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Cognitive Vision in Robotic Surgery

The Cognitive Vision in Robotic Surgery Lab is developing computer vision and AI techniques for intraoperative navigation and real-time tissue characterisation.

Head of Group

Dr Stamatia (Matina) Giannarou

411 Bessemer Building
South Kensington Campus

+44 (0) 20 7594 8904

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 in Surgical Robot Vision

Mr Yihang Zhou
Research Assistant

Citation

BibTex format

@article{Weld:2024:10.1007/s00701-024-06179-8,
author = {Weld, A and Dixon, L and Anichini, G and Patel, N and Nimer, A and Dyck, M and O'Neill, K and Lim, A and Giannarou, S and Camp, S},
doi = {10.1007/s00701-024-06179-8},
journal = {Acta Neurochirurgica: the European journal of neurosurgery},
title = {Challenges with segmenting intraoperative ultrasound for brain tumours},
url = {http://dx.doi.org/10.1007/s00701-024-06179-8},
volume = {166},
year = {2024}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Objective - Addressing the challenges that come with identifying and delineating brain tumours in intraoperative ultrasound. Our goal is to both qualitatively and quantitatively assess the interobserver variation, amongst experienced neuro-oncological intraoperative ultrasound users (neurosurgeons and neuroradiologists), in detecting and segmenting brain tumours on ultrasound. We then propose that, due to the inherent challenges of this task, annotation by localisation of the entire tumour mass with a bounding box could serve as an ancillary solution to segmentation for clinical training, encompassing margin uncertainty and the curation of large datasets. Methods - 30 ultrasound images of brain lesions in 30 patients were annotated by 4 annotators - 1 neuroradiologist and 3 neurosurgeons. The annotation variation of the 3 neurosurgeons was first measured, and then the annotations of each neurosurgeon were individually compared to the neuroradiologist's, which served as a reference standard as their segmentations were further refined by cross-reference to the preoperative magnetic resonance imaging (MRI). The following statistical metrics were used: Intersection Over Union (IoU), Sørensen-Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). These annotations were then converted into bounding boxes for the same evaluation. Results - There was a moderate level of interobserver variance between the neurosurgeons [ I o U : 0.789 ,  D S C : 0.876 ,  H D : 103.227 ]  and a larger level of variance when compared against the MRI-informed reference standard annotations by the neuroradiologist, mean across annotators [ I o U : 0.723 ,  D S C : 0.813 ,  H D : 115.675 ]  . After converting the segments to bounding boxes, all metrics improve, most significantly, the interquartile range drops by [ I o U : 37 % ,  D S C : 41 % ,  H D : 54 % ]  . Conclusion - This study highlights the current challenges with detecting and defining tumour boundaries in neuro-oncologi
AU  - Weld,A
AU  - Dixon,L
AU  - Anichini,G
AU  - Patel,N
AU  - Nimer,A
AU  - Dyck,M
AU  - O'Neill,K
AU  - Lim,A
AU  - Giannarou,S
AU  - Camp,S
DO  - 10.1007/s00701-024-06179-8
PY  - 2024///
SN  - 0001-6268
TI  - Challenges with segmenting intraoperative ultrasound for brain tumours
T2  - Acta Neurochirurgica: the European journal of neurosurgery
UR  - http://dx.doi.org/10.1007/s00701-024-06179-8
UR  - https://www.ncbi.nlm.nih.gov/pubmed/39090435
UR  - https://link.springer.com/article/10.1007/s00701-024-06179-8
UR  - http://hdl.handle.net/10044/1/113810
VL  - 166
ER  -