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Human-centred Automation, Robotics and Monitoring

Head of Group

Dr George Mylonas

About us

We use perceptual methods, AI, and frugal robotics innovation to deliver transformative diagnostic and treatment solutions.

Youtube: HARMS Lab

What we do

The HARMS lab leverages perceptually enabled methodologies, artificial intelligence, and frugal innovation in robotics (such as soft surgical robots) to deliver transformative solutions for diagnosis and treatment. Our research is driven by both problem-solving and curiosity, aiming to build a comprehensive understanding of the actions, interactions, and reactions occurring in the operating room. We focus on using robotic technologies to facilitate procedures that are not yet widely adopted, particularly in endoluminal surgery, such as advanced treatments for gastrointestinal cancer.

Why it is important?

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How can it benefit patients?

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Meet the team

Dr George Mylonas BEng, MSc, DIC, PhD
Senior Lecturer in Robotics and Technology in Cancer

Dr Adrian Rubio Solis
Research Associate in Sensing and Machine Learning

Citation

BibTex format

@inproceedings{Stoyanov:2005,
author = {Stoyanov, D and Mylonas, G and Deligianni, F and Darzi, A and Yang, G},
pages = {139--146},
title = {Soft-tissue motion tracking and structure estimation for robotic assisted MIS procedures},
year = {2005}
}

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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - In robotically assisted laparoscopic surgery, soft-tissue motion tracking and structure recovery are important for intraoperative surgical guidance, motion compensation and delivering active constraints. In this paper, we present a novel method for feature based motion tracking of deformable soft-tissue surfaces in totally endoscopic coronary artery bypass graft (TECAB) surgery. We combine two feature detectors to recover distinct regions on the epicardial surface for which the sparse 3D surface geometry may be computed using a pre-calibrated stereo laparoscope. The movement of the 3D points is then tracked in the stereo images with stereo-temporal constrains by using an iterative registration algorithm. The practical value of the technique is demonstrated on both a deformable phantom model with tomographically derived surface geometry and in vivo robotic assisted minimally invasive surgery (MIS) image sequences.
AU  - Stoyanov,D
AU  - Mylonas,G
AU  - Deligianni,F
AU  - Darzi,A
AU  - Yang,G
EP  - 146
PY  - 2005///
SP  - 139
TI  - Soft-tissue motion tracking and structure estimation for robotic assisted MIS procedures
ER  -

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