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Lung Extraction, Lobe Segmentation and Hierarchical Region Assessment for Quantitative Analysis on High Resolution Computed Tomography Images
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Ross J.C.1, San Jose Estepar R.2, Diaz A.4, Westin C-F.2, Kikinis R.3, Silverman E.K.5, Washko G.R.5
Institution: |
1Channing Laboratory, Brigham and Women’s Hospital, Boston, MA 2Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA 3Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. 4Pontificia Universidad Catolica de Chile, Chile 5Pulmonary and Critical Care Division, Brigham and Women’s Hospital, Boston, MA |
Publisher: |
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2009 |
Publication Date: |
Sep-2009 |
Volume Number: |
12 |
Issue Number: |
Pt 2 |
Pages: |
690-698 |
Citation: |
Int Conf Med Image Comput Comput Assist Interv. 2009;12(Pt 2):690-698. |
PubMed ID: |
20426172 |
PMCID: |
PMC3061233 |
Appears in Collections: |
LMI, SPL |
Sponsors: |
NIH U01 HL089897 NIH U01 HL089856 NIH K23 HL089353 |
Generated Citation: |
Ross J.C., San Jose Estepar R., Diaz A., Westin C-F., Kikinis R., Silverman E.K., Washko G.R. Lung Extraction, Lobe Segmentation and Hierarchical Region Assessment for Quantitative Analysis on High Resolution Computed Tomography Images. Int Conf Med Image Comput Comput Assist Interv. 2009;12(Pt 2):690-698. PMID: 20426172. PMCID: PMC3061233. |
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Regional assessment of lung disease (such as chronic obstructive pul- monary disease) is a critical component to accurate patient diagnosis. Software tools than enable such analysis are also important for clinical research studies. In this work, we present an image segmentation and data representation frame- work that enables quantitative analysis specific to different lung regions on high resolution computed tomography (HRCT) datasets. We present an offline, fully automatic image processing chain that generates airway, vessel, and lung mask segmentations in which the left and right lung are delineated. We describe a novel lung lobe segmentation tool that produces reproducible results with minimal user interaction. A usability study performed across twenty datasets (inspiratory and expiratory exams including a range of disease states) demonstrates the tool’s abil- ity to generate results within five to seven minutes on average. We also describe a data representation scheme that involves compact encoding of label maps such that both “regions” (such as lung lobes) and “types” (such as emphysematous parenchyma ) can be simultaneously represented at a given location in the HRCT.
Additional Material
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Ross-MICCAI2009-fig4.jpg (252.077kB)