Share Email Print
cover

Proceedings Paper

Quantification of esophageal wall thickness in CT using atlas-based segmentation technique
Author(s): Jiahui Wang; Min Kyu Kang; Seth Kligerman; Wei Lu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Esophageal wall thickness is an important predictor of esophageal cancer response to therapy. In this study, we developed a computerized pipeline for quantification of esophageal wall thickness using computerized tomography (CT). We first segmented the esophagus using a multi-atlas-based segmentation scheme. The esophagus in each atlas CT was manually segmented to create a label map. Using image registration, all of the atlases were aligned to the imaging space of the target CT. The deformation field from the registration was applied to the label maps to warp them to the target space. A weighted majority-voting label fusion was employed to create the segmentation of esophagus. Finally, we excluded the lumen from the esophagus using a threshold of -600 HU and measured the esophageal wall thickness. The developed method was tested on a dataset of 30 CT scans, including 15 esophageal cancer patients and 15 normal controls. The mean Dice similarity coefficient (DSC) and mean absolute distance (MAD) between the segmented esophagus and the reference standard were employed to evaluate the segmentation results. Our method achieved a mean Dice coefficient of 65.55 ± 10.48% and mean MAD of 1.40 ± 1.31 mm for all the cases. The mean esophageal wall thickness of cancer patients and normal controls was 6.35 ± 1.19 mm and 6.03 ± 0.51 mm, respectively. We conclude that the proposed method can perform quantitative analysis of esophageal wall thickness and would be useful for tumor detection and tumor response evaluation of esophageal cancer.

Paper Details

Date Published: 17 March 2015
PDF: 6 pages
Proc. SPIE 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, 941708 (17 March 2015); doi: 10.1117/12.2081533
Show Author Affiliations
Jiahui Wang, Univ. of Maryland School of Medicine (United States)
Min Kyu Kang, Univ. of Maryland School of Medicine (United States)
Yeungnam Univ. College of Medicine (Korea, Republic of)
Seth Kligerman, Univ. of Maryland School of Medicine (United States)
Wei Lu, Univ. of Maryland School of Medicine (United States)


Published in SPIE Proceedings Vol. 9417:
Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Robert C. Molthen, Editor(s)

© SPIE. Terms of Use
Back to Top