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Automatic Identification and Validation of Planar Collagen Organization in the Aorta Wall with Application to Abdominal Aortic Aneurysm

Published online by Cambridge University Press:  09 September 2013

Stanislav Polzer ,
T. Christian Gasser ,
Caroline Forsell ,
Hana Druckmüllerova ,
Michal Tichy ,
Robert Staffa ,
Robert Vlachovsky  and
Jiri Bursa
Stanislav Polzer*
Affiliation:
Institute of Solid Mechanics, Mechatronics and Biomechanics, Brno University of Technology, Czech Republic
T. Christian Gasser
Affiliation:
Department of Solid Mechanics, Royal Institute of Technology, Stockholm, Sweden
Caroline Forsell
Affiliation:
Department of Solid Mechanics, Royal Institute of Technology, Stockholm, Sweden
Hana Druckmüllerova
Affiliation:
Institute of Mathematics, Faculty of Mechanical Engineering, Brno University of Technology, Czech Republic
Michal Tichy
Affiliation:
1st Department of Pathology and Anatomy, St. Anne's University Hospital, Brno, Czech Republic
Robert Staffa
Affiliation:
2nd Department of Surgery, St. Anne's University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic
Robert Vlachovsky
Affiliation:
2nd Department of Surgery, St. Anne's University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic
Jiri Bursa
Affiliation:
Institute of Solid Mechanics, Mechatronics and Biomechanics, Brno University of Technology, Czech Republic
*
*Corresponding author. E-mail: [email protected]
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Abstract

Arterial physiology relies on a delicate three-dimensional (3D) organization of cells and extracellular matrix, which is remarkably altered by vascular diseases like abdominal aortic aneurysms (AAA). The ability to explore the micro-histology of the aorta wall is important in the study of vascular pathologies and in the development of vascular constitutive models, i.e., mathematical descriptions of biomechanical properties of the wall. The present study reports and validates a fast image processing sequence capable of quantifying collagen fiber organization from histological stains. Powering and re-normalizing the histogram of the classical fast Fourier transformation (FFT) is a key step in the proposed analysis sequence. This modification introduces a powering parameter w, which was calibrated to best fit the reference data obtained using classical FFT and polarized light microscopy (PLM) of stained histological slices of AAA wall samples. The values of w = 3 and 7 give the best correlation (Pearson's correlation coefficient larger than 0.7, R2 about 0.7) with the classical FFT approach and PLM measurements. A fast and operator independent method to identify collagen organization in the arterial wall was developed and validated. This overcomes severe limitations of currently applied methods like PLM to identify collagen organization in the arterial wall.

Keywords

fast Fourier transformcollagen organizationcollagen dispersionpolarized light microscopy
Type
Biomedical and Biological Applications
Information
Microscopy and Microanalysis , Volume 19 , Issue 6 , December 2013 , pp. 1395 - 1404
Copyright
Copyright © Microscopy Society of America 2013 

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References

Ayres, Ch.E., Bwlin, G.L., Henderson, S.C., Taylor, L., Shultz, J., Alexander, J., Telemeco, T.A. & Simpson, D.G. (2006). Modulation of anisotropy in electrospun tisuue-engineering scaffolds: Analysis of fiber alignment by the fast Fourier transform. Biomaterials 27, 5524–5534.Google Scholar
Ayres, Ch.E., Jha, B.S., Meredith, H., Bowman, J.R., Bowlin, G.L., Henderson, S.C. & Simpson, D.G. (2008). Measuring fiber alignment in electrospun scaffolds: A user's guide to the 2D fast Fourier transform approach. J Biomater Sci Polymer Edn 19, 603–621.Google Scholar
Bashey, R.I., Cox, R., McCann, J. & Jimenez, S.A. (1989). Changes in collagen biosynthesis, types, and mechanics of aorta in hypertensive rats. J Lab Clin Med 113, 604–611.Google Scholar
Canham, P.B., Finlay, H.M., Dixon, J.G., Boughner, D.R. & Chen, A. (1989). Measurements from light and polarised light microscopy of human coronary arteries fixed at distending pressure. Cardiovasc Res 23, 973–982.Google Scholar
Canham, P.B., Finlay, H.M., Dixon, J.G. & Ferguson, S.E. (1991). Layered collagen fabric of cerebral aneurysms quantitatively assessed by the universal stage and polarized light microscopy. Anat Rec 231, 549–592.Google Scholar
Carmo, M., Colombo, L., Bruno, A., Corsi, F.R., Roncoroni, L., Cuttin, M.S., Radice, F., Mussini, E. & Settembrini, P.G. (2002). Alteration of elastin, collagen and their cross-links in abdominal aortic aneurysms. Eur J Vasc Endovasc Surg 23, 543–549.CrossRefGoogle ScholarPubMed
D'Amore, A., Stella, J.A., Wagner, W.R. & Sacks, M.S. (2010). Characterization of the complete fiber network topology of planar fibrous tissues and scaffolds. Biomater 31, 5345–5354.Google Scholar
Elbischer, P.J., Bischof, H., Regitnig, P. & Holzapfel, G.A. (2004). Automated analysis of collagen fiber orientation in the outermost layer of human arteries. Pattern Ana Applic 7, 269–284.Google Scholar
Finlay, H.M., McCullough, L. & Canham, P.B. (1995). Three dimensional collagen organisation of human brain arteries at different transmural pressures. J Vasc Res 32, 301–312.Google Scholar
Frisch, K.E., Duenwald-Kuehl, S.E., Kbayashi, H., Chamberlain, C.S., Lakes, R.S. & Vanderby, R. (2012). Quantification of collagen organization using fractal dimensions and Fourier transforms. Acta Histochem 144, 140–144.Google Scholar
Gasser, T.C., Gallinetti, S., Xing, X., Forsell, C., Swedenborg, J. & Roy, J. (2012). Spatial orientation of collagen fibers in the abdominal aortic aneurysms wall and its relation to wall mechanics. Acta Biomater 8, 3091–3103.Google Scholar
Gasser, T.C., Ogden, R.W. & Holzapfel, G.A. (2006). Hyperelastic modelling of arterial layers with distributed collagen fiber orientations. J R Soc Interface 3, 15–35.CrossRefGoogle Scholar
Jinqueria, L.C., Bignolas, G. & Brentani, R.R. (1979). Picrosirius staining plus polarization microscopy a specific method for collagen detection in tissue sections. Histochem J 11, 447–455.Google Scholar
Kim, K., Kim, S., Minxha, J. & Palmore, G.T.R. (2011). A novel method for analyzing images of live nerve cells. J Neuros Meth 201, 98–105.Google Scholar
Länne, T., Sonesson, B., Bergqvist, D., Bengtsson, H. & Gustafsson, D. (1992). Diameter and compliance in the male human abdominal aorta: Influence of age and aortic aneurysm. Eur J Vasc Surg 6, 178–184.CrossRefGoogle ScholarPubMed
Lee, A.A., Graham, D.A., Dela Cruz, S., Ratcliffe, A. & Karlon, W.J. (2002). Fluid shear stress-induced alignment of cultured vascular smooth muscle cells. J Biomech Eng 124, 37–43.Google Scholar
López-Candales, A., Holmes, D.R., Liao, S., Scott, M.J., Wickline, S.A. & Thompson, R.W. (1997). Decreased vascular smooth muscle cell density in medial degeneration of human abdominal aortic aneurysms. Am J Pathol 150, 993–1007.Google Scholar
Marquez, P. (2006). Fourier analysis and automated measurement of cell and fiber angular orientation distributions. Int J Solids Struc 43, 6413–6423.Google Scholar
Pratt, W.K. (2007). Digital Image Processing: PIKS Scientific Inside. Hoboken, NJ: John Wiley & Sons Inc. Google Scholar
Reuze, P., Coatrieux, J., Luo, L. & Dillenseger, J. (1993). A 3d moment based approach for blood vessel detection and quantification in MRA. Technol Health Care 1, 181–188.Google Scholar
Rizzo, R.J., McCarthy, W.J., Dixit, S.N., Lilly, M.P., Shively, V.P., Flinn, W.R. & Yao, J.S.T. (1989). Collagen types and matrix protein content in human abdominal aortic aneurysms. J Vasc Surg 10, 365–373.CrossRefGoogle ScholarPubMed
Sacks, M.S., Smith, D.B. & Hiester, E.D. (1997). A small angle light scattering device for planar connective tissue microsctructural analysis. Ann Biomed Eng 25, 678–689.Google Scholar
Sander, E.A. & Barocas, V.H. (2009). Comparison of 2d fiber network orientation measurement methods. J Biomed Mater Res Part A 88A, 322–331.Google Scholar
Schriefl, A.J., Reinisch, A.J., Sankaran, S., Pierce, D.M. & Holzapfel, G.A. (2012a). Quantitative assessment of collagen fiber orientations from 2D images of soft biological tissues. J R Soc Interface 9, 3081–3093.Google Scholar
Schriefl, A.J., Wolinski, H., Regitnig, P., Kohlwin, S.D. & Holzapfel, G.A. (2013). An automated approach for three-dimensional quantification of fibrillar structures in optically cleared soft biological tissues. J R Soc Interface 10 (In Press).Google Scholar
Schriefl, A.J., Zeindlinger, G., Pierce, D.M., Regitnig, P. & Holzapfel, G.A. (2012b). Determination of the layer-specific distributed collagen fiber orientations in human thoracic and abdominal aortas and common iliac arteries. J R Soc Interface 9, 1275–1286.Google Scholar
Smith, H.F., Canham, P.B. & Strake, J. (1981). Orientation of collagen in the tunica adventitia of the human cerebral artery measured with polarized light and the universal stage. J Ultrastructure Res 77, 133–145.Google Scholar
Starha, P., Druckmüllerova, H. & Tremlova, B. (2011). Numerical analysis of color hue component in digital images. In MENDEL 2011: 17th International Conference of Soft Computing, Matousek, R. (Ed.), pp. 497–503, Brno, Czech Republic, June 15–17, 2011. Google Scholar
Timmins, L.H., Wu, Q., Yer, A.T., Moore, J.E. & Greenwald, S.E. (2010). Structural inhomogeneity and fiber orientation in the inner arterial media. Am J Physiol Heart Circ Physiol 298, 1537–1545.Google Scholar
Tower, T.T., Neidert, M.R. & Tranquillo, R.T. (2002). Fiber alignment imaging during mechanical testing of soft tissues. Ann Biomed Eng 30, 1221–1233.Google Scholar
Xia, Y. & Elder, K. (2001). Quantification of the graphical details of collagen fibrils in transmission electron micrographs. J Microsc 204, 2–16.Google Scholar
Xu, F., Beyazoglu, T., Hefner, E., Gurkan, U.A. & Demirci, U. (2011). Automated and adaptable quantification of cellular alignment from microscopic images for tissue engineering applications. Tis Eng Part C 17, 641–648.Google Scholar