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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

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Conference paper
Shi C, Giannarou S, Lee S-L, Yang G-Zet al., 2013,
Intravascular Modelling and Navigation for Transcatheter Aortic Valve Implantation
- Cite
Journal article
Giannarou S, Visentini-Scarzanella M, Yang G-Z, 2013,
Probabilistic Tracking of Affine-Invariant Anisotropic Regions
, IEEE Trans. Pattern Anal. Machine Intell., Vol: 35, Pages: 130-143
- Open Access Link
- Cite
Journal article
Copley SJ, Giannarou S, Schmid VJ, Hansell DM, Wells AU, Yang G-Zet al., 2012,
Effect of Aging on Lung Structure In Vivo: Assessment With Densitometric and Fractal Analysis of High-resolution Computed Tomography Data
, JOURNAL OF THORACIC IMAGING, Vol: 27, Pages: 366-371, ISSN: 0883-5993
- Author Web Link
- Cite
- Citations: 32
Conference paper
Giannarou S, Zhang Z, Yang G-Z, 2012,
Deformable structure from motion by fusing visual and inertial measurement data
, Pages: 4816-4821
- Cite
Journal article
Copley SJ, Giannarou S, Schmid VJ, Hansell DM, Wells AU, Yang GZet al., 2012,
Effect of Ageing on Lung Microstructure in vivo: Assessment with Densitometric and Textural Analysis of High resolution CT Data
, Journal of Thoracic Imaging, Vol: 27, Pages: 366-371
- Cite
Conference paper
Giannarou S, Yang G-Z, 2012,
Tissue Deformation Recovery with Gaussian Mixture Model Based Structure from Motion
, Pages: 47-57
- Cite
Conference paper
Giannarou S, Zhang Z, Yang G-Z, 2012,
Fusion of Visual and Inertial Measurements for 3D Tissue Reconstruction in Minimally Invasive Surgery
- Cite
Journal article
Giannarou S, Stathaki T, 2011,
Optimal Edge Detection Using Multiple Operators for Image Understanding
, EURASIP Journal on Advances in Signal Processing, Vol: 28
- Cite
Book chapter
Giannarou S, Stathaki T, 2011,
A Novel Framework for Object Recognition Under Severe Occlusions
, Advances in Intelligent Signal Processing and Data Mining, Editors: Georgieva, Mihaylova, Jain, Publisher: Springer
- Cite
Conference paper
Giannarou S, Yang G-Z, 2011,
3D Tissue Deformation Recovery for Minimally Invasive Surgery
- Cite

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