Abstract

Automatic quantification and visualization of 3-D collagen fiber architecture using Optical Coherence Tomography (OCT) has previously relied on polarization information and/or prior knowledge of tissue-specific fiber architecture. This study explores image processing, enhancement, segmentation, and detection algorithms to map 3-D collagen fiber architecture from OCT images alone. 3-D fiber mapping, histogram analysis, and 3-D tractography revealed fiber groupings and macro-organization previously unseen in uterine tissue samples. We applied our method on centimeter-scale mosaic OCT volumes of uterine tissue blocks from pregnant and non-pregnant specimens revealing a complex, patient-specific network of fibrous collagen and myocyte bundles.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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    [Crossref]

2020 (2)

G. Yao and D. Duan, “High-resolution 3D tractography of fibrous tissue based on polarization-sensitive optical coherence tomography,” Exp. Biol. Med. 245(4), 273–281 (2020).
[Crossref]

C. Vinegoni, P. Fumene Feruglio, G. Courties, S. Schmidt, M. Hulsmans, S. Lee, R. Wang, D. Sosnovik, M. Nahrendorf, and R. Weissleder, “Fluorescence microscopy tensor imaging representations for large-scale dataset analysis,” Sci. Rep. 10(1), 5632 (2020).
[Crossref]

2019 (2)

2018 (5)

J. A. Germann, E. Martinez-Enriquez, and S. Marcos, “Quantization of collagen organization in the stroma with a new order coefficient,” Biomed. Opt. Express 9(1), 173–189 (2018).
[Crossref]

H. Yan, O. Carmichael, D. Paul, and J. Peng, “Estimating fiber orientation distribution from diffusion MRI with spherical needlets,” Med. Image Anal. 46, 57–72 (2018).
[Crossref]

J. Hao, W. Yao, W. B. R. Harris, J. Y. Vink, K. M. Myers, and E. Donnelly, “Characterization of the collagen microstructural organization of human cervical tissue,” Reproduction 156(1), 71–79 (2018).
[Crossref]

C. J. Stender, E. Rust, P. T. Martin, E. E. Neumann, R. J. Brown, and T. J. Lujan, “Modeling the effect of collagen fibril alignment on ligament mechanical behavior,” Biomech. Model. Mechanobiol. 17(2), 543–557 (2018).
[Crossref]

T. H. Lye, K. P. Vincent, A. D. McCulloch, and C. P. Hendon, “Tissue-Specific Optical Mapping Models of Swine Atria Informed by Optical Coherence Tomography,” Biophys. J. 114(6), 1477–1489 (2018).
[Crossref]

2017 (4)

D. Qu, P. J. Chuang, S. Prateepchinda, P. S. Balasubramanian, X. Yao, S. B. Doty, C. P. Hendon, and H. H. Lu, “Micro- and Ultrastructural Characterization of Age-Related Changes at the Anterior Cruciate Ligament-to-Bone Insertion,” ACS Biomater. Sci. Eng. 3(11), 2806–2814 (2017).
[Crossref]

Z. Liu, D. Pouli, D. Sood, A. Sundarakrishnan, C. K. Hui Mingalone, L. M. Arendt, C. Alonzo, K. P. Quinn, C. Kuperwasser, L. Zeng, T. Schnelldorfer, D. L. Kaplan, and I. Georgakoudi, “Automated quantification of three-dimensional organization of fiber-like structures in biological tissues,” Biomaterials 116, 34–47 (2017).
[Crossref]

K. M. Myers and D. Elad, “Biomechanics of the human uterus,” Wiley Interdiscip. Rev.: Syst. Biol. Med. 9(5), e1388 (2017).
[Crossref]

E. J. Lutton, W. J. E. P. Lammers, S. James, H. A. van den Berg, and A. M. Blanks, “A computational method for three-dimensional reconstruction of the microarchitecture of myometrial smooth muscle from histological sections,” PLoS One 12(3), e0173404 (2017).
[Crossref]

2016 (2)

W. Yao, Y. Gan, K. M. Myers, J. Y. Vink, R. J. Wapner, and C. P. Hendon, “Collagen fiber orientation and dispersion in the upper cervix of non-pregnant and pregnant women,” PLoS One 11(11), e0166709 (2016).
[Crossref]

