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.
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{Ye:2013:10.1007/978-3-642-40763-5_9,
author = {Ye, M and Giannarou, S and Patel, N and Teare, J and Yang, G-Z},
doi = {10.1007/978-3-642-40763-5_9},
pages = {67--74},
publisher = {Springer Berlin Heidelberg},
title = {Pathological Site Retargeting under Tissue Deformation Using Geometrical Association and Tracking},
url = {http://dx.doi.org/10.1007/978-3-642-40763-5_9},
year = {2013}
}
TY - CPAPER
AB - Recent advances in microscopic detection techniques includefluorescence spectroscopy, fibred confocal microscopy and optical coher-ence tomography. These methods can be integrated with miniaturisedprobes to assist endoscopy, thus enabling diseases to be detected at anearly and pre-invasive stage, forgoing the need for histopathological sam-ples and off-line analysis. Since optical-based biopsy does not leave vis-ible marks after sampling, it is important to track the biopsy sites toenable accurate retargeting and subsequent serial examination. In thispaper, a novel approach is proposed for pathological site retargeting ingastroscopic examinations. The proposed method is based on affine defor-mation modelling with geometrical association combined with cascadedonline learning and tracking. It provides online in vivo retargeting, and is able to track pathological sites in the presence of tissue deformation. It is also robust to partial occlusions and can be applied to a range of imaging probes including confocal laser endomicroscopy.
AU - Ye,M
AU - Giannarou,S
AU - Patel,N
AU - Teare,J
AU - Yang,G-Z
DO - 10.1007/978-3-642-40763-5_9
EP - 74
PB - Springer Berlin Heidelberg
PY - 2013///
SN - 0302-9743
SP - 67
TI - Pathological Site Retargeting under Tissue Deformation Using Geometrical Association and Tracking
UR - http://dx.doi.org/10.1007/978-3-642-40763-5_9
UR - http://hdl.handle.net/10044/1/27122
ER -
The Hamlyn Centre
Bessemer Building
South Kensington Campus
Imperial College
London, SW7 2AZ
Map location
