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The Cognitive Vision in Robotic Surgery Lab is developing computer vision and AI techniques for intraoperative navigation and real-time tissue characterisation.
Dr Stamatia (Matina) Giannarou
411 Bessemer Building
South Kensington Campus
+44 (0) 20 7594 8904
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.
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.
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.
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 in Surgical Robot Vision
Mr Yihang Zhou
Research Assistant
@article{Tukra:2021:10.1109/TPAMI.2021.3058410,
author = {Tukra, S and Marcus, HJ and Giannarou, S},
doi = {10.1109/TPAMI.2021.3058410},
journal = {IEEE Trans Pattern Anal Mach Intell},
title = {See-Through Vision with Unsupervised Scene Occlusion Reconstruction.},
url = {http://dx.doi.org/10.1109/TPAMI.2021.3058410},
volume = {PP},
year = {2021}
}
TY - JOUR
AB - Among the greatest of the challenges of Minimally Invasive Surgery (MIS) is the inadequate visualisation of the surgical field through keyhole incisions. Moreover, occlusions caused by instruments or bleeding can completely obfuscate anatomical landmarks, reduce surgical vision and lead to iatrogenic injury. The aim of this paper is to propose an unsupervised end-to-end deep learning framework, based on Fully Convolutional Neural networks to reconstruct the view of the surgical scene under occlusions and provide the surgeon with intraoperative see-through vision in these areas. A novel generative densely connected encoder-decoder architecture has been designed which enables the incorporation of temporal information by introducing a new type of 3D convolution, the so called 3D partial convolution, to enhance the learning capabilities of the network and fuse temporal and spatial information. To train the proposed framework, a unique loss function has been proposed which combines perceptual, reconstruction, style, temporal and adversarial loss terms, for generating high fidelity image reconstructions. Advancing the state-of-the-art, our method can reconstruct the underlying view obstructed by irregularly shaped occlusions of divergent size, location and orientation. The proposed method has been validated on in-vivo MIS video data, as well as natural scenes on a range of occlusion-to-image (OIR) ratios.
AU - Tukra,S
AU - Marcus,HJ
AU - Giannarou,S
DO - 10.1109/TPAMI.2021.3058410
PY - 2021///
TI - See-Through Vision with Unsupervised Scene Occlusion Reconstruction.
T2 - IEEE Trans Pattern Anal Mach Intell
UR - http://dx.doi.org/10.1109/TPAMI.2021.3058410
UR - https://www.ncbi.nlm.nih.gov/pubmed/33566758
VL - PP
ER -
The Hamlyn Centre
Bessemer Building
South Kensington Campus
Imperial College
London, SW7 2AZ
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