Deep Learning based acoustic measurement approach for robotic applications on orthopedics

Bangyu Lan*, Momen Abayazid, Nico Verdonschot, Stefano Stramigioli, Kenan Niu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In Total Knee Replacement Arthroplasty (TKA), surgical robotics can provide image-guided navigation to fit implants with high precision. Its tracking approach highly relies on inserting bone pins into the bones tracked by the optical tracking system. This is normally done by invasive, radiative manners (implantable markers and CT scans), which introduce unnecessary trauma and prolong the preparation time for patients. To tackle this issue, ultrasound-based bone tracking could offer an alternative. In this study, we proposed a novel deep-learning structure to improve the accuracy of bone tracking by an A-mode ultrasound (US). We first obtained a set of ultrasound dataset from the cadaver experiment, where the ground truth locations of bones were calculated using bone pins. These data were used to train the proposed CasAtt-UNet to predict bone location automatically and robustly. The ground truth bone locations and those locations of US were recorded simultaneously. Therefore, we could label bone peaks in the raw US signals. As a result, our method achieved sub-millimeter precision across all eight bone areas with the only exception of one channel in the ankle. This method enables the robust measurement of lower extremity bone positions from 1D raw ultrasound signals. It shows great potential to apply A-mode ultrasound in orthopedic surgery from safe, convenient, and efficient perspectives.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Robotics and Automation, ICRA 2024
PublisherIEEE
Pages921-927
Number of pages7
ISBN (Electronic)979-8-3503-8457-4
DOIs
Publication statusPublished - 2024
Event2024 IEEE International Conference on Robotics and Automation, ICRA 2024 - Yokohama, Japan
Duration: 13 May 202417 May 2024

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2024 IEEE International Conference on Robotics and Automation, ICRA 2024
Country/TerritoryJapan
CityYokohama
Period13/05/2417/05/24

Keywords

  • 2024 OA procedure

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