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{Xu:2023:10.1109/icra48891.2023.10160287,
author = {Xu, H and Runciman, M and Cartucho, J and Xu, C and Giannarou, S},
doi = {10.1109/icra48891.2023.10160287},
pages = {2731--2737},
publisher = {IEEE},
title = {Graph-based pose estimation of texture-less surgical tools for autonomous robot control},
url = {http://dx.doi.org/10.1109/icra48891.2023.10160287},
year = {2023}
}
TY - CPAPER
AB - In Robot-assisted Minimally Invasive Surgery (RMIS), the estimation of the pose of surgical tools is crucial for applications such as surgical navigation, visual servoing, autonomous robotic task execution and augmented reality. A plethora of hardware-based and vision-based methods have been proposed in the literature. However, direct application of these methods to RMIS has significant limitations due to partial tool visibility, occlusions and changes in the surgical scene. In this work, a novel keypoint-graph-based network is proposed to estimate the pose of texture-less cylindrical surgical tools of small diameter. To deal with the challenges in RMIS, keypoint object representation is used and for the first time, temporal information is combined with spatial information in keypoint graph representation, for keypoint refinement. Finally, stable and accurate tool pose is computed using a PnP solver. Our performance evaluation study has shown that the proposed method is able to accurately predict the pose of a textureless robotic shaft with an ADD-S score of over 98%. The method outperforms state-of-the-art pose estimation models under challenging conditions such as object occlusion and changes in the lighting of the scene.
AU - Xu,H
AU - Runciman,M
AU - Cartucho,J
AU - Xu,C
AU - Giannarou,S
DO - 10.1109/icra48891.2023.10160287
EP - 2737
PB - IEEE
PY - 2023///
SP - 2731
TI - Graph-based pose estimation of texture-less surgical tools for autonomous robot control
UR - http://dx.doi.org/10.1109/icra48891.2023.10160287
UR - http://hdl.handle.net/10044/1/106522
ER -
The Hamlyn Centre
Bessemer Building
South Kensington Campus
Imperial College
London, SW7 2AZ
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