {Augmented Active Shape Model Search -- towards 3D Ultrasound-based Bone Surface Reconstruction},
  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     = {117--121},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {https://easychair.org/publications/paper/LmQX},
  doi       = {10.29007/3px6}}
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