[BIC-announce] SEMINAR announcement Quantifying and Visualizing Uncertainty in the Operating Room

[Jennifer Chew, Ms.]

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From: cim-faculty-bounces at cim.mcgill.ca
[mailto:cim-faculty-bounces at cim.mcgill.ca] On Behalf Of Cynthia Davidson
Sent: Tuesday, September 30, 2008 2:19 PM
To: cim-all at cim.mcgill.ca
Subject: [Fwd: SEMINAR announcement]

CIM Seminar: "Quantifying and Visualizing Uncertainty in the Operating
Room"
Amber Simson, PhD candidate, Queen's University October 6, 3:30-4:30pm
CIM Seminar room (McConnell 437)

Quantifying and Visualizing Uncertainty in the Operating Room

Measurement uncertainty corrupts data at all stages of computer-assisted
surgery: during imaging, 3D model construction, calibration,
registration, and instrument tracking. Uncertainty has been ignored by
computer-assisted surgery research and commercial surgical systems.
Anatomical data is presented to the surgeon as though it were perfect.
A surgeon who is not aware of this uncertainty can make critical errors
(consider, for example, inserting screws into the spine). We contribute
a method of estimating uncertainty in tracking, calibration, and
registration in real time in the operating room. We show that our method
produces reliable estimates of uncertainty.  We compare methods of
conveying uncertainty information in a user study. Our work addresses
the existence of uncertainty in surgery and offers a first step towards
helping surgeons make informed decisions in the presence of imperfect
data.

Bio: Amber Simpson is a PhD student in the School of Computing at
Queen's University, Kingston, Ontario. She is supervised by Randy Ellis
and James Stewart. She received her BSc (2000) in computer science and
mathematics from Trent University and her MSc (2002) in computer science
from Queen's University. She works closely with the Human Mobility
Research Centre at Kingston General Hospital. Amber's research interests
span medical robotics, human-computer interaction, and visualization.
Her method of computer-assisted deformity correction has been performed
on six patients to date. A major current research focus is improving the
quality of error and uncertainty information given to surgeons in the
operating room. In addition, Amber teaches Lego robotics through
Computing Unplugged, a program, supported by an NSERC Promoscience
grant, that offers outreach and mentorship to girls in grades five and
six. She also coached the Queen's Programming Contest Team; placing 12th
at the world finals contest in Prague in 2004 and placing 27th in
Honolulu in 2002.


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Tal Arbel, Assistant Professor
Dept. of Electrical and Computer Engineering        Tel. (514) 398-8204
Centre for Intelligent Machines                Fax. (514) 398-7348
McConnell Engineering Building, room 425                        McGill 
University                    arbel at cim.mcgill.ca
3480 University, Montreal, Quebec, Canada H3A 2A7
http://www.cim.mcgill.ca/~arbel
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