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{Mountney:2009:10.1007/978-3-642-04268-3_60,
author = {Mountney, P and Giannarou, S and Elson, D and Yang, GZ},
doi = {10.1007/978-3-642-04268-3_60},
pages = {483--490},
title = {Optical biopsy mapping for minimally invasive cancer screening},
url = {http://dx.doi.org/10.1007/978-3-642-04268-3_60},
year = {2009}
}
TY - CPAPER
AB - The quest for providing tissue characterization and functional mapping during minimally invasive surgery (MIS) has motivated the development of new surgical tools that extend the current functional capabilities of MIS. Miniaturized optical probes can be inserted into the instrument channel of standard endoscopes to reveal tissue cellular and subcellular microstructures, allowing excision-free optical biopsy. One of the limitations of such a point based imaging and tissue characterization technique is the difficulty of tracking probed sites in vivo. This prohibits large area surveillance and integrated functional mapping. The purpose of this paper is to present an image-based tracking framework by combining a semi model-based instrument tracking method with vision-based simultaneous localization and mapping. This allows the mapping of all spatio-temporally tracked biopsy sites, which can then be re-projected back onto the endoscopic video to provide a live augmented view in vivo, thus facilitating re-targeting and serial examination of potential lesions. The proposed method has been validated on phantom data with known ground truth and the accuracy derived demonstrates the strength and clinical value of the technique. The method facilitates a move from the current point based optical biopsy towards large area multi-scale image integration in a routine clinical environment. © 2009 Springer-Verlag.
AU - Mountney,P
AU - Giannarou,S
AU - Elson,D
AU - Yang,GZ
DO - 10.1007/978-3-642-04268-3_60
EP - 490
PY - 2009///
SN - 0302-9743
SP - 483
TI - Optical biopsy mapping for minimally invasive cancer screening
UR - http://dx.doi.org/10.1007/978-3-642-04268-3_60
ER -
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