automated lobe-based airway labeling

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ID: 149391
2012
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Abstract
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.
Reference Key
gu2012internationalautomated Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Suicheng Gu;Zhimin Wang;Jill M. Siegfried;David Wilson;William L. Bigbee;Jiantao Pu
Journal Chemistry, an Asian journal
Year 2012
DOI
10.1155/2012/382806
URL
http://dx.doi.org/10.1155/2012/382806
Keywords
medical physics nuclear medicinemedical technology medical radiology

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