Research Papers

Quantifying Normal Geometric Variation in Human Pulmonary Lobar Geometry From High Resolution Computed Tomography

[+] Author and Article Information
Ho-Fung Chan

Auckland Bioengineering Institute,
University of Auckland,
Auckland 1142, New Zealand
e-mail: hcha184@aucklanduni.ac.nz

Alys R. Clark

Auckland Bioengineering Institute,
University of Auckland,
Auckland 1142, New Zealand
e-mail: alys.clark@auckland.ac.nz

Eric A. Hoffman

Departments of Radiology
and Biomedical Engineering,
University of Iowa,
Iowa City, IA 52242
e-mail: eric-hoffman@uiowa.edu

Duane T. K. Malcolm

Auckland Bioengineering Institute,
University of Auckland,
Auckland 1142, New Zealand
e-mail: d.malcolm@auckland.ac.nz

Merryn H. Tawhai

Auckland Bioengineering Institute,
University of Auckland,
Private Bag 92019,
Auckland 1142, New Zealand
e-mail: m.tawhai@auckland.ac.nz

1Corresponding author.

Manuscript received August 10, 2014; final manuscript received February 16, 2015; published online March 18, 2015. Assoc. Editor: Ender A. Finol.

J Biomech Eng 137(5), 051010 (Mar 18, 2015) (7 pages) Paper No: BIO-14-1382; doi: 10.1115/1.4029919 History: Received August 10, 2014

Previous studies of the ex vivo lung have suggested significant intersubject variability in lung lobe geometry. A quantitative description of normal lung lobe shape would therefore have value in improving the discrimination between normal population variability in shape and pathology. To quantify normal human lobe shape variability, a principal component analysis (PCA) was performed on high resolution computed tomography (HRCT) imaging of the lung at full inspiration. Volumetric imaging from 22 never-smoking subjects (10 female and 12 male) with normal lung function was included in the analysis. For each subject, an initial finite element mesh geometry was generated from a group of manually selected nodes that were placed at distinct anatomical locations on the lung surface. Each mesh used cubic shape functions to describe the surface curvilinearity, and the mesh was fitted to surface data for each lobe. A PCA was performed on the surface meshes for each lobe. Nine principal components (PCs) were sufficient to capture >90% of the normal variation in each of the five lobes. The analysis shows that lobe size can explain between 20% and 50% of intersubject variability, depending on the lobe considered. Diaphragm shape was the next most significant intersubject difference. When the influence of lung size difference is removed, the angle of the fissures becomes the most significant shape difference, and the variability in relative lobe size becomes important. We also show how a lobe from an independent subject can be projected onto the study population’s PCs, demonstrating potential for abnormalities in lobar geometry to be defined in a quantitative manner.

Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.


