Automated Lobe-Based Airway Labeling
Regional quantitative analysis of airway morphological abnormalities is of great interest in lung disease investigation. Considering that pulmonary lobes are relatively independent functional unit, we develop and test a novel and efficient computerized scheme in this study to automatically and robus...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2012/382806 |
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| _version_ | 1850170996413169664 |
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| author | Suicheng Gu Zhimin Wang Jill M. Siegfried David Wilson William L. Bigbee Jiantao Pu |
| author_facet | Suicheng Gu Zhimin Wang Jill M. Siegfried David Wilson William L. Bigbee Jiantao Pu |
| author_sort | Suicheng Gu |
| collection | DOAJ |
| description | Regional quantitative analysis of airway morphological abnormalities is of great interest in lung disease investigation. Considering that pulmonary lobes are relatively independent functional unit, we develop and test a novel and efficient computerized scheme in this study to automatically and robustly classify the airways into different categories in terms of pulmonary lobe. Given an airway tree, which could be obtained using any available airway segmentation scheme, the developed approach consists of four basic steps: (1) airway skeletonization or centerline extraction, (2) individual airway branch identification, (3) initial rule-based airway classification/labeling, and (4) self-correction of labeling errors. In order to assess the performance of this approach, we applied it to a dataset consisting of 300 chest CT examinations in a batch manner and asked an image analyst to subjectively examine the labeled results. Our preliminary experiment showed that the labeling accuracy for the right upper lobe, the right middle lobe, the right lower lobe, the left upper lobe, and the left lower lobe is 100%, 99.3%, 99.3%, 100%, and 100%, respectively. Among these, only two cases are incorrectly labeled due to the failures in airway detection. It takes around 2 minutes to label an airway tree using this algorithm. |
| format | Article |
| id | doaj-art-a442e2d3fdbf42ff9dd773615c450e20 |
| institution | OA Journals |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-a442e2d3fdbf42ff9dd773615c450e202025-08-20T02:20:22ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962012-01-01201210.1155/2012/382806382806Automated Lobe-Based Airway LabelingSuicheng Gu0Zhimin Wang1Jill M. Siegfried2David Wilson3William L. Bigbee4Jiantao Pu5Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USADepartment of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USADepartment of of Pharmacology & Chemical Biology, University of Pittsburgh, Pittsburgh, PA 15213, USADepartment of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USADepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15213, USADepartment of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USARegional quantitative analysis of airway morphological abnormalities is of great interest in lung disease investigation. Considering that pulmonary lobes are relatively independent functional unit, we develop and test a novel and efficient computerized scheme in this study to automatically and robustly classify the airways into different categories in terms of pulmonary lobe. Given an airway tree, which could be obtained using any available airway segmentation scheme, the developed approach consists of four basic steps: (1) airway skeletonization or centerline extraction, (2) individual airway branch identification, (3) initial rule-based airway classification/labeling, and (4) self-correction of labeling errors. In order to assess the performance of this approach, we applied it to a dataset consisting of 300 chest CT examinations in a batch manner and asked an image analyst to subjectively examine the labeled results. Our preliminary experiment showed that the labeling accuracy for the right upper lobe, the right middle lobe, the right lower lobe, the left upper lobe, and the left lower lobe is 100%, 99.3%, 99.3%, 100%, and 100%, respectively. Among these, only two cases are incorrectly labeled due to the failures in airway detection. It takes around 2 minutes to label an airway tree using this algorithm.http://dx.doi.org/10.1155/2012/382806 |
| spellingShingle | Suicheng Gu Zhimin Wang Jill M. Siegfried David Wilson William L. Bigbee Jiantao Pu Automated Lobe-Based Airway Labeling International Journal of Biomedical Imaging |
| title | Automated Lobe-Based Airway Labeling |
| title_full | Automated Lobe-Based Airway Labeling |
| title_fullStr | Automated Lobe-Based Airway Labeling |
| title_full_unstemmed | Automated Lobe-Based Airway Labeling |
| title_short | Automated Lobe-Based Airway Labeling |
| title_sort | automated lobe based airway labeling |
| url | http://dx.doi.org/10.1155/2012/382806 |
| work_keys_str_mv | AT suichenggu automatedlobebasedairwaylabeling AT zhiminwang automatedlobebasedairwaylabeling AT jillmsiegfried automatedlobebasedairwaylabeling AT davidwilson automatedlobebasedairwaylabeling AT williamlbigbee automatedlobebasedairwaylabeling AT jiantaopu automatedlobebasedairwaylabeling |