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
@article{Shen:2015:10.1007/s11548-015-1197-y,
author = {Shen, M and Giannarou, S and Yang, G-Z},
doi = {10.1007/s11548-015-1197-y},
journal = {International Journal of Computer Assisted Radiology and Surgery},
pages = {801--813},
title = {Robust camera localisation with depth reconstruction for bronchoscopic navigation},
url = {http://dx.doi.org/10.1007/s11548-015-1197-y},
volume = {10},
year = {2015}
}
TY - JOUR
AB - PurposeBronchoscopy is a standard technique for airway examination, providing a minimally invasive approach for both diagnosis and treatment of pulmonary diseases. To target lesions identified pre-operatively, it is necessary to register the location of the bronchoscope to the CT bronchial model during the examination. Existing vision-based techniques rely on the registration between virtually rendered endobronchial images and videos based on image intensity or surface geometry. However, intensity-based approaches are sensitive to illumination artefacts, while gradient-based approaches are vulnerable to surface texture.MethodsIn this paper, depth information is employed in a novel way to achieve continuous and robust camera localisation. Surface shading has been used to recover depth from endobronchial images. The pose of the bronchoscopic camera is estimated by maximising the similarity between the depth recovered from a video image and that captured from a virtual camera projection of the CT model. The normalised cross-correlation and mutual information have both been used and compared for the similarity measure.ResultsThe proposed depth-based tracking approach has been validated on both phantom and in vivo data. It outperforms the existing vision-based registration methods resulting in smaller pose estimation error of the bronchoscopic camera. It is shown that the proposed approach is more robust to illumination artefacts and surface texture and less sensitive to camera pose initialisation.ConclusionsA reliable camera localisation technique has been proposed based on depth information for bronchoscopic navigation. Qualitative and quantitative performance evaluations show the clinical value of the proposed framework.
AU - Shen,M
AU - Giannarou,S
AU - Yang,G-Z
DO - 10.1007/s11548-015-1197-y
EP - 813
PY - 2015///
SN - 1861-6410
SP - 801
TI - Robust camera localisation with depth reconstruction for bronchoscopic navigation
T2 - International Journal of Computer Assisted Radiology and Surgery
UR - http://dx.doi.org/10.1007/s11548-015-1197-y
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000355422800013&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/43865
VL - 10
ER -
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