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【16-03期VALSE Webinar活动】
 报告嘉宾2：李硕（University of Western Ontario）
报告时间：2016年1月20日（星期三）晚21:00（北京时间）
报告题目： Toward
Medical Image Analysis without Segmentation [Slides]
主持人： 何晖光（中科院自动化所）
报告摘要：Direct methods have recently emerged as an effective and
efficient tool in automated medical image analysis and become a trend to solve
diverse challenging tasks in clinical practise. Compared to traditional
methods, direct methods are of much more clinical significance by straightly
targeting to the final clinical goal rather than relying on any intermediate
steps. These intermediate steps, e.g., segmentation, registration and tracking,
are actually not necessary and only limited to very constrained tasks far from
being used in practical clinical applications; moreover they are
computationally expensive and timeconsuming, which causes a high waste of
research resources. The advantages of direct methods stem from 1) removal of
intermediate steps, e.g., segmentation, tracking and registration; 2) avoidance
of user inputs and initialization; 3) reformulation of conventional challenging
problems, e.g., inversion problem, with efficient solutions.
参考文献：
[ 1 ] M.
Afshin, I. B. Ayed, A. Islam, A. Goela, T. M. Peters, S. Li, Global assessment of cardiac function using image
statistics in MRI, in: Medical Image Computing and Computer-Assisted
Intervention-MICCAI 2012, 2012, pp. 535-543.
[ 2 ] Z. Wang, M. Ben Salah, B. Gu,
A. Islam, A. Goela, S. Li, Direct estimation of cardiac bi-ventricular volumes with
an adapted bayesian formulation, IEEE Transactions on Biomedical
Engineering (2014) 1251-1260.
[ 3 ] X. Zhen, Z. Wang, A. Islam,
I. Chan, S. Li, Direct estimation of cardiac bi-ventricular volumes with
regression forests, in: Medical Image Computing and
Computer-Assisted Intervention-MICCAI 2014, 2014.
[ 4 ] X. Zhen, Z. Wang, A. Islam,
M. Bhaduri, I. Chan, S. Li, Multi-scale deep networks and regression forests for
direct bi-ventricular volume estimation, Medical Image Analysis,
2015.
[ 5 ] X. Zhen, A. Islam, M.
Bhaduri, I. Chan, S. Li, Direct and simultaneous four-chamber volume estimatino
by multi-output regression, in: Medical Image Computing and Computer-Assisted
Intervention-MICCAI 2015, Springer, 2015.
[ 6 ] M. Afshin, I. Ben Ayed, K. Punithakumar, M. Law,
A. Islam, A. Goela, T. Peters, S. Li, Regional
assessment of cardiac left ventricular myocardial function via mri statistical
features, IEEE Transactions on Medical Imaging.
报告人简介：
Dr.
Shuo Li is an associate professor in department of medical imaging and medical
biophysics in the University of Western Ontario and scientist in Lawson Health
Research Institute. Before this position he was research scientist and project
manager in general electric (GE) healthcare, Canada for 9 years. He fund and
direct the Digital Imaging Group of London (http://digitalimaginggroup.ca/)
since 2006, which is a very dynamic and highly multiple disciplinary
collaboration group. He received his Ph.D. degree in computer science from
Concordia University 2006, where his PhD thesis won the doctoral prize giving
to the most deserving graduating student in the faculty of engineering and
computer science. He has published over
100 publications; He is the recipient of several GE internal awards; He serves
as guest editors and associate editor in several prestigious journals in the
field; He servers as program committee members in highly influential
conferences; He is the editors of five springer books. His current interest is
development intelligent analytic tools to help physicians and hospital
administrative to handle the big medical data, centered with medical images.
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