Imperial College London

DrSu-LinLee

Faculty of EngineeringDepartment of Computing

Lecturer
 
 
 
//

Contact

 

su-lin.lee Website

 
 
//

Location

 

B414ABessemer BuildingSouth Kensington Campus

//

Summary

 

Summary

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.

Publications

Journals

Feng Y, Guo Z, Dong Z, et al., 2017, An efficient cardiac mapping strategy for radiofrequency catheter ablation with active learning, International Journal of Computer Assisted Radiology and Surgery, Vol:12, ISSN:1861-6410, Pages:1199-1207

Feng Y, Guo Z, Dong Z, 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:1861-6410, Pages:1209-1209

Conference

Huang B, Ye M, Lee S, 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

Zhou X, Riga C, Yang G, et al., 3D Shape Recovery of Deployed Stent Grafts from a Single X-ray Image based on Newly Designed Markers, MICCAI Workshop on CVII-STENT 2016

Lee S-L, 2017, Examining the use of a novel dynamic endovascular simulator to facilitate intelligent localization and robotic technologies, Vascular-Societies Annual Scientific Meeting, WILEY, Pages:16-16, ISSN:0007-1323

More Publications