3D Reconstruction of Femur using Ultrasound - Hand Guided Evaluation and Autonomous Robotic Approach

7 pages•Published: March 8, 2024

Lovis Phlippen, Benjamin Hohlmann and Klaus Radermacher

Abstract

Total Knee Arthroplasty is a frequently performed surgery. Patient specific planning and implants may improve surgical outcome. For this purpose, 3D models of the bones are required, which are typically generated by using computed tomography. A radiation free and cheaper alternative could be ultrasound. However, bone segmentation and a competitive method of creating a complete bone model are a challenge.
In this work a fully-automatic bone reconstruction pipeline using ultrasound, which includes machine learning based image segmentation and an interpolation algorithm for missing areas using statistical shape models, is presented and evaluation results with free hand probe guidance are outlined. A mean surface distance error of 0.96 mm for femur bone reconstruction is achieved. Furthermore, a robotic scanning approach is presented to automate the entire process. Autonomous scanning of the anterior distal femur was successful for 4 out of 5 probands. On average, 54 % of the accessed bone surface could be reconstructed.

Keyphrases: convolutional neural network, extrapolation, knee, robotic ultrasound system, segmentation, ultrasound

In: Joshua W Giles (editor). Proceedings of The 22nd Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 6, pages 75-81.

Links:	https://easychair.org/publications/paper/dKNG
	https://doi.org/10.29007/7rr8

BibTeX entry

@inproceedings{CAOS2023:3D_Reconstruction_Femur_using,
  author    = {Lovis Phlippen and Benjamin Hohlmann and Klaus Radermacher},
  title     = {3D Reconstruction of Femur using Ultrasound - Hand Guided Evaluation and Autonomous Robotic Approach},
  booktitle = {Proceedings of The 22nd Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Joshua W Giles},
  series    = {EPiC Series in Health Sciences},
  volume    = {6},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {/publications/paper/dKNG},
  doi       = {10.29007/7rr8},
  pages     = {75-81},
  year      = {2024}}

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