Shuo Li

Dr. Shuo Li was an associate professor in the department of medical imaging and medical biophysics at the University of Western Ontario and a scientist in the Lawson Health Research Institute. Before this position he was research scientist and project manager at general electric (GE) healthcare, Canada for 9 years. He founded and has directed the Digital Imaging Group of London, Ontario since 2006, which is a very dynamic and highly multidisciplinary collaboration group. He received his Ph.D. degree in computer science from Concordia University 2006, where his PhD thesis won the doctoral prize given to the most deserving graduating student in the faculty of engineering and computer science. He has published over 100 peer-reviewed articles; he is the recipient of several GE internal awards; he serves as guest editor and associate editor on several prestigious journals in the field; he serves on program committees in highly influential conferences; and he is the editor of five Springer books. His current research interest is the development of intelligent analytic tools to help physicians and hospital administrators handle big medical datasets, centered on medical images.

Associated articles

JTEHM, Articles, Published Articles
Cardiac-DeepIED: Automatic Pixel-level Deep Segmentation for Cardiac Bi-ventricle Using Improved End-to-End Encoder-Decoder Network
     Abstract Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the complex structure of CBV image makes fully automatic segmentation as a well-known challenge.... Read more
JTEHM, Articles, Published Articles
Correlated Regression Feature Learning for Automated Right Ventricle Segmentation
      Abstract: Accurate segmentation of right ventricle (RV) from cardiac magnetic resonance (MR) images can help doctor to robustly quantify the clinical indices including ejection fraction. In this paper, we develop one regression convolutional neural network (RegressionCNN) that a holistic regression model... Read more
JTEHM, Articles, Published Articles
3D Motion Estimation of Left Ventricular Dynamics Using MRI and Track-to-Track Fusion
This study investigates the estimation of three dimensional (3D) left ventricular (LV) motion using the fusion of different two dimensional (2D) cine magnetic resonance (CMR) sequences acquired during routine imaging sessions. Although standard clinical cine CMR data is inherently 2D,... Read more