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Title: Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique

Abstract

Accurate segmentation of pulmonary nodules in computed tomography (CT) is an important and difficult task for computer-aided diagnosis of lung cancer. Therefore, the authors developed a novel automated method for accurate segmentation of nodules in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. To simplify nodule segmentation, the 3D VOI was transformed into a two-dimensional (2D) image by use of a key 'spiral-scanning' technique, in which a number of radial lines originating from the center of the VOI spirally scanned the VOI from the 'north pole' to the 'south pole'. The voxels scanned by the radial lines provided a transformed 2D image. Because the surface of a nodule in the 3D image became a curve in the transformed 2D image, the spiral-scanning technique considerably simplified the segmentation method and enabled reliable segmentation results to be obtained. A dynamic programming technique was employed to delineate the 'optimal' outline of a nodule in the 2D image, which corresponded to the surface of the nodule in the 3D image. The optimal outline was then transformed back into 3D image space to provide the surface of the nodule. An overlap between nodule regions provided bymore » computer and by the radiologists was employed as a performance metric for evaluating the segmentation method. The database included two Lung Imaging Database Consortium (LIDC) data sets that contained 23 and 86 CT scans, respectively, with 23 and 73 nodules that were 3 mm or larger in diameter. For the two data sets, six and four radiologists manually delineated the outlines of the nodules as reference standards in a performance evaluation for nodule segmentation. The segmentation method was trained on the first and was tested on the second LIDC data sets. The mean overlap values were 66% and 64% for the nodules in the first and second LIDC data sets, respectively, which represented a higher performance level than those of two existing segmentation methods that were also evaluated by use of the LIDC data sets. The segmentation method provided relatively reliable results for pulmonary nodule segmentation and would be useful for lung cancer quantification, detection, and diagnosis.« less

Authors:
; ;  [1]
  1. Department of Radiology, MC2026, University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637 (United States)
Publication Date:
OSTI Identifier:
21032875
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 34; Journal Issue: 12; Other Information: DOI: 10.1118/1.2799885; (c) 2007 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; BIOMEDICAL RADIOGRAPHY; COMPUTERIZED TOMOGRAPHY; DIAGNOSIS; DYNAMIC PROGRAMMING; IMAGE PROCESSING; IMAGES; LUNGS; NEOPLASMS

Citation Formats

Jiahui, Wang, Engelmann, Roger, and Qiang, Li. Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique. United States: N. p., 2007. Web. doi:10.1118/1.2799885.
Jiahui, Wang, Engelmann, Roger, & Qiang, Li. Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique. United States. https://doi.org/10.1118/1.2799885
Jiahui, Wang, Engelmann, Roger, and Qiang, Li. Sat . "Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique". United States. https://doi.org/10.1118/1.2799885.
@article{osti_21032875,
title = {Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique},
author = {Jiahui, Wang and Engelmann, Roger and Qiang, Li},
abstractNote = {Accurate segmentation of pulmonary nodules in computed tomography (CT) is an important and difficult task for computer-aided diagnosis of lung cancer. Therefore, the authors developed a novel automated method for accurate segmentation of nodules in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. To simplify nodule segmentation, the 3D VOI was transformed into a two-dimensional (2D) image by use of a key 'spiral-scanning' technique, in which a number of radial lines originating from the center of the VOI spirally scanned the VOI from the 'north pole' to the 'south pole'. The voxels scanned by the radial lines provided a transformed 2D image. Because the surface of a nodule in the 3D image became a curve in the transformed 2D image, the spiral-scanning technique considerably simplified the segmentation method and enabled reliable segmentation results to be obtained. A dynamic programming technique was employed to delineate the 'optimal' outline of a nodule in the 2D image, which corresponded to the surface of the nodule in the 3D image. The optimal outline was then transformed back into 3D image space to provide the surface of the nodule. An overlap between nodule regions provided by computer and by the radiologists was employed as a performance metric for evaluating the segmentation method. The database included two Lung Imaging Database Consortium (LIDC) data sets that contained 23 and 86 CT scans, respectively, with 23 and 73 nodules that were 3 mm or larger in diameter. For the two data sets, six and four radiologists manually delineated the outlines of the nodules as reference standards in a performance evaluation for nodule segmentation. The segmentation method was trained on the first and was tested on the second LIDC data sets. The mean overlap values were 66% and 64% for the nodules in the first and second LIDC data sets, respectively, which represented a higher performance level than those of two existing segmentation methods that were also evaluated by use of the LIDC data sets. The segmentation method provided relatively reliable results for pulmonary nodule segmentation and would be useful for lung cancer quantification, detection, and diagnosis.},
doi = {10.1118/1.2799885},
url = {https://www.osti.gov/biblio/21032875}, journal = {Medical Physics},
issn = {0094-2405},
number = 12,
volume = 34,
place = {United States},
year = {2007},
month = {12}
}