In this section

Cognitive Vision in Robotic Surgery

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

Head of Group

Dr Stamatia (Matina) Giannarou

411 Bessemer Building
South Kensington Campus

+44 (0) 20 7594 8904

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{Maier-Hein:2017:10.1038/s41551-017-0132-7,
author = {Maier-Hein, L and Vedula, SS and Speidel, S and Navab, N and Kikinis, R and Park, A and Eisenmann, M and Feussner, H and Forestier, G and Giannarou, S and Hashizume, M and Katic, D and Kenngott, H and Kranzfelder, M and Malpani, A and Maerz, K and Neumuth, T and Padoy, N and Pugh, C and Schoch, N and Stoyanov, D and Taylor, R and Wagner, M and Hager, GD and Jannin, P},
doi = {10.1038/s41551-017-0132-7},
journal = {Nature Biomedical Engineering},
pages = {691--696},
title = {Surgical data science for next-generation interventions},
url = {http://dx.doi.org/10.1038/s41551-017-0132-7},
volume = {1},
year = {2017}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.
AU  - Maier-Hein,L
AU  - Vedula,SS
AU  - Speidel,S
AU  - Navab,N
AU  - Kikinis,R
AU  - Park,A
AU  - Eisenmann,M
AU  - Feussner,H
AU  - Forestier,G
AU  - Giannarou,S
AU  - Hashizume,M
AU  - Katic,D
AU  - Kenngott,H
AU  - Kranzfelder,M
AU  - Malpani,A
AU  - Maerz,K
AU  - Neumuth,T
AU  - Padoy,N
AU  - Pugh,C
AU  - Schoch,N
AU  - Stoyanov,D
AU  - Taylor,R
AU  - Wagner,M
AU  - Hager,GD
AU  - Jannin,P
DO  - 10.1038/s41551-017-0132-7
EP  - 696
PY  - 2017///
SN  - 2157-846X
SP  - 691
TI  - Surgical data science for next-generation interventions
T2  - Nature Biomedical Engineering
UR  - http://dx.doi.org/10.1038/s41551-017-0132-7
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000418859300007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/62866
VL  - 1
ER  -