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The Cognitive Vision in Robotic Surgery Lab is developing computer vision and AI techniques for intraoperative navigation and real-time tissue characterisation.
Dr Stamatia (Matina) Giannarou
411 Bessemer Building
South Kensington Campus
+44 (0) 20 7594 8904
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{Elmikaty:2013:10.1049/ic.2012.0122,
author = {Elmikaty, M and Stathaki, T and Kimber, P and Giannarou, S},
doi = {10.1049/ic.2012.0122},
title = {A novel two-level shape descriptor for pedestrian detection},
url = {http://dx.doi.org/10.1049/ic.2012.0122},
year = {2013}
}
TY - CPAPER
AB - The demand for pedestrian detection and tracking algorithms is rapidly increasing with applications in security systems, human computer interaction and human activity analysis. A pedestrian is a person standing in an upright position. Previous work involves using various types of image descriptors to detect humans. However, the existing approaches, although exhibit low misdetection rate, result in high rate of false alarms in the case of complex image backgrounds. In this work, a novel approach for pedestrian detection is proposed which is based on the combined use of two object detection approaches with the aim of reducing the false alarm rate of the individual detectors. These are the Histogram of Oriented Gradients (HOG) and a Shape Context based object detector (SC). Preliminary results are very encouraging and demonstrate clearly the ability of the proposed system to reduce the number of false alarms without significant increase in the processing time.
AU - Elmikaty,M
AU - Stathaki,T
AU - Kimber,P
AU - Giannarou,S
DO - 10.1049/ic.2012.0122
PY - 2013///
TI - A novel two-level shape descriptor for pedestrian detection
UR - http://dx.doi.org/10.1049/ic.2012.0122
UR - http://hdl.handle.net/10044/1/50055
ER -
The Hamlyn Centre
Bessemer Building
South Kensington Campus
Imperial College
London, SW7 2AZ
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