In this section
The Cognitive Vision in Robotic Surgery Lab is developing computer vision and AI techniques for intraoperative navigation and real-time tissue characterisation.
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.
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.
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.
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
@inproceedings{Ye:2014:10.1007/978-3-319-10470-6_40,
author = {Ye, M and Johns, E and Giannarou, S and Yang, G-Z},
doi = {10.1007/978-3-319-10470-6_40},
pages = {316--323},
publisher = {Springer International Publishing},
title = {Online Scene Association for Endoscopic Navigation},
url = {http://dx.doi.org/10.1007/978-3-319-10470-6_40},
year = {2014}
}
TY - CPAPER
AB - Endoscopic surveillance is a widely used method for moni-toring abnormal changes in the gastrointestinal tract such as Barrett'sesophagus. Direct visual assessment, however, is both time consumingand error prone, as it involves manual labelling of abnormalities on alarge set of images. To assist surveillance, this paper proposes an onlinescene association scheme to summarise an endoscopic video into scenes,on-the-y. This provides scene clustering based on visual contents, andalso facilitates topological localisation during navigation. The proposedmethod is based on tracking and detection of visual landmarks on thetissue surface. A generative model is proposed for online learning of pair-wise geometrical relationships between landmarks. This enables robustdetection of landmarks and scene association under tissue deformation.Detailed experimental comparison and validation have been conductedon in vivo endoscopic videos to demonstrate the practical value of ourapproach.
AU - Ye,M
AU - Johns,E
AU - Giannarou,S
AU - Yang,G-Z
DO - 10.1007/978-3-319-10470-6_40
EP - 323
PB - Springer International Publishing
PY - 2014///
SN - 0302-9743
SP - 316
TI - Online Scene Association for Endoscopic Navigation
UR - http://dx.doi.org/10.1007/978-3-319-10470-6_40
UR - http://hdl.handle.net/10044/1/27120
ER -
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