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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{Cartucho:2021:10.1080/21681163.2021.1997647,
author = {Cartucho, J and Wang, C and Huang, B and Elson, DS and Darzi, A and Giannarou, S},
doi = {10.1080/21681163.2021.1997647},
journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
pages = {1--9},
title = {An enhanced marker pattern that achieves improved accuracy in surgical tool tracking},
url = {http://dx.doi.org/10.1080/21681163.2021.1997647},
volume = {10},
year = {2021}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In computer assisted interventions (CAI), surgical tool tracking is crucial for applications such as surgical navigation, surgical skill assessment, visual servoing, and augmented reality. Tracking of cylindrical surgical tools can be achieved by printing and attaching a marker to their shaft. However, the tracking error of existing cylindrical markers is still in the millimetre range, which is too large for applications such as neurosurgery requiring sub-millimetre accuracy. To achieve tool tracking with sub-millimetre accuracy, we designed an enhanced marker pattern, which is captured on images from a monocular laparoscopic camera. The images are used as input for a tracking method which is described in this paper. Our tracking method was compared to the state-of-the-art, on simulation and ex vivo experiments. This comparison shows that our method outperforms the current state-of-the-art. Our marker achieves a mean absolute error of 0.28 [mm] and 0.45 [°] on ex vivo data, and 0.47 [mm] and 1.46 [°] on simulation. Our tracking method is real-time and runs at 55 frames per second for 720×576 image resolution.
AU  - Cartucho,J
AU  - Wang,C
AU  - Huang,B
AU  - Elson,DS
AU  - Darzi,A
AU  - Giannarou,S
DO  - 10.1080/21681163.2021.1997647
EP  - 9
PY  - 2021///
SN  - 2168-1163
SP  - 1
TI  - An enhanced marker pattern that achieves improved accuracy in surgical tool tracking
T2  - Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
UR  - http://dx.doi.org/10.1080/21681163.2021.1997647
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000716888200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.tandfonline.com/doi/full/10.1080/21681163.2021.1997647?scroll=top&needAccess=true
UR  - http://hdl.handle.net/10044/1/93275
VL  - 10
ER  -