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

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....

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Bibliographic Details
Published in:Journal of biomedical optics 2014-01, Vol.19 (1), p.016007-016007
Main Authors: Bredfeldt, Jeremy S, Liu, Yuming, Pehlke, Carolyn A, Conklin, Matthew W, Szulczewski, Joseph M, Inman, David R, Keely, Patricia J, Nowak, Robert D, Mackie, Thomas R, Eliceiri, Kevin W
Format: Article
Language:English
Subjects:
Algorithms
Animals
Automation
Breast
Breast Neoplasms - pathology
Cancer
Collagen - chemistry
Collagens
Disease Progression
Extracellular Matrix - metabolism
Female
Fibers
Fires
Humans
Image Processing, Computer-Assisted
Imaging
Mammary Neoplasms, Experimental - pathology
Mice
Research Papers: Imaging
Signal-To-Noise Ratio
Software
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Summary: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.
ISSN:1083-3668
1560-2281
DOI:10.1117/1.JBO.19.1.016007