Research Papers: Imaging

Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer

[+] Author Affiliations
Jeremy S. Bredfeldt, Thomas R. Mackie

University of Wisconsin at Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, Wisconsin 53706

Morgridge Institute for Research, 330 North Orchard Street, Madison, Wisconsin 53715

Yuming Liu, Carolyn A. Pehlke

University of Wisconsin at Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, Wisconsin 53706

Matthew W. Conklin, Joseph M. Szulczewski, David R. Inman, Patricia J. Keely

University of Wisconsin at Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, Wisconsin 53706

University of Wisconsin at Madison, Laboratory for Cell and Molecular Biology, 1525 Linden Drive, Madison, Wisconsin 53706

Robert D. Nowak

University of Wisconsin at Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, Wisconsin 53706

University of Wisconsin at Madison, Department of Electrical and Computer Engineering, 1415 Engineering Drive, Madison, Wisconsin 53706

Kevin W. Eliceiri

University of Wisconsin at Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, Wisconsin 53706

Morgridge Institute for Research, 330 North Orchard Street, Madison, Wisconsin 53715

University of Wisconsin at Madison, Laboratory for Cell and Molecular Biology, 1525 Linden Drive, Madison, Wisconsin 53706

J. Biomed. Opt. 19(1), 016007 (Jan 09, 2014). doi:10.1117/1.JBO.19.1.016007
History: Received March 17, 2013; Revised September 8, 2013; Accepted October 17, 2013
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Abstract.  Second-harmonic generation (SHG) imaging can help reveal interactions between collagen fibers and cancer cells. Quantitative analysis of SHG images of collagen fibers is challenged by the heterogeneity of collagen structures and low signal-to-noise ratio often found while imaging collagen in tissue. The role of collagen in breast cancer progression can be assessed post acquisition via enhanced computation. To facilitate this, we have implemented and evaluated four algorithms for extracting fiber information, such as number, length, and curvature, from a variety of SHG images of collagen in breast tissue. The image-processing algorithms included a Gaussian filter, SPIRAL-TV filter, Tubeness filter, and curvelet-denoising filter. Fibers are then extracted using an automated tracking algorithm called fiber extraction (FIRE). We evaluated the algorithm performance by comparing length, angle and position of the automatically extracted fibers with those of manually extracted fibers in twenty-five SHG images of breast cancer. We found that the curvelet-denoising filter followed by FIRE, a process we call CT-FIRE, outperforms the other algorithms under investigation. CT-FIRE was then successfully applied to track collagen fiber shape changes over time in an in vivo mouse model for breast cancer.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Jeremy S. Bredfeldt ; Yuming Liu ; Carolyn A. Pehlke ; Matthew W. Conklin ; Joseph M. Szulczewski, et al.
"Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer", J. Biomed. Opt. 19(1), 016007 (Jan 09, 2014). ; http://dx.doi.org/10.1117/1.JBO.19.1.016007


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