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{Shen:2017:10.1007/978-3-319-66185-8_21,
author = {Shen, M and Giannarou, S and Shah, PL and Yang, GZ},
doi = {10.1007/978-3-319-66185-8_21},
pages = {182--189},
publisher = {Springer},
title = {Branch: Bifurcation recognition for airway navigation based on structural characteristics},
url = {http://dx.doi.org/10.1007/978-3-319-66185-8_21},
year = {2017}
}
TY - CPAPER
AB - Bronchoscopic navigation is challenging, especially at the level of peripheral airways due to the complicated bronchial structures and the large respiratory motion. The aim of this paper is to propose a localisation approach tailored for navigation in the distal airway branches. Salient regions are detected on the depth maps of video images and CT virtual projections to extract anatomically meaningful areas that represent airway bifurcations. An airway descriptor based on shape context is introduced which encodes both the structural characteristics of the bifurcations and their spatial distribution. The bronchoscopic camera is localised in the airways by minimising the cost of matching the region features in video images to the pre-computed CT depth maps considering both the shape and temporal information. The method has been validated on phantom and in vivo data and the results verify its robustness to tissue deformation and good performance in distal airways.
AU - Shen,M
AU - Giannarou,S
AU - Shah,PL
AU - Yang,GZ
DO - 10.1007/978-3-319-66185-8_21
EP - 189
PB - Springer
PY - 2017///
SN - 0302-9743
SP - 182
TI - Branch: Bifurcation recognition for airway navigation based on structural characteristics
UR - http://dx.doi.org/10.1007/978-3-319-66185-8_21
UR - http://hdl.handle.net/10044/1/53723
ER -
The Hamlyn Centre
Bessemer Building
South Kensington Campus
Imperial College
London, SW7 2AZ
Map location
