Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate

An algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed tomography scans. The pleural surface was estimated and used to segment the nodule from the chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural...

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Main Authors: Artit C. Jirapatnakul, Yury D. Mulman, Anthony P. Reeves, David F. Yankelevitz, Claudia I. Henschke
Format: Article
Language:English
Published: Hindawi Limited 2011-01-01
Series:International Journal of Biomedical Imaging
Online Access:http://dx.doi.org/10.1155/2011/632195
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spelling doaj-14b442bfcfad403795dda8152984b3d62020-11-24T23:16:30ZengHindawi LimitedInternational Journal of Biomedical Imaging1687-41881687-41962011-01-01201110.1155/2011/632195632195Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface EstimateArtit C. Jirapatnakul0Yury D. Mulman1Anthony P. Reeves2David F. Yankelevitz3Claudia I. Henschke4School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USASchool of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USASchool of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USADepartment of Radiology, Mount Sinai School of Medicine, 1 Gustave L. Levy Place, New York, NY 10029, USADepartment of Radiology, Mount Sinai School of Medicine, 1 Gustave L. Levy Place, New York, NY 10029, USAAn algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed tomography scans. The pleural surface was estimated and used to segment the nodule from the chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural surface but not on the nodule. A 3D surface was estimated from the identified surface points. The segmentation performance of the algorithm was evaluated on a database of 150 solid juxtapleural pulmonary nodules. Segmented images were rated on a scale of 1 to 4 based on visual inspection, with 3 and 4 considered acceptable. This algorithm offers a large improvement in the success rate of juxtapleural nodule segmentation, successfully segmenting 98.0% of nodules compared to 81.3% for a previously published plane-fitting algorithm, which will provide for the development of more robust automated nodule measurement methods.http://dx.doi.org/10.1155/2011/632195
collection DOAJ
language English
format Article
sources DOAJ
author Artit C. Jirapatnakul
Yury D. Mulman
Anthony P. Reeves
David F. Yankelevitz
Claudia I. Henschke
spellingShingle Artit C. Jirapatnakul
Yury D. Mulman
Anthony P. Reeves
David F. Yankelevitz
Claudia I. Henschke
Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate
International Journal of Biomedical Imaging
author_facet Artit C. Jirapatnakul
Yury D. Mulman
Anthony P. Reeves
David F. Yankelevitz
Claudia I. Henschke
author_sort Artit C. Jirapatnakul
title Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate
title_short Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate
title_full Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate
title_fullStr Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate
title_full_unstemmed Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate
title_sort segmentation of juxtapleural pulmonary nodules using a robust surface estimate
publisher Hindawi Limited
series International Journal of Biomedical Imaging
issn 1687-4188
1687-4196
publishDate 2011-01-01
description An algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed tomography scans. The pleural surface was estimated and used to segment the nodule from the chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural surface but not on the nodule. A 3D surface was estimated from the identified surface points. The segmentation performance of the algorithm was evaluated on a database of 150 solid juxtapleural pulmonary nodules. Segmented images were rated on a scale of 1 to 4 based on visual inspection, with 3 and 4 considered acceptable. This algorithm offers a large improvement in the success rate of juxtapleural nodule segmentation, successfully segmenting 98.0% of nodules compared to 81.3% for a previously published plane-fitting algorithm, which will provide for the development of more robust automated nodule measurement methods.
url http://dx.doi.org/10.1155/2011/632195
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