Zhang, L. , Hoffman, E. A. , and Reinhardt, J. M. , 2006, “Atlas-Driven Lung Lobe Segmentation in Volumetric X-Ray CT Images,” IEEE Trans. Med. Imaging, 25(1), pp. 1–16. [CrossRef] [PubMed]
Aziz, A. , Ashizawa, K. , Nagaoki, K. , and Hayashi, K. , 2004, “High Resolution CT Anatomy of the Pulmonary Fissures,” J. Thorac. Imaging, 19(3), pp. 186–191. [CrossRef] [PubMed]
Prakash , Bhardwaj, A. K. , Shashirekha, M. , Suma, H. Y. , Krishna, G. G. , and Singh, G. , 2010, “Lung Morphology: A Cadaver Study in Indian Population,” Ital. J. Anat. Embryol., 115(3), pp. 235–240. [PubMed]
Bhimai, D. N. , Narasinga, R. B. , and Sunitha, V. , 2011, “Morphological Variations of Lung—A Cadaveric Study in North Coastal Andhra Pradesh,” Int. J. Biol. Med. Res., 2(4), pp. 1149–1152.
Hayashi, K. , Aziz, A. , Ashizawa, K. , Hayashi, H. , Nagaoki, K. , and Otsuji, H. , 2001, “Radiographic Appearances of the Major Fissures,” Radiographics, 21(4), pp. 861–874. [CrossRef] [PubMed]
Hoffman, E. A. , Clough, A. V. , Christensen, G. E. , Lin, C. L. , Mclennan, G. , Reinhardt, J. M. , Simon, B. A. , Sonka, M. , Tawhai, M. H. , Van Beek, E. J. , and Wang, G. , 2004, “The Comprehensive Imaging-Based Analysis of the Lung: A Forum for Team Science,” Acad. Radiol., 11(12), pp. 1370–1380. [CrossRef] [PubMed]
Li, B. , Christensen, G. E. , Hoffman, E. A. , Mclennan, G. , and Reinhardt, J. M. , 2012, “Establishing a Normative Atlas of the Human Lung: Computing the Average Transformation and Atlas Construction,” Acad. Radiol., 19(11), pp. 1368–1381. [CrossRef] [PubMed]
Cootes, T. F. , Taylor, C. J. , Cooper, C. H. , and Graham, J. , 1995, “Active Shape Models—Their Training and Application,” Comput. Vision Image Understanding, 61(1), pp. 38–59. [CrossRef]
Rajagopal, V. , Boyes, R. , Azhar, M. , Gamage, T. P. B. , Nielsen, P. M. F. , and Nash, M. P. , 2011, “Towards a Completely Automated Clinical Workflow for Breast Image Analysis Using Patient-Specific Biomechanical Models,” Proceedings of MICCAI2011 Workshop on Breast Image Analysis, pp. 121–128.
Jolliffe, I. T. , 2002, Principal Component Analysis, Springer-Verlag, New York.
Saita, S. , Kubo, M. , Kawata, Y. , Niki, N. , Ohmatsu, H. , and Moriyama, N. , 2006, “An Algorithm for the Extraction of Pulmonary Fissures From Low-Dose Multislice CT Image,” Syst. Comput. Jpn., 37(9), pp. 63–76. [CrossRef]
Zhou, X. , Hayashi, T. , Hara, T. , Fujita, H. , Yokoyama, R. , Kiryu, T. , and Hoshi, H. , 2004, “Automatic Recognition of Lung Lobes and Fissures From Multislice CT Images,” Proc. SPIE, 5370, pp. 1629–1633.
Wei, Q. , and Hu, Y. , 2014, “A Hybrid Approach to Segmentation of Diseased Lung Lobes,” IEEE J. Biomed. Health Inf., 18(5), pp. 1696–1706. [CrossRef]
Shi, Y. , Qi, F. , Xue, Z. , Chen, L. , Ito, K. , Matsuo, H. , and Shen, D. , 2008, “Segmenting Lung Fields in Serial Chest Radiographs Using Both Population-Based and Patient-Specific Shape Statistics,” IEEE Trans. Med. Imaging, 27(4), pp. 481–494. [CrossRef] [PubMed]
Li, B. , Christensen, G. E. , Hoffman, E. A. , Mclennan, G. , and Reinhardt, J. M. , 2003, “Establishing a Normative Atlas of the Human Lung: Intersubject Warping and Registration of Volumetric CT Images,” Acad. Radiol., 10(3), pp. 255–265. [CrossRef] [PubMed]
Fernandez, J. W. , Mithraratne, P. , Thrupp, S. F. , Tawhai, M. H. , and Hunter, P. J. , 2004, “Anatomically Based Geometric Modelling of the Musculo-Skeletal System and Other Organs,” Biomech. Model. Mechanobiol., 2(3), pp. 139–155. [CrossRef] [PubMed]
Cowie, H. , Lloyd, M. H. , and Soutar, C. A. , 1985, “Study of Lung Function Data by Principal Components Analysis,” Thorax, 40(6), pp. 438–443. [CrossRef] [PubMed]


Grahic Jump Location
Fig. 2

Initial LUL surface mesh nodes. (a) Manually selected nodes. (b) Generated linear Lagrange mesh structure.

Grahic Jump Location
Fig. 1

Schematic depictions of the left and right lungs with anatomical landmarks labeled. The entrance to the lungs of the arteries (Art), airways (Air), and veins (V) is labeled along with other key anatomical features. Left lung landmarks are: (1) the highest point on fissure, (2) the top of aorta impression, (3) the intersection between cardiac notch, anterior surface, and diaphragmatic surface, (4) the lowest point on fissure /base of the lower lobe, and (5) the base of aorta impression. Right lung landmarks are: (1) the apex of arch of azygos vein impression, (2) the apex of the oblique fissure, (3) the apex of esophageal groove (lower lobe), (4) the apex of the intersection between horizontal and oblique fissures, (5) the base of the esophageal groove (lower lobe), (6) the intersection between the cardiac impression, anterior surface, and diaphragmatic surface, (7) the point on horizontal fissure at anterior surface boundary, and (8) the base of venous insertion.

Grahic Jump Location
Fig. 3

Example of cubic surface mesh fitting process at the top of LUL. (a) Data plane ring with mesh nodes in their original positions after conversion from linear to cubic mesh. (b) Edge nodes pushed to the edge and data plane ring split into two parts. Intermediate node placement via data crawl function for front (c) and back (d) regions of data plane ring.

Grahic Jump Location
Fig. 4

The percentage of explained variance for each lobe versus the number of PCs for the study population PCA

Grahic Jump Location
Fig. 5

Description of shape changes to the LLL for the first three PCs

Grahic Jump Location
Fig. 6

Relationship between the weighting of the first PC of the LLL and the TLC (obtained from PFTs) of each subject in the study population



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In