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Cognitive Vision in Robotic Surgery

Head of Group

Dr Stamatia Giannarou

About us

The Cognitive Vision in Robotic Surgery Lab is developing computer vision and AI techniques for intraoperative navigation and real-time tissue characterisation.

What we do

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.

Why it is important?

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.

How can it benefit patients?

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.

Meet the team

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

Mr Yihang Zhou
Research Assistant

Citation

BibTex format

@article{Giannarou:2016:10.1007/s11548-016-1361-z,
author = {Giannarou, S and Ye, M and Gras, G and Leibrandt, K and Marcus, HJ and Yang, GZ},
doi = {10.1007/s11548-016-1361-z},
journal = {International Journal of Computer Assisted Radiology and Surgery},
pages = {929--936},
title = {Vision-based deformation recovery for intraoperative force estimation of tool–tissue interaction for neurosurgery},
url = {http://dx.doi.org/10.1007/s11548-016-1361-z},
volume = {11},
year = {2016}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Purpose: In microsurgery, accurate recovery of the deformation of the surgical environment is important for mitigating the risk of inadvertent tissue damage and avoiding instrument maneuvers that may cause injury. The analysis of intraoperative microscopic data can allow the estimation of tissue deformation and provide to the surgeon useful feedback on the instrument forces exerted on the tissue. In practice, vision-based recovery of tissue deformation during tool–tissue interaction can be challenging due to tissue elasticity and unpredictable motion.Methods: The aim of this work is to propose an approach for deformation recovery based on quasi-dense 3D stereo reconstruction. The proposed framework incorporates a new stereo correspondence method for estimating the underlying 3D structure. Probabilistic tracking and surface mapping are used to estimate 3D point correspondences across time and recover localized tissue deformations in the surgical site.Results: We demonstrate the application of this method to estimating forces exerted on tissue surfaces. A clinically relevant experimental setup was used to validate the proposed framework on phantom data. The quantitative and qualitative performance evaluation results show that the proposed 3D stereo reconstruction and deformation recovery methods achieve submillimeter accuracy. The force–displacement model also provides accurate estimates of the exerted forces.Conclusions: A novel approach for tissue deformation recovery has been proposed based on reliable quasi-dense stereo correspondences. The proposed framework does not rely on additional equipment, allowing seamless integration with the existing surgical workflow. The performance evaluation analysis shows the potential clinical value of the technique.
AU  - Giannarou,S
AU  - Ye,M
AU  - Gras,G
AU  - Leibrandt,K
AU  - Marcus,HJ
AU  - Yang,GZ
DO  - 10.1007/s11548-016-1361-z
EP  - 936
PY  - 2016///
SN  - 1861-6410
SP  - 929
TI  - Vision-based deformation recovery for intraoperative force estimation of tool–tissue interaction for neurosurgery
T2  - International Journal of Computer Assisted Radiology and Surgery
UR  - http://dx.doi.org/10.1007/s11548-016-1361-z
UR  - https://link.springer.com/article/10.1007/s11548-016-1361-z
UR  - http://hdl.handle.net/10044/1/30946
VL  - 11
ER  -