Biomedical Engineering Reference
In-Depth Information
Human Supervisory Control
Framework for Interactive Medical
Image Segmentation
Ivan Kolesov, Peter Karasev, Grant Muller,
Karol Chudy, John Xerogeanes, and Allen Tannenbaum
Abstract In this work, interactive segmentation is integrated with an active con-
tour model, and segmentation is posed as a human-supervisory-control problem.
User input is tightly coupled with an automatic segmentation algorithm leveraging
the user's high-level anatomical knowledge and the automated method's speed.
Real-time visualization enables the user to quickly identify and correct the result in
a subdomain where the variational model's statistical assumptions do not agree
with his expert knowledge. Methods developed in this work are applied to magnetic
resonance imaging (MRI) volumes as part of a population study of human skeletal
development. Segmentation time is reduced by approximately five times over
similarly accurate manual segmentation of large bone structures.
1
Introduction
A driving clinical study for the present work is a population study of skeletal develop-
ment in youth. Bone grows from the
physis
(growth plate), located in the middle of a
long bone between the
epiphysis
and
metaphysis
. Full adult growth is reachedwhen the
physis disappears completely. Precise understanding of how the growth plates in
femur and tibia change from childhood to adulthood enables improved surgical
I. Kolesov (
*
) P. Karasev
School of Electrical & Computer Engineering, Georgia Institute of Technology,
Atlanta, GA, USA
e-mail:
ivan.kolesov@gatech.edu
G. Muller J. Xerogeanes
Division Department of Orthopedic Surgery, Emory University, Atlanta, GA, USA
K. Chudy
School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
A. Tannenbaum
School of Electrical and Computer Engineering, Boston University, Boston, MA, USA
