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Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT
Target definition is the largest source of geometric uncertainty in radiation therapy. This is partly due to a lack of contrast between tumor and healthy soft tissue for computed tomography (CT) and due to blurriness, lower spatial resolution, and lack of a truly quantitative unit for positron emiss...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3600349/ https://www.ncbi.nlm.nih.gov/pubmed/23533750 http://dx.doi.org/10.1155/2013/980769 |
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author | Markel, Daniel Caldwell, Curtis Alasti, Hamideh Soliman, Hany Ung, Yee Lee, Justin Sun, Alexander |
author_facet | Markel, Daniel Caldwell, Curtis Alasti, Hamideh Soliman, Hany Ung, Yee Lee, Justin Sun, Alexander |
author_sort | Markel, Daniel |
collection | PubMed |
description | Target definition is the largest source of geometric uncertainty in radiation therapy. This is partly due to a lack of contrast between tumor and healthy soft tissue for computed tomography (CT) and due to blurriness, lower spatial resolution, and lack of a truly quantitative unit for positron emission tomography (PET). First-, second-, and higher-order statistics, Tamura, and structural features were characterized for PET and CT images of lung carcinoma and organs of the thorax. A combined decision tree (DT) with K-nearest neighbours (KNN) classifiers as nodes containing combinations of 3 features were trained and used for segmentation of the gross tumor volume. This approach was validated for 31 patients from two separate institutions and scanners. The results were compared with thresholding approaches, the fuzzy clustering method, the 3-level fuzzy locally adaptive Bayesian algorithm, the multivalued level set algorithm, and a single KNN using Hounsfield units and standard uptake value. The results showed the DTKNN classifier had the highest sensitivity of 73.9%, second highest average Dice coefficient of 0.607, and a specificity of 99.2% for classifying voxels when using a probabilistic ground truth provided by simultaneous truth and performance level estimation using contours drawn by 3 trained physicians. |
format | Online Article Text |
id | pubmed-3600349 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-36003492013-03-26 Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT Markel, Daniel Caldwell, Curtis Alasti, Hamideh Soliman, Hany Ung, Yee Lee, Justin Sun, Alexander Int J Mol Imaging Clinical Study Target definition is the largest source of geometric uncertainty in radiation therapy. This is partly due to a lack of contrast between tumor and healthy soft tissue for computed tomography (CT) and due to blurriness, lower spatial resolution, and lack of a truly quantitative unit for positron emission tomography (PET). First-, second-, and higher-order statistics, Tamura, and structural features were characterized for PET and CT images of lung carcinoma and organs of the thorax. A combined decision tree (DT) with K-nearest neighbours (KNN) classifiers as nodes containing combinations of 3 features were trained and used for segmentation of the gross tumor volume. This approach was validated for 31 patients from two separate institutions and scanners. The results were compared with thresholding approaches, the fuzzy clustering method, the 3-level fuzzy locally adaptive Bayesian algorithm, the multivalued level set algorithm, and a single KNN using Hounsfield units and standard uptake value. The results showed the DTKNN classifier had the highest sensitivity of 73.9%, second highest average Dice coefficient of 0.607, and a specificity of 99.2% for classifying voxels when using a probabilistic ground truth provided by simultaneous truth and performance level estimation using contours drawn by 3 trained physicians. Hindawi Publishing Corporation 2013 2013-02-26 /pmc/articles/PMC3600349/ /pubmed/23533750 http://dx.doi.org/10.1155/2013/980769 Text en Copyright © 2013 Daniel Markel et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Clinical Study Markel, Daniel Caldwell, Curtis Alasti, Hamideh Soliman, Hany Ung, Yee Lee, Justin Sun, Alexander Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT |
title | Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT |
title_full | Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT |
title_fullStr | Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT |
title_full_unstemmed | Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT |
title_short | Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT |
title_sort | automatic segmentation of lung carcinoma using 3d texture features in 18-fdg pet/ct |
topic | Clinical Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3600349/ https://www.ncbi.nlm.nih.gov/pubmed/23533750 http://dx.doi.org/10.1155/2013/980769 |
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