In this section
The Cognitive Vision in Robotic Surgery Lab is developing computer vision and AI techniques for intraoperative navigation and real-time tissue characterisation.
Surgery is undergoing rapid changes driven by recent technological advances and our on-going pursuit towards early intervention and personalised treatment. We are developing computer vision and Artificial Intelligence techniques for intraoperative navigation and real-time tissue characterisation during minimally invasive and robot-assisted operations to improve both the efficacy and safety of surgical procedures. Our work will revolutionize the treatment of cancers and pave the way for autonomous robot-assisted interventions.
With recent advances in medical imaging, sensing, and robotics, surgical oncology is entering a new era of early intervention, personalised treatment, and faster patient recovery. The main goal is to completely remove cancerous tissue while minimising damage to surrounding areas. However, achieving this can be challenging, often leading to imprecise surgeries, high re-excision rates, and reduced quality of life due to unintended injuries. Therefore, technologies that enhance cancer detection and enable more precise surgeries may improve patient outcomes.
Our methods aim to ensure patients receive accurate and timely surgical treatment while reducing surgeons' mental workload, overcoming limitations, and minimizing errors. By improving tumor excision, our hybrid diagnostic and therapeutic tools will lower recurrence rates and enhance survival outcomes. More complete tumor removal will also reduce the need for repeat procedures, improving patient quality of life, life expectancy, and benefiting society and the economy.
Dr Stamatia Giannarou
Senior Lecturer
Dr Po Wen Lo
Research Associate
Mr Alfie Roddan
Research Postgraduate
Mr Alistair G Weld
Research Postgraduate
Mr Chi Xu
Research Assistant
Mr Haozheng Xu
Research Assistant in Surgical Robot Vision
Mr Yihang Zhou
Research Assistant
@inproceedings{Wang:2022:10.1109/IROS47612.2022.9981141,
author = {Wang, C and Cartucho, J and Elson, D and Darzi, A and Giannarou, S},
doi = {10.1109/IROS47612.2022.9981141},
pages = {2395--2401},
publisher = {IEEE},
title = {Towards autonomous control of surgical instruments using adaptive-fusion tracking and robot self-calibration},
url = {http://dx.doi.org/10.1109/IROS47612.2022.9981141},
year = {2022}
}
TY - CPAPER
AB - The ability to track surgical instruments in realtime is crucial for autonomous Robotic Assisted Surgery (RAS). Recently, the fusion of visual and kinematic data has been proposed to track surgical instruments. However, these methods assume that both sensors are equally reliable, and cannot successfully handle cases where there are significant perturbations in one of the sensors' data. In this paper, we address this problem by proposing an enhanced fusion-based method. The main advantage of our method is that it can adjust fusion weights to adapt to sensor perturbations and failures. Another problem is that before performing an autonomous task, these robots have to be repetitively recalibrated by a human for each new patient to estimate the transformations between the different robotic arms. To address this problem, we propose a self-calibration algorithm that empowers the robot to autonomously calibrate the transformations by itself in the beginning of the surgery. We applied our fusion and selfcalibration algorithms for autonomous ultrasound tissue scanning and we showed that the robot achieved stable ultrasound imaging when using our method. Our performance evaluation shows that our proposed method outperforms the state-of-art both in normal and challenging situations.
AU - Wang,C
AU - Cartucho,J
AU - Elson,D
AU - Darzi,A
AU - Giannarou,S
DO - 10.1109/IROS47612.2022.9981141
EP - 2401
PB - IEEE
PY - 2022///
SN - 2153-0858
SP - 2395
TI - Towards autonomous control of surgical instruments using adaptive-fusion tracking and robot self-calibration
UR - http://dx.doi.org/10.1109/IROS47612.2022.9981141
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000908368202002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR - http://hdl.handle.net/10044/1/107117
ER -
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