Surgical Robot Vision

Our work is focused on developing new algorithms, devices and systems for improving diagnostic and therapeutic interventions.

About us

We are a research team working on computer-assisted interventions within the Wellcome / EPSRC Centre for Interventional and Surgical Sciences and the UCL Computer Science department. We are also part of the Centre for Medical Image Computing (CMIC) and the UCL Robotics Institute. 

We work on computer vision and artificial intelligence for understanding surgical images, robotics for creating better surgical tools, and augmented reality for improving surgical visualization and navigation.

The people

Find out more about the people behind the UCL Surgical Robot Vision research group.

Principal Investigator

Dan Stoyanov

Executive Assistant

Laura Marmor (l.marmor@ucl.ac.uk)

Post-doctoral researchers

Sophia Bano

François Chadebecq

Neil Clancy

Eddie Edwards

Evangelos Mazomenos

Ji Qi

Agostino Stilli

Francisco Vasconcelos

PhD students

Patrick Brandão

Claudia D'Ettorre

Geogre Dwyer

Mirek Janatka

Geoff Jones

Rene Lacher

Krittin Pachtrachai

Anita Rau

Lydia Zajiczek

Alumni

Max Allen

Ping-Ling Chang

Xiaofei Du

Yusuf Helo

Martin Kocha

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Publications

Publications by the Surgical Robot Vision research group. Complete lists of the group’s or individual members’ publications can be found from Google Scholar or the UCL IRIS service.

CHESS—Calibrating the Hand-Eye Matrix With Screw Constraints and Synchronization K Pachtrachai, F Vasconcelos, G Dwyer, V Pawar, S Hailes, D Stoyanov. IEEE Robotics and Automation Letters. (2018)

Arthroscopic simulation using a knee model can be used to train speed and gaze strategies in knee arthroscopy. VVG An, Y Mirza, E Mazomenos, F Vasconcelos, D Stoyanov, S Oussedik. The Knee. (2018)

Higher Order of Motion Magnification for Vessel Localisation in Surgical Video. M Janatka, A Sridhar, J Kelly, D Stoyanov. MICCAI. (2018)

Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks. EB Mazomenos, K Bansal, B Martin, A Smith, S Wright, D Stoyanov. MICCAI. (2018)

Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks. Patrick Brandao, Odysseas Zisimopoulos, Evangelos Mazomenos, Gastone Ciuti, Jorge Bernal, Marco Visentini-Scarzanella, Arianna Menciassi, Paolo Dario, Anastasios Koulaouzidis, Alberto Arezzo, David J Hawkes, Danail Stoyanov. Journal of Medical Robotics Research (2018)

3-D Pose Estimation of Articulated Instruments in Robotic Minimally Invasive Surgery. M Allan, S Ourselin, DJ Hawkes, JD Kelly, D Stoyanov. IEEE Transactions on Medical Imaging. (2018)

Automated pick-up of suturing needles for robotic surgical assistance. C D’Ettorre, G Dwyer, X Du, F Chadebecq, F Vasconcelos, E De Momi, D Stoyanov. ICRA. (2018)

Augmented reality needle ablation guidance tool for irreversible electroporation in the pancreas. Timur Kuzhagaliyev, Neil T Clancy, Mirek Janatka, Kevin Tchaka, Francisco Vasconcelos, Matthew J Clarkson, Kurinchi Gurusamy, David J Hawkes, Brian Davidson, Danail Stoyanov. Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling. (2018)

Relative pose estimation from image correspondences under a remote center of motion constraint. F Vasconcelos, E Mazomenos, J Kelly, S Ourselin, D Stoyanov. IEEE Robotics and Automation Letters. (2018)

Articulated Multi-Instrument 2D Pose Estimation Using Fully Convolutional Networks. Xiaofei Du, Thomas Kurmann, Ping-Lin Chang, Maximilian Allan, Sebastien Ourselin, Raphael Sznitman, John D Kelly, Danail Stoyanov. IEEE Transactions on Medical Imaging. (2018)

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Resources

Find useful information generated by the research group as well as the facilities available.

github code

To see more on what we have been working on, visit our Github page.

Data

page/link

Facilities

page/link

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