Main content blocks
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.
Research lab info
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
Results
- Showing results for:
- Reset all filters
Search results
-
Conference paperGiannarou S, Yang G-Z, 2010,
Content-Based Surgical Workflow Representation Using Probabilistic Motion Modeling
, Pages: 314-323 -
Journal articleLee SL, M Lerotic VV, Giannarou S, et al., 2010,
From Medical Images to Minimally Invasive Intervention: Computer Assistance for Robotic Surgery
, Computerized Medical Imaging and Graphics (CMIG), Vol: 34, Pages: 33-45-33-45 -
Conference paperMountney P, Giannarou S, Elson D, et al., 2009,
Optical biopsy mapping for minimally invasive cancer screening
, Pages: 483-490, ISSN: 0302-9743The quest for providing tissue characterization and functional mapping during minimally invasive surgery (MIS) has motivated the development of new surgical tools that extend the current functional capabilities of MIS. Miniaturized optical probes can be inserted into the instrument channel of standard endoscopes to reveal tissue cellular and subcellular microstructures, allowing excision-free optical biopsy. One of the limitations of such a point based imaging and tissue characterization technique is the difficulty of tracking probed sites in vivo. This prohibits large area surveillance and integrated functional mapping. The purpose of this paper is to present an image-based tracking framework by combining a semi model-based instrument tracking method with vision-based simultaneous localization and mapping. This allows the mapping of all spatio-temporally tracked biopsy sites, which can then be re-projected back onto the endoscopic video to provide a live augmented view in vivo, thus facilitating re-targeting and serial examination of potential lesions. The proposed method has been validated on phantom data with known ground truth and the accuracy derived demonstrates the strength and clinical value of the technique. The method facilitates a move from the current point based optical biopsy towards large area multi-scale image integration in a routine clinical environment. © 2009 Springer-Verlag.
-
Conference paperSkodras A, Giannarou S, Fenwick M, et al., 2009,
Object recognition in the ovary: Quantification of oocytes from microscopic images
, Pages: 1-6 -
Conference paperGiannarou S, Visentini-Scarzanella M, Yang G-Z, 2009,
Affine-invariant anisotropic detector for soft tissue tracking in minimally invasive surgery
, Pages: 1059-1062 -
Book chapterGiannarou S, Stathaki T, 2009,
Fusion of Edge Maps Using Statistical Approaches
, Image Fusion: Algorithms and Applications, Editors: Stathaki, Publisher: Academic Press -
Conference paperGiannarou S, Elson D, Yang G-Z, 2009,
Tracking of spectroscopic and microscopic optical probes in endoscopy using the endoscope image field
-
Conference paperSkodras A, Giannarou S, Fenwick M, et al., 2009,
OBJECT RECOGNITION IN THE OVARY: QUANTIFICATION OF OOCYTES FROM MICROSCOPIC IMAGES
, 16th International Conference on Digital Signal Processing, Publisher: IEEE, Pages: 345-+- Author Web Link
- Cite
- Citations: 2
-
Conference paperAtasoy S, Glocker B, Giannarou S, et al., 2009,
Probabilistic Region Matching in Narrow-Band Endoscopy for Targeted Optical Biopsy
, Pages: 499-506 -
Conference paperCopley SJ, Giannarou S, Schmid VJ, et al., 2009,
Objective evaluation of pulmonary parenchyma on Thin-section CT using fractal analysis: a comparative study between $>$75 and $<$55 year old individuals
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.
Contact Us
The Hamlyn Centre
Bessemer Building
South Kensington Campus
Imperial College
London, SW7 2AZ
Map location