R. Zareian, M. E. Susilo, J. A. Paten, J. P. McLean, J. Hollmann, D. Karamichos, C. S. Messer, D. T. Tambe, N. Saeidi, J. D. Zieske, and J. W. Ruberti, “Human Corneal Fibroblast Pattern Evolution and Matrix Synthesis on Mechanically Biased Substrates,” Tissue Eng., Part A 22(19-20), 1204–1217 (2016).
[Crossref]

2015 (3)

2013 (2)

K. P. Quinn and I. Georgakoudi, “Rapid quantification of pixel-wise fiber orientation data in micrographs,” J. Biomed. Opt. 18(4), 046003 (2013).
[Crossref]

Y. Gan and C. P. Fleming, “Extracting three-dimensional orientation and tractography of myofibers using optical coherence tomography,” Biomed. Opt. Express 4(10), 2150–2165 (2013).
[Crossref]

2012 (3)

2011 (1)

H. Wang, A. J. Black, J. Zhu, T. W. Stigen, M. K. Al-Qaisi, T. I. Netoff, A. Abosch, and T. Akkin, “Reconstructing micrometer-scale fiber pathways in the brain: Multi-contrast optical coherence tomography based tractography,” NeuroImage 58(4), 984–992 (2011).
[Crossref]

2009 (1)

N. Ugryumova, J. Jacobs, M. Bonesi, and S. J. Matcher, “Novel optical imaging technique to determine the 3-D orientation of collagen fibers in cartilage: variable-incidence angle polarization-sensitive optical coherence tomography,” Osteoarthr. Cartil. 17(1), 33–42 (2009).
[Crossref]

2008 (2)

A. J. Pope, G. B. Sands, B. H. Smaill, and I. J. LeGrice, “Three-dimensional transmural organization of perimysial collagen in the heart,” Am. J. Physiol. - Hear. Circ. Physiol. 295(3), H1243–H1252 (2008).
[Crossref]

C. P. Fleming, C. M. Ripplinger, B. Webb, I. R. Efimov, and A. M. Rollins, “Quantification of cardiac fiber orientation using optical coherence tomography,” J. Biomed. Opt. 13(3), 030505 (2008).
[Crossref]

2007 (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. on Image Process. 16(8), 2080–2095 (2007).
[Crossref]

2006 (1)

S. Weiss, T. Jaermann, P. Schmid, P. Staempfli, P. Boesiger, P. Niederer, R. Caduff, and M. Bajka, “Three-dimensional fiber architecture of the nonpregnant human uterus determined ex vivo using magnetic resonance diffusion tensor imaging,” Anat. Rec., Part A 288A(1), 84–90 (2006).
[Crossref]

2004 (1)

W. Drexler, “Ultrahigh-resolution optical coherence tomography,” J. Biomed. Opt. 9(1), 47 (2004).
[Crossref]

2003 (1)

F. H. Silver, J. W. Freeman, and G. P. Seehra, “Collagen self-assembly and the development of tendon mechanical properties,” J. Biomech. 36(10), 1529–1553 (2003).
[Crossref]

2002 (1)

M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50(2), 174–188 (2002).
[Crossref]

Abosch, A.

H. Wang, A. J. Black, J. Zhu, T. W. Stigen, M. K. Al-Qaisi, T. I. Netoff, A. Abosch, and T. Akkin, “Reconstructing micrometer-scale fiber pathways in the brain: Multi-contrast optical coherence tomography based tractography,” NeuroImage 58(4), 984–992 (2011).
[Crossref]

Akkin, T.

H. Wang, A. J. Black, J. Zhu, T. W. Stigen, M. K. Al-Qaisi, T. I. Netoff, A. Abosch, and T. Akkin, “Reconstructing micrometer-scale fiber pathways in the brain: Multi-contrast optical coherence tomography based tractography,” NeuroImage 58(4), 984–992 (2011).
[Crossref]

Alonzo, C.

Z. Liu, D. Pouli, D. Sood, A. Sundarakrishnan, C. K. Hui Mingalone, L. M. Arendt, C. Alonzo, K. P. Quinn, C. Kuperwasser, L. Zeng, T. Schnelldorfer, D. L. Kaplan, and I. Georgakoudi, “Automated quantification of three-dimensional organization of fiber-like structures in biological tissues,” Biomaterials 116, 34–47 (2017).
[Crossref]

Al-Qaisi, M. K.

