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27 April 2016 Dispersion analysis of collagen fiber networks in cervical tissue using optical coherence tomography (Conference Presentation)
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Abstract
Understanding the human cervical collagen fiber network is critical to delineating the physiology of cervical remodeling during pregnancy. Previously, we presented our methodology to study the ultrastructure of collagen fibers over an entire field of transverse slices of human cervix tissue using optical coherence tomography. Here, we present a pixel-wise fiber orientation method to enable dispersion analysis on entire slices of human cervical tissues. We obtained en face images that were parallel to the surface. In each en face image, we masked the collagen fiber region based on signal noise ratio. Then, we extracted fiber orientations in each pixel using a weighted summation scheme and generated a pixel-wise directionality map within the entire region. The weight was determined by intensity variations between a pixel of interest and its neighboring pixels and their corresponding distances. We divided the directionality map into regions of 400 μm × 400 μm along radial direction in all four quadrants. In each region, we fit von-Mises distribution to fiber orientations of pixels with mode θ and dispersion b. We compared dispersions among regions and samples. Using IRB approved protocols, we obtained whole transverse slices of cervical tissue from pregnant (n = 2) and non-pregnant (n = 13) women. We observed higher dispersion in pregnant samples compared to non-pregnant samples and higher dispersions in patient’s right/left zones than posterior/anterior zones within an axial slice. Future studies will analyze how collagen fiber dispersion patterns change from the internal to the external os.
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Yu Gan, Wang Yao, Kristin M. Myers, Joy Y. Vink, Ronald J. Wapner, and Christine P. Hendon "Dispersion analysis of collagen fiber networks in cervical tissue using optical coherence tomography (Conference Presentation)", Proc. SPIE 9689, Photonic Therapeutics and Diagnostics XII, 96893X (27 April 2016); https://doi.org/10.1117/12.2212747
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