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1. G. Iacobellis, D. Pistilli, M. Gucciardo, F. Leonetti, F. Miraldi, G. Brancaccio, P. Gallo, and C. R. di Gioia, “Adiponectin expression in human epicardial adipose tissue in vivo is lower in patients with coronary artery disease,” Cytokine 29, 251255 (2005).
2. D. Dey, R. Nakazato, D. Li, and D. S. Berman, “Epicardial and thoracic fat-noninvasive measurement and clinical implications,” Cardiovasc. Diagn. Ther. 2, 8593 (2012).
3. B. van den Borst, H. R. Gosker, and A. M. Schols, “Central fat and peripheral muscle: Partners in crime in chronic obstructive pulmonary disease,” Am. J. Respir. Crit. Care Med. 187, 813 (2013).
4. D. Dey, N. D. Wong, B. Tamarappoo, R. Nakazato, H. Gransar, V. Y. Cheng, A. Ramesh, I. Kakadiaris, G. Germano, P. J. Slomka, and D. S. Berman, “Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and Metabolic Syndrome,” Atherosclerosis 209, 136141 (2010).
5. K. J. Rosenquist, A. Pedley, J. M. Massaro, K. E. Therkelsen, J. M. Murabito, U. Hoffmann, and C. S. Fox, “Visceral and subcutaneous fat quality and cardiometabolic risk,” JACC Cardiovasc. Imaging 6, 762771 (2013).
6. R. Furutate, T. Ishii, R. Wakabayashi, T. Motegi, K. Yamada, A. Gemma, and K. Kida, “Excessive visceral fat accumulation in advanced chronic obstructive pulmonary disease,” Int. J. Chronic Obstruct. Pulm. Dis. 2011(6), 423430 (2011).
7. A. W. Vaes, F. M. Franssen, K. Meijer, M. W. Cuijpers, E. F. Wouters, E. P. Rutten, and M. A. Spruit, “Effects of body mass index on task-related oxygen uptake and dyspnea during activities of daily life in COPD,” PLoS One 7, e41078 (2012).
8. B. van den Borst, H. R. Gosker, A. Koster, B. Yu, S. B. Kritchevsky, Y. Liu, B. Meibohm, T. B. Rice, M. Shlipak, S. Yende, T. B. Harris, and A. M. Schols, “The influence of abdominal visceral fat on inflammatory pathways and mortality risk in obstructive lung disease,” Am. J. Clin. Nutr. 96, 516526 (2012).
9. J. Zhang, Z. He, and X. Huang, “Automatic 3D anatomy-based mediastinum segmentation method in CT images,” JDCTA 5, 266274 (2011).
10. D. Chittajallu, P. Balanca, and I. Kakadiaris, “Automatic delineation of the inner thoracic region in non-contrast CT data,” in Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, EMBC (IEEE, Minneapolis, MN, 2009), pp. 35693572.
11. X. Zhou, H. Ninomiya, T. Hara, H. Fujita, R. Yokoyama, H. Chen, T. Kiryu, and H. Hoshi, “Automated estimation of the upper surface of the diaphragm in 3-D CT images,” IEEE Trans. Biomed. Eng. 55, 351353 (2008).
12. R. Yalamanchili, D. Chittajallu, P. Balanca, B. Tamarappoo, D. Berman, D. Dey, and I. Kakadiaris, “Automatic segmentation of the diaphragm in non-contrast CT images,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro (IEEE, Rotterdam, 2010), pp. 900903.
13. R. M. Rangayyan, R. H. Vu, and G. S. Boag, “Automatic delineation of the diaphragm in computed tomographic images,” J. Digit Imaging 21, 134147 (2008).
14. K. Li, X. D. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images - A graph-theoretic approach,” IEEE Trans. Pattern Anal. 28, 119134 (2006).
15. Y. F. Zheng, A. Barbu, B. Georgescu, M. Scheuering, and D. Comaniciu, “Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features,” IEEE Trans. Med. Imaging 27, 16681681 (2008).
16. G. Funka-Lea, Y. Boykov, C. Florin, M.-P. Jolly, R. Moreau-Gobard, R. Ramaraj, and D. Rinck, “Automatic heart isolation for CT coronary visualization using graph-cuts,” in 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE, Arlington, VA, 2006), pp. 614617.
17. Y. Zheng, F. Vega-Higuera, S. K. Zhou, and D. Comaniciu, “Fast and automatic heart isolation in 3D CT volumes: Optimal shape initialization,” in Machine Learning in Medical Imaging (Springer, Beijing, China, 2010), pp. 8491.
18. M. E. Leventon, W. E. L. Grimson, and O. Faugeras, “Statistical shape influence in geodesic active contours,” in IEEE Conference Proceedings on Computer Vision and Pattern Recognition (IEEE, Hilton Head Island, SC, 2000), Vol. 1, pp. 316323.
19. A. P. Kiraly, W. E. Higgins, G. McLennan, E. A. Hoffman, and J. M. Reinhardt, “Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy,” Acad. Radiol. 9, 11531168 (2002).
20. K. Mori, J. Hasegawa, J. Toriwaki, H. Anno, and K. Katada, “Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system,” Proc. 13th Int. Conf. Pattern Recognit. 3, 528532 (1996).
21. L. Hedlund, R. Anderson, P. Goulding, J. Beck, E. Effmann, and C. Putman, “Two methods for isolating the lung area of a CT scan for density information,” Radiology 144, 353357 (1982).
22. W. A. Kalender, H. Fichte, W. Bautz, and M. Skalej, “Semiautomatic evaluation procedures for quantitative CT of the lung,” J. Comput. Assisted Tomogr. 15, 248 (1991).
23. Y. J. Lee, M. Lee, N. Kim, J. B. Seo, and J. Y. Park, “Automatic left and right lung separation using free-formed surface fitting on volumetric CT,” J. Digit Imaging (submitted).
24. Accessed 16 November 2012.
25. J. D’Errico, “Surface fitting using gridfit - File exchange - MATLAB central” (available URL: Accessed 14 October 2013.
26. D. C. Lay, Linear Algebra and its Applications (Addison-Wesley, New York, 2000).
27. J. P. Bae, N. Kim, J.-E. Kim, Y. Chang, S. M. Lee, J. B. Seo, J. Lee, and H. C. Kim, “Automatic lung segmentation for high-resolution computed tomography of patients with diffuse interstitial lung disease using a rib detection and inverse level set algorithm,” J. Digit Imaging (submitted).
28. R. Fabbri, L. D. F. Costa, J. C. Torelli, and O. M. Bruno, “2D Euclidean distance transform algorithms: A comparative survey,” ACM Comput. Surv. 40(1), 2 (2008).
29. D. Bailey, “An efficient euclidean distance transform,” Combinatorial Image Analysis, Lecture Notes in Computer Science Vol. 3322 (Springer, 2005), pp. 394408.
30. W. E. Lorensen and H. E. Cline, “Marching cubes: A high resolution 3D surface construction algorithm,” ACM Siggraph Comput. Graphics 21, 163169 (1987).
31. J. A. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (Cambridge University Press, Cambridge University, 1999).
32. R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front propagation - A level set approach,” IEEE Trans. Pattern Anal. 17, 158175 (1995).
33. R. H. Byrd, P. H. Lu, J. Nocedal, and C. Y. Zhu, “A limited memory algorithm for bound constrained optimization,” SIAM J. Sci. Comput. 16, 11901208 (1995).
34. D. M. Mount and S. Arya, “ANN: A library for approximate nearest neighbor searching” (available URL: Accessed 14 October 2013.

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To develop and validate a semiautomatic segmentation method for thoracic cavity volumetry and mediastinum fat quantification of patients with chronic obstructive pulmonary disease.

The thoracic cavity region was separated by segmenting multiorgans, namely, the rib, lung, heart, and diaphragm. To encompass various lung disease-induced variations, the inner thoracic wall and diaphragm were modeled by using a three-dimensional surface-fitting method. To improve the accuracy of the diaphragm surface model, the heart and its surrounding tissue were segmented by a two-stage level set method using a shape prior. To assess the accuracy of the proposed algorithm, the algorithm results of 50 patients were compared to the manual segmentation results of two experts with more than 5 years of experience (these manual results were confirmed by an expert thoracic radiologist). The proposed method was also compared to three state-of-the-art segmentation methods. The metrics used to evaluate segmentation accuracy were volumetric overlap ratio (VOR), false positive ratio on VOR (FPRV), false negative ratio on VOR (FNRV), average symmetric absolute surface distance (ASASD), average symmetric squared surface distance (ASSSD), and maximum symmetric surface distance (MSSD).

In terms of thoracic cavity volumetry, the mean ± SD VOR, FPRV, and FNRV of the proposed method were (98.17 ± 0.84)%, (0.49 ± 0.23)%, and (1.34 ± 0.83)%, respectively. The ASASD, ASSSD, and MSSD for the thoracic wall were 0.28 ± 0.12, 1.28 ± 0.53, and 23.91 ± 7.64 mm, respectively. The ASASD, ASSSD, and MSSD for the diaphragm surface were 1.73 ± 0.91, 3.92 ± 1.68, and 27.80 ± 10.63 mm, respectively. The proposed method performed significantly better than the other three methods in terms of VOR, ASASD, and ASSSD.

The proposed semiautomatic thoracic cavity segmentation method, which extracts multiple organs (namely, the rib, thoracic wall, diaphragm, and heart), performed with high accuracy and may be useful for clinical purposes.


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