H. Wang, A. J. Black, J. Zhu, T. W. Stigen, M. K. Al-Qaisi, T. I. Netoff, A. Abosch, and T. Akkin, “Reconstructing micrometer-scale fiber pathways in the brain: Multi-contrast optical coherence tomography based tractography,” NeuroImage 58(4), 984–992 (2011).
[Crossref]

Ambrosi, C. M.

C. M. Ambrosi, V. V. Fedorov, R. B. Schuessler, A. M. Rollins, and I. R. Efimov, “Quantification of fiber orientation in the canine atrial pacemaker complex using optical coherence tomography,” J. Biomed. Opt. 17(7), 1 (2012).
[Crossref]

Arendt, L. M.

Z. Liu, D. Pouli, D. Sood, A. Sundarakrishnan, C. K. Hui Mingalone, L. M. Arendt, C. Alonzo, K. P. Quinn, C. Kuperwasser, L. Zeng, T. Schnelldorfer, D. L. Kaplan, and I. Georgakoudi, “Automated quantification of three-dimensional organization of fiber-like structures in biological tissues,” Biomaterials 116, 34–47 (2017).
[Crossref]

Arulampalam, M. S.

M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50(2), 174–188 (2002).
[Crossref]

Ávila, F. J.

Bajka, M.

S. Weiss, T. Jaermann, P. Schmid, P. Staempfli, P. Boesiger, P. Niederer, R. Caduff, and M. Bajka, “Three-dimensional fiber architecture of the nonpregnant human uterus determined ex vivo using magnetic resonance diffusion tensor imaging,” Anat. Rec., Part A 288A(1), 84–90 (2006).
[Crossref]

Balasubramanian, P. S.

D. Qu, P. J. Chuang, S. Prateepchinda, P. S. Balasubramanian, X. Yao, S. B. Doty, C. P. Hendon, and H. H. Lu, “Micro- and Ultrastructural Characterization of Age-Related Changes at the Anterior Cruciate Ligament-to-Bone Insertion,” ACS Biomater. Sci. Eng. 3(11), 2806–2814 (2017).
[Crossref]

Black, A. J.

H. Wang, A. J. Black, J. Zhu, T. W. Stigen, M. K. Al-Qaisi, T. I. Netoff, A. Abosch, and T. Akkin, “Reconstructing micrometer-scale fiber pathways in the brain: Multi-contrast optical coherence tomography based tractography,” NeuroImage 58(4), 984–992 (2011).
[Crossref]

Blanks, A. M.

E. J. Lutton, W. J. E. P. Lammers, S. James, H. A. van den Berg, and A. M. Blanks, “A computational method for three-dimensional reconstruction of the microarchitecture of myometrial smooth muscle from histological sections,” PLoS One 12(3), e0173404 (2017).
[Crossref]

Boesiger, P.

S. Weiss, T. Jaermann, P. Schmid, P. Staempfli, P. Boesiger, P. Niederer, R. Caduff, and M. Bajka, “Three-dimensional fiber architecture of the nonpregnant human uterus determined ex vivo using magnetic resonance diffusion tensor imaging,” Anat. Rec., Part A 288A(1), 84–90 (2006).
[Crossref]

Bonesi, M.

N. Ugryumova, J. Jacobs, M. Bonesi, and S. J. Matcher, “Novel optical imaging technique to determine the 3-D orientation of collagen fibers in cartilage: variable-incidence angle polarization-sensitive optical coherence tomography,” Osteoarthr. Cartil. 17(1), 33–42 (2009).
[Crossref]

Boote, C.

A. J. Quantock, M. Winkler, G. J. Parfitt, R. D. Young, D. J. Brown, C. Boote, and J. V. Jester, “From nano to macro: Studying the hierarchical structure of the corneal extracellular matrix,” Exp. Eye Res. 133, 81–99 (2015).
[Crossref]

Brown, D. J.

A. J. Quantock, M. Winkler, G. J. Parfitt, R. D. Young, D. J. Brown, C. Boote, and J. V. Jester, “From nano to macro: Studying the hierarchical structure of the corneal extracellular matrix,” Exp. Eye Res. 133, 81–99 (2015).
[Crossref]

Brown, R. J.

C. J. Stender, E. Rust, P. T. Martin, E. E. Neumann, R. J. Brown, and T. J. Lujan, “Modeling the effect of collagen fibril alignment on ligament mechanical behavior,” Biomech. Model. Mechanobiol. 17(2), 543–557 (2018).
[Crossref]

Bueno, J. M.

