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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 in Surgical Robot Vision

Mr Yihang Zhou
Research Assistant

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  -