TY - JOUR
T1 - Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level"
AU - Tirindelli, Maria
AU - Victorova, Maria
AU - Esteban, Javier
AU - Kim, Seong Tae
AU - Navarro-Alarcon, David
AU - Zheng, Yongping
AU - Navab, Nassir
N1 - Funding Information:
Manuscript received February 23, 2020; accepted June 4, 2020. Date of publication July 14, 2020; date of current version July 28, 2020. This letter was recommended for publication by Associate Editor E. De Momi and Editor P. Val-dastri upon evaluation of the Reviewers’ comments. This work was supported by the Bayerische Forschungsstiftung, under Grant DOK-180-19. (Maria Tirindelli and Maria Victorova contributed equally to this work.) (Corresponding author: Maria Tirindelli.) Maria Tirindelli, Javier Esteban, and Seong Tae Kim are with Computer Aided Medical Procedures, Technische Universität München, Munich 80333, Germany (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
KW - Medical robots and systems
KW - computer vision for medical robotics
UR - http://www.scopus.com/inward/record.url?scp=85089889184&partnerID=8YFLogxK
U2 - 10.1109/LRA.2020.3009069
DO - 10.1109/LRA.2020.3009069
M3 - Journal article
SN - 2377-3766
VL - 5
SP - 5661
EP - 5668
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 4
M1 - 9140314
ER -