Caduff, R.

S. Weiss, T. Jaermann, P. Schmid, P. Staempfli, P. Boesiger, P. Niederer, R. Caduff, and M. Bajka, “Three-dimensional fiber architecture of the nonpregnant human uterus determined ex vivo using magnetic resonance diffusion tensor imaging,” Anat. Rec., Part A 288A(1), 84–90 (2006).
[Crossref]

Carmichael, O.

H. Yan, O. Carmichael, D. Paul, and J. Peng, “Estimating fiber orientation distribution from diffusion MRI with spherical needlets,” Med. Image Anal. 46, 57–72 (2018).
[Crossref]

Chuang, P. J.

D. Qu, P. J. Chuang, S. Prateepchinda, P. S. Balasubramanian, X. Yao, S. B. Doty, C. P. Hendon, and H. H. Lu, “Micro- and Ultrastructural Characterization of Age-Related Changes at the Anterior Cruciate Ligament-to-Bone Insertion,” ACS Biomater. Sci. Eng. 3(11), 2806–2814 (2017).
[Crossref]

Clapp, T.

M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50(2), 174–188 (2002).
[Crossref]

Courties, G.

C. Vinegoni, P. Fumene Feruglio, G. Courties, S. Schmidt, M. Hulsmans, S. Lee, R. Wang, D. Sosnovik, M. Nahrendorf, and R. Weissleder, “Fluorescence microscopy tensor imaging representations for large-scale dataset analysis,” Sci. Rep. 10(1), 5632 (2020).
[Crossref]

Dabov, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. on Image Process. 16(8), 2080–2095 (2007).
[Crossref]

DiMarzio, C.

Donnelly, E.

J. Hao, W. Yao, W. B. R. Harris, J. Y. Vink, K. M. Myers, and E. Donnelly, “Characterization of the collagen microstructural organization of human cervical tissue,” Reproduction 156(1), 71–79 (2018).
[Crossref]

Doty, S. B.

D. Qu, P. J. Chuang, S. Prateepchinda, P. S. Balasubramanian, X. Yao, S. B. Doty, C. P. Hendon, and H. H. Lu, “Micro- and Ultrastructural Characterization of Age-Related Changes at the Anterior Cruciate Ligament-to-Bone Insertion,” ACS Biomater. Sci. Eng. 3(11), 2806–2814 (2017).
[Crossref]

Drexler, W.

W. Drexler, “Ultrahigh-resolution optical coherence tomography,” J. Biomed. Opt. 9(1), 47 (2004).
[Crossref]

Duan, D.

G. Yao and D. Duan, “High-resolution 3D tractography of fibrous tissue based on polarization-sensitive optical coherence tomography,” Exp. Biol. Med. 245(4), 273–281 (2020).
[Crossref]

Efimov, I. R.

C. M. Ambrosi, V. V. Fedorov, R. B. Schuessler, A. M. Rollins, and I. R. Efimov, “Quantification of fiber orientation in the canine atrial pacemaker complex using optical coherence tomography,” J. Biomed. Opt. 17(7), 1 (2012).
[Crossref]

C. P. Fleming, C. M. Ripplinger, B. Webb, I. R. Efimov, and A. M. Rollins, “Quantification of cardiac fiber orientation using optical coherence tomography,” J. Biomed. Opt. 13(3), 030505 (2008).
[Crossref]

Egiazarian, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. on Image Process. 16(8), 2080–2095 (2007).
[Crossref]

Elad, D.

K. M. Myers and D. Elad, “Biomechanics of the human uterus,” Wiley Interdiscip. Rev.: Syst. Biol. Med. 9(5), e1388 (2017).
[Crossref]

Farah, C. S.

Fedorov, V. V.

C. M. Ambrosi, V. V. Fedorov, R. B. Schuessler, A. M. Rollins, and I. R. Efimov, “Quantification of fiber orientation in the canine atrial pacemaker complex using optical coherence tomography,” J. Biomed. Opt. 17(7), 1 (2012).
[Crossref]

Fleming, C. P.

Y. Gan and C. P. Fleming, “Extracting three-dimensional orientation and tractography of myofibers using optical coherence tomography,” Biomed. Opt. Express 4(10), 2150–2165 (2013).
[Crossref]

C. P. Fleming, C. M. