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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{Ye:2016:10.1016/j.media.2015.10.003,
author = {Ye, M and Giannarou, S and Meining, A and Yang, G-Z},
doi = {10.1016/j.media.2015.10.003},
journal = {Medical Image Analysis},
pages = {144--157},
title = {Online tracking and retargeting with applications to optical biopsy in gastrointestinal endoscopic examinations},
url = {http://dx.doi.org/10.1016/j.media.2015.10.003},
volume = {30},
year = {2016}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - With recent advances in biophotonics, techniques such as narrow band imaging, confocal laser endomicroscopy, fluorescence spectroscopy, and optical coherence tomography, can be combined with normal white-light endoscopes to provide in vivo microscopic tissue characterisation, potentially avoiding the need for offline histological analysis. Despite the advantages of these techniques to provide online optical biopsy in situ, it is challenging for gastroenterologists to retarget the optical biopsy sites during endoscopic examinations. This is because optical biopsy does not leave any mark on the tissue. Furthermore, typical endoscopic cameras only have a limited field-of-view and the biopsy sites often enter or exit the camera view as the endoscope moves. In this paper, a framework for online tracking and retargeting is proposed based on the concept of tracking-by-detection. An online detection cascade is proposed where a random binary descriptor using Haar-like features is included as a random forest classifier. For robust retargeting, we have also proposed a RANSAC-based location verification component that incorporates shape context. The proposed detection cascade can be readily integrated with other temporal trackers. Detailed performance evaluation on in vivo gastrointestinal video sequences demonstrates the performance advantage of the proposed method over the current state-of-the-art.
AU  - Ye,M
AU  - Giannarou,S
AU  - Meining,A
AU  - Yang,G-Z
DO  - 10.1016/j.media.2015.10.003
EP  - 157
PY  - 2016///
SN  - 1361-8415
SP  - 144
TI  - Online tracking and retargeting with applications to optical biopsy in gastrointestinal endoscopic examinations
T2  - Medical Image Analysis
UR  - http://dx.doi.org/10.1016/j.media.2015.10.003
UR  - https://www.sciencedirect.com/science/article/pii/S1361841515001449
UR  - http://hdl.handle.net/10044/1/26970
VL  - 30
ER  -