Abstract #0851
Three-Dimensional Pulmonary 1 H MRI Multi-Region Segmentation Using Convex Optimization
Fumin Guo 1,2 , Sarah Svenningsen 1,3 , Aaron Fenster 1,2 , and Grace Parraga 1,2
1
Imaging Research Laboratories, Robarts
Research Institute, London, Ontario, Canada,
2
Graduate
Program in Biomedical Engineering, The University of
Western Ontario, London, Ontario, Canada,
3
Department
of Medical Biophysics, The University of Western
Ontario, London, Ontario, Canada
Many applications of pulmonary
1
H
MRI require lung cavity segmentation as a prerequisite.
Accordingly, we proposed a convex optimization based
approach to simultaneously segment the right and left
lungs from pulmonary
1
H
MRI in 3D. Our approach employs the latest developments
in convex optimization techniques and solves the
original challenging optimization problem globally and
exactly under the primal and dual perspective. We
implemented the algorithm in a modern parallel computing
platform and applied it to a clinical dataset of ten
COPD subjects. Our experimental results demonstrate that
this computationally efficient method yields highly
accurate lung volumes with minimal user interaction.
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