In this section

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

@inproceedings{Zhao:2016,
author = {Zhao, L and Giannarou, S and Lee, S and Yang, GZ},
publisher = {Springer},
title = {Registration-free simultaneous catheter and environment modelling},
url = {http://hdl.handle.net/10044/1/33602},
year = {2016}
}

Download

RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Endovascular procedures are challenging to perform due tothe complexity and difficulty in catheter manipulation. The simultaneousrecovery of the 3D structure of the vasculature and the catheter posi-tion  and  orientation  intra-operatively  is  necessary  in  catheter  controland  navigation.  State-of-art  Simultaneous  Catheter  and  EnvironmentModelling provides robust and real-time 3D vessel reconstruction based on real-time intravascular ultrasound (IVUS) imaging and electromagnetic (EM) sensing, but still relies on accurate registration between EM and  pre-operative  data.  In  this  paper,  a  registration-free  vessel  reconstruction method is proposed for endovascular navigation. In the optimisation framework, the EM-CT registration is estimated and updated intra-operatively together with the 3D vessel reconstruction from IVUS, EM and pre-operative data, and thus does not require explicit registration. The proposed algorithm can also deal with global (patient) motion and  periodic  deformation  caused  by  cardiac  motion.  Phantom  and  in-vivo experiments validate the accuracy of the algorithm and the resultsdemonstrate the potential clinical value of the technique.
AU  - Zhao,L
AU  - Giannarou,S
AU  - Lee,S
AU  - Yang,GZ
PB  - Springer
PY  - 2016///
TI  - Registration-free simultaneous catheter and environment modelling
UR  - http://hdl.handle.net/10044/1/33602
ER  -

Download