Partial Differential Equations-Based Segmentation for Radiotherapy Treatment Planning
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1.
Department of Computer Science and Department of Mechanical Engineering, University of California at Santa Barbara, CA 93106-5070
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2.
Department of Mathematics, Stanford University, Stanford, CA 94305-2125
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3.
Siemens Medical Solutions, Med SW West, 755 College Road East, Princeton, NJ 08540
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4.
Department of Radiation Oncology, Stanford University, Stanford, CA 94305
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Received:
01 October 2004
Accepted:
29 June 2018
Published:
01 March 2005
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MSC :
primary 92C55; secondary 92C50.
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The purpose of this study is to develop automatic algorithms for the segmentation phase of radiotherapy treatment
planning. We develop new image processing techniques that are based on solving a partial differential equation for the
evolution of the curve that identifies the segmented organ. The velocity function is based on the piecewise
Mumford-Shah functional. Our method incorporates information about the target organ into classical segmentation
algorithms. This information, which is given in terms of a three-dimensional wireframe representation of the organ,
serves as an initial guess for the segmentation algorithm. We check the performance of the new algorithm on eight data
sets of three different organs: rectum, bladder, and kidney. The results of the automatic segmentation were compared
with a manual segmentation of each data set by radiation oncology faculty and residents. The quality of the automatic
segmentation was measured with the ''$\kappa$-statistics'', and with a count of over- and undersegmented frames, and
was shown in most cases to be very close to the manual segmentation of the same data. A typical segmentation of an
organ with sixty slices takes less than ten seconds on a Pentium IV laptop.
Citation: Frédéric Gibou, Doron Levy, Carlos Cárdenas, Pingyu Liu, Arthur Boyer. Partial Differential Equations-Based Segmentation for Radiotherapy Treatment Planning[J]. Mathematical Biosciences and Engineering, 2005, 2(2): 209-226. doi: 10.3934/mbe.2005.2.209
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Abstract
The purpose of this study is to develop automatic algorithms for the segmentation phase of radiotherapy treatment
planning. We develop new image processing techniques that are based on solving a partial differential equation for the
evolution of the curve that identifies the segmented organ. The velocity function is based on the piecewise
Mumford-Shah functional. Our method incorporates information about the target organ into classical segmentation
algorithms. This information, which is given in terms of a three-dimensional wireframe representation of the organ,
serves as an initial guess for the segmentation algorithm. We check the performance of the new algorithm on eight data
sets of three different organs: rectum, bladder, and kidney. The results of the automatic segmentation were compared
with a manual segmentation of each data set by radiation oncology faculty and residents. The quality of the automatic
segmentation was measured with the ''$\kappa$-statistics'', and with a count of over- and undersegmented frames, and
was shown in most cases to be very close to the manual segmentation of the same data. A typical segmentation of an
organ with sixty slices takes less than ten seconds on a Pentium IV laptop.
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