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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Series: | International Journal of Biomedical Imaging |
Online Access: | http://dx.doi.org/10.1155/2011/632195 |
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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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