Su-Lin Lee received her MEng in Information Systems Engineering in the Department of Electrical and Electronic Engineering in 2002 and her PhD on medical image computing in the Department of Computing in 2006 at Imperial College London. She is currently a lecturer with the Hamlyn Centre for Robotic Surgery. Previous projects include virtual tagging using velocity mapping of the myocardium and whole body modelling on the EPSRC project on Imaging based subject-specific RF simulation environment for wearable and implantable wireless Body Sensor Networks (iRFSim for BSNs).
Previously, Dr Lee worked on SCATh – Smart Catheterization, a project funded by the 7th Framework Programme of the European Commission, and is currently involved on CASCADE (Cognitive AutonomouS CAtheters operating in Dynamic Environments). She serves as a reviewer in numerous research conferences and journals (including MICCAI and IEEE TMI) and is Associate Editor for the Journal of Medical Robotics Research (JMRR).
Dr Lee's current research interests are on machine learning with application to navigation in cardiovascular interventions. Current projects focus on novel visualisation methods for endovascular procedures and efficient robotic catheter manoeuvres for cardiac electrophysiology mapping.
et al., 2018, Probabilistic guidance for catheter tip motion in cardiac ablation procedures, Medical Image Analysis, Vol:47, ISSN:1361-8415, Pages:1-14
et al., 2018, A multi-robot cooperation framework for sewing personalized stent grafts, Ieee Transactions on Industrial Informatics, Vol:14, ISSN:1551-3203, Pages:1776-1785
Zhou X, Yang G, Lee S, 2017, A Real-time and Registration-free Framework for Dynamic Shape Instantiation, Medical Image Analysis, Vol:44, ISSN:1361-8415, Pages:86-97
et al., 2017, An efficient cardiac mapping strategy for radiofrequency catheter ablation with active learning (vol 12, pg 1199, 2017), International Journal of Computer Assisted Radiology and Surgery, Vol:12, ISSN:1861-6429, Pages:1209-1209
et al., A Vision-Guided Multi-Robot Cooperation Framework for Learning-By-Demonstration and Task Reproduction, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE