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Cognitive Vision in Robotic Surgery

Head of Group

Dr Stamatia Giannarou

About us

The Cognitive Vision in Robotic Surgery Lab is developing computer vision and AI techniques for intraoperative navigation and real-time tissue characterisation.

What we do

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.

Why it is important?

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.

How can it benefit patients?

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.

Meet the team

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

Mr Yihang Zhou
Research Assistant

Citation

BibTex format

@inbook{Zhao:2020:10.1016/B978-0-12-818833-0.00011-4,
author = {Zhao, L and Giannarou, S and Lee, SL and Yang, GZ},
booktitle = {Intravascular Ultrasound: From Acquisition to Advanced Quantitative Analysis},
doi = {10.1016/B978-0-12-818833-0.00011-4},
pages = {185--197},
title = {Real-Time Robust Simultaneous Catheter and Environment Modeling for Endovascular Navigation},
url = {http://dx.doi.org/10.1016/B978-0-12-818833-0.00011-4},
year = {2020}
}

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RIS format (EndNote, RefMan)

TY  - CHAP
AB  - Due to the complexity in catheter control and navigation, endovascular procedures are characterized by significant challenges. Real-time recovery of the 3D structure of the vasculature intraoperatively is necessary to visualize the interaction between the catheter and its surrounding environment to facilitate catheter manipulations. Nonionizing imaging techniques such as intravascular ultrasound (IVUS) are increasingly used in vessel reconstruction approaches. To enable accurate recovery of vessel structures, this chapter presents a robust and real-time simultaneous catheter and environment modeling method for endovascular navigation based on IVUS imaging, electromagnetic (EM) sensing as well as the vessel structure information obtained from the preoperative CT/MR imaging. By considering the uncertainty in both the IVUS contour and the EM pose in the proposed nonlinear optimization problem, the proposed algorithm can provide accurate vessel reconstruction, at the same time deal with sensing errors and abrupt catheter motions. Experimental results using two different phantoms, with different catheter motions demonstrated the accuracy of the vessel reconstruction and the potential clinical value of the proposed vessel reconstruction method.
AU  - Zhao,L
AU  - Giannarou,S
AU  - Lee,SL
AU  - Yang,GZ
DO  - 10.1016/B978-0-12-818833-0.00011-4
EP  - 197
PY  - 2020///
SP  - 185
TI  - Real-Time Robust Simultaneous Catheter and Environment Modeling for Endovascular Navigation
T1  - Intravascular Ultrasound: From Acquisition to Advanced Quantitative Analysis
UR  - http://dx.doi.org/10.1016/B978-0-12-818833-0.00011-4
ER  -

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