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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

@inproceedings{Weld:2023:10.1007/978-3-031-27324-7_8,
author = {Weld, A and Agrawal, A and Giannarou, S},
doi = {10.1007/978-3-031-27324-7_8},
pages = {63--68},
publisher = {Springer Nature Switzerland},
title = {Ultrasound segmentation using a 2D UNet with Bayesian volumetric support},
url = {http://dx.doi.org/10.1007/978-3-031-27324-7_8},
year = {2023}
}

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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We present a novel 2D segmentation neural network design for the segmentation of tumour tissue in intraoperative ultrasound (iUS). Due to issues with brain shift and tissue deformation, pre-operative imaging for tumour resection has limited reliability within the operating room (OR). iUS serves as a tool for improving tumour localisation and boundary delineation. Our proposed method takes inspiration from Bayesian networks. Rather than using a conventional 3D UNet, we develop a technique which samples from the volume around the query slice, and perform multiple segmentation’s which provides volumetric support to improve the accuracy of the segmentation of the query slice. Our results show that our proposed architecture achieves an 0.04 increase in the validation dice score compared to the benchmark network.
AU  - Weld,A
AU  - Agrawal,A
AU  - Giannarou,S
DO  - 10.1007/978-3-031-27324-7_8
EP  - 68
PB  - Springer Nature Switzerland
PY  - 2023///
SN  - 0302-9743
SP  - 63
TI  - Ultrasound segmentation using a 2D UNet with Bayesian volumetric support
UR  - http://dx.doi.org/10.1007/978-3-031-27324-7_8
UR  - https://link.springer.com/chapter/10.1007/978-3-031-27324-7_8
ER  -

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