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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
Mountney P, Giannarou S, Elson D, Yang G-Zet al., 2009,
Optical Biopsy Mapping for Minimally Invasive Cancer Screening
, Pages: 483-490
- Cite
Conference paper
Giannarou S, Stathaki T, 2007,
Object Identification in Complex Scenes using Shape Context Descriptors and Multi-Stage Clustering
, Pages: 244-247
- Cite
Conference paper
Giannarou S, Stathaki T, 2007,
Shape Signature Matching for Object Identification Invariant to Image Transformations and Occlusion
, Pages: 710-717
- Cite
Conference paper
Giannarou S, Stathaki T, 2006,
Novel Statistical Approaches to the Quantitative Combination of Multiple Edge Detectors
, Pages: 184-195
- Cite
Conference paper
Giannarou S, Stathaki T, 2005,
Edge detection using quantitative combination of multiple operators
, Pages: 359-364
- Cite

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