In this section

Mechatronics in Medicine

The MIM Lab develops robotic and mechatronics surgical systems for a variety of procedures.

Head of Group

B415C Bessemer Building
South Kensington Campus

+44 (0)20 7594 7046

What we do

The Mechatronics in Medicine Laboratory develops robotic and mechatronics surgical systems for a variety of procedures including neuro, cardiovascular, orthopaedic surgeries, and colonoscopies. Examples include bio-inspired catheters that can navigate along complex paths within the brain (such as EDEN2020), soft robots to explore endoluminal anatomies (such as the colon), and virtual reality solutions to support surgeons during knee replacement surgeries.

Meet the team

Mr Ayhan Aktas
Casual - Student demonstrator - lower rate

Dr Daniel Bautista Salinas
Research Associate

Dr Kaiwen Chen
Research Associate

Mr Zejian Cui
Research Assistant

Mr Connor Daly
Research Postgraduate

Emeritus Professor Brian L Davies FREng
Emeritus Professor

Mr Zhaoyang Jacopo Hu
Research Postgraduate

Dr Hisham M Iqbal
Research Associate

Mr Alex Ranne
Research Postgraduate

Professor Ferdinando M Rodriguez y Baena
Co-Director of Hamlyn Centre, Professor of Medical Robotics

Dr Jialei Shi
Research Associate

Mr Spyridon Souipas
Casual - Other work

Ms Emilia Zari
Research Postgraduate

Citation

BibTex format

@article{Liu:2020:10.1109/ACCESS.2020.2977072,
author = {Liu, H and Rodriguez, y Baena F},
doi = {10.1109/ACCESS.2020.2977072},
journal = {IEEE Access},
pages = {42010--42020},
title = {Automatic markerless registration and tracking of the bone for computer-assisted orthopaedic surgery},
url = {http://dx.doi.org/10.1109/ACCESS.2020.2977072},
volume = {8},
year = {2020}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - To achieve a simple and less invasive registration procedure in computer-assisted orthopaedic surgery, we propose an automatic, markerless registration and tracking method based on depth imaging and deep learning. A depth camera is used to continuously capture RGB and depth images of the exposed bone during surgery, and deep neural networks are trained to first localise the surgical target using the RGB image, then segment the target area of the corresponding depth image, from which the surface geometry of the target bone can be extracted. The extracted surface is then compared to a pre-operative model of the same bone for registration. This process can be performed dynamically during the procedure at a rate of 5-6 Hz, without any need for surgeon intervention or invasive optical markers. Ex vivo registration experiments were performed on a cadaveric knee, and accuracy measurements against an optically tracked ground truth resulted in a mean translational error of 2.74 mm and a mean rotational error of 6.66°. Our results are the first to describe a promising new way to achieve automatic markerless registration and tracking in computer-assisted orthopaedic surgery, demonstrating that truly seamless registration and tracking of the limb is within reach. Our method reduces invasiveness by removing the need for percutaneous markers. The surgeon is also exempted from inserting markers and collecting registration points manually, which contributes to a more efficient surgical workflow and shorter procedure time in the operating room.
AU  - Liu,H
AU  - Rodriguez,y Baena F
DO  - 10.1109/ACCESS.2020.2977072
EP  - 42020
PY  - 2020///
SN  - 2169-3536
SP  - 42010
TI  - Automatic markerless registration and tracking of the bone for computer-assisted orthopaedic surgery
T2  - IEEE Access
UR  - http://dx.doi.org/10.1109/ACCESS.2020.2977072
UR  - https://ieeexplore.ieee.org/document/9018195
UR  - http://hdl.handle.net/10044/1/77960
VL  - 8
ER  -