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Proceedings Paper

3D intrathoracic region definition and its application to PET-CT analysis
Author(s): Ronnarit Cheirsilp; Rebecca Bascom; Thomas W. Allen; William E. Higgins
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Paper Abstract

Recently developed integrated PET-CT scanners give co-registered multimodal data sets that offer complementary three-dimensional (3D) digital images of the chest. PET (positron emission tomography) imaging gives highly specific functional information of suspect cancer sites, while CT (X-ray computed tomography) gives associated anatomical detail. Because the 3D CT and PET scans generally span the body from the eyes to the knees, accurate definition of the intrathoracic region is vital for focusing attention to the central-chest region. In this way, diagnostically important regions of interest (ROIs), such as central-chest lymph nodes and cancer nodules, can be more efficiently isolated. We propose a method for automatic segmentation of the intrathoracic region from a given co-registered 3D PET-CT study. Using the 3D CT scan as input, the method begins by finding an initial intrathoracic region boundary for a given 2D CT section. Next, active contour analysis, driven by a cost function depending on local image gradient, gradient-direction, and contour shape features, iteratively estimates the contours spanning the intrathoracic region on neighboring 2D CT sections. This process continues until the complete region is defined. We next present an interactive system that employs the segmentation method for focused 3D PET-CT chest image analysis. A validation study over a series of PET-CT studies reveals that the segmentation method gives a Dice index accuracy of less than 98%. In addition, further results demonstrate the utility of the method for focused 3D PET-CT chest image analysis, ROI definition, and visualization.

Paper Details

Date Published: 18 March 2014
PDF: 11 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352J (18 March 2014); doi: 10.1117/12.2037076
Show Author Affiliations
Ronnarit Cheirsilp, The Pennsylvania State Univ. (United States)
Rebecca Bascom, The Pennsylvania State Univ. (United States)
Thomas W. Allen, The Pennsylvania State Univ. (United States)
William E. Higgins, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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