Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level"

Maria Tirindelli, Maria Victorova, Javier Esteban, Seong Tae Kim, David Navarro-Alarcon, Yongping Zheng, Nassir Navab

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Spine injections are commonly performed in several clinical procedures. The localization of the target vertebral level (i.e. the position of a vertebra in a spine) is typically done by back palpation or under X-ray guidance, yielding either higher chances of procedure failure or exposure to ionizing radiation. Preliminary studies have been conducted in the literature, suggesting that ultrasound imaging may be a precise and safe alternative to X-ray for spine level detection. However, ultrasound data are noisy and complicated to interpret. In this study, a robotic-ultrasound approach for automatic vertebral level detection is introduced. The method relies on the fusion of ultrasound and force data, thus providing both 'tactile' and visual feedback during the procedure, which results in higher performances in presence of data corruption. A robotic arm automatically scans the volunteer's back along the spine by using force-ultrasound data to locate vertebral levels. The occurrences of vertebral levels are visible on the force trace as peaks, which are enhanced by properly controlling the force applied by the robot on the patient back. Ultrasound data are processed with a Deep Learning method to extract a 1D signal modelling the probabilities of having a vertebra at each location along the spine. Processed force and ultrasound data are fused using both a non deep learning method and a Temporal Convolutional Network to compute the locations of the vertebral levels. The benefits of fusing force and image signals for the identification of vertebrae locations are showcased through extensive evaluation.

Original languageEnglish
Article number9140314
Pages (from-to)5661-5668
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume5
Issue number4
DOIs
Publication statusPublished - Jun 2020

Keywords

  • Medical robots and systems
  • computer vision for medical robotics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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