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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{Cartucho:2021:10.1080/21681163.2020.1835546,
author = {Cartucho, J and Tukra, S and Li, Y and S, Elson D and Giannarou, S},
doi = {10.1080/21681163.2020.1835546},
journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization},
pages = {331--338},
title = {VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery},
url = {http://dx.doi.org/10.1080/21681163.2020.1835546},
volume = {9},
year = {2021}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Surgical robots rely on robust and efficient computer vision algorithms to be able to intervene in real-time. The main problem, however, is that the training or testing of such algorithms, especially when using deep learning techniques, requires large endoscopic datasets which are challenging to obtain, since they require expensive hardware, ethical approvals, patient consent and access to hospitals. This paper presents VisionBlender, a solution to efficiently generate large and accurate endoscopic datasets for validating surgical vision algorithms. VisionBlender is a synthetic dataset generator that adds a user interface to Blender, allowing users to generate realistic video sequences with ground truth maps of depth, disparity, segmentation masks, surface normals, optical flow, object pose, and camera parameters. VisionBlender was built with special focus on robotic surgery, and examples of endoscopic data that can be generated using this tool are presented. Possible applications are also discussed, and here we present one of those applications where the generated data has been used to train and evaluate state-of-the-art 3D reconstruction algorithms. Being able to generate realistic endoscopic datasets efficiently, VisionBlender promises an exciting step forward in robotic surgery.
AU  - Cartucho,J
AU  - Tukra,S
AU  - Li,Y
AU  - S,Elson D
AU  - Giannarou,S
DO  - 10.1080/21681163.2020.1835546
EP  - 338
PY  - 2021///
SN  - 2168-1163
SP  - 331
TI  - VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery
T2  - Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
UR  - http://dx.doi.org/10.1080/21681163.2020.1835546
UR  - https://www.tandfonline.com/doi/full/10.1080/21681163.2020.1835546
UR  - http://hdl.handle.net/10044/1/85733
VL  - 9
ER  -