Imperial College London
Alternate

DrPhilipPratt

Faculty of Medicine, Department of Surgery & Cancer

Honorary Senior Research Fellow
 
 
 
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Contact

 

+44 (0)20 3312 5525p.pratt Website

 
 
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Location

 

005Paterson WingSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zhang:2017:10.1007/978-3-319-66185-8_70,
author = {Zhang, L and Ye, M and Giannarou, S and Pratt, P and Yang, GZ},
doi = {10.1007/978-3-319-66185-8_70},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {619--627},
title = {Motion-compensated autonomous scanning for tumour localisation using intraoperative ultrasound},
url = {http://dx.doi.org/10.1007/978-3-319-66185-8_70},
volume = {10434},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Intraoperative ultrasound facilitates localisation of tumour boundaries during minimally invasive procedures. Autonomous ultrasound scanning systems have been recently proposed to improve scanning accuracy and reduce surgeons’ cognitive load. However, current methods mainly consider static scanning environments typically with the probe pressing against the tissue surface. In this work, a motion-compensated autonomous ultrasound scanning system using the da Vinci® Research Kit (dVRK) is proposed. An optimal scanning trajectory is generated considering both the tissue surface shape and the ultrasound transducer dimensions. An effective vision-based approach is proposed to learn the underlying tissue motion characteristics. The learned motion model is then incorporated into the visual servoing framework. The proposed system has been validated with both phantom and ex vivo experiments.
AU  - Zhang,L
AU  - Ye,M
AU  - Giannarou,S
AU  - Pratt,P
AU  - Yang,GZ
DO  - 10.1007/978-3-319-66185-8_70
EP  - 627
PY  - 2017///
SN  - 0302-9743
SP  - 619
TI  - Motion-compensated autonomous scanning for tumour localisation using intraoperative ultrasound
T2  - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
UR  - http://dx.doi.org/10.1007/978-3-319-66185-8_70
UR  - http://hdl.handle.net/10044/1/53830
VL  - 10434
ER  -
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