2009
DOI: 10.1088/0031-9155/54/22/017
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Airway segmentation and analysis for the study of mouse models of lung disease using micro-CT

Abstract: Animal models of lung disease are gaining importance in understanding the underlying mechanisms of diseases such as emphysema and lung cancer. Micro-CT allows in vivo imaging of these models, thus permitting the study of the progression of the disease or the effect of therapeutic drugs in longitudinal studies. Automated analysis of micro-CT images can be helpful to understand the physiology of diseased lungs, especially when combined with measurements of respiratory system input impedance. In this work, we pre… Show more

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Cited by 36 publications
(31 citation statements)
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“…The lungs were automatically delineated in the 3D micro-CT images using a previously published segmentation algorithm [13] and the airways were removed from the lung delineation [14]. Mean lung voxel intensity (MLVI) and relative volume below −900 HU (RVB -900 HU) were computed.…”
Section: Breath-hold Gated Micro-ct Imaging and Image Analysismentioning
confidence: 99%
“…The lungs were automatically delineated in the 3D micro-CT images using a previously published segmentation algorithm [13] and the airways were removed from the lung delineation [14]. Mean lung voxel intensity (MLVI) and relative volume below −900 HU (RVB -900 HU) were computed.…”
Section: Breath-hold Gated Micro-ct Imaging and Image Analysismentioning
confidence: 99%
“…Finally, the airways were segmented and the radii of the right (RRB) and left (RLB) mainstem bronchi were measured, using a method previously developed by us [25].…”
Section: Image Analysismentioning
confidence: 99%
“…Micro-CT imaging has been used to study the structure and disorder in bones [9][10][11][12][13][14], microvasculature anatomy [15,16], oncology models [17][18][19], drug or radiation delivery [20][21][22] and pulmonary diseases such as asthma and emphysema [23][24][25][26][27] in small animals. Dynamic micro-CT examinations were not possible in general in the past.…”
Section: Introductionmentioning
confidence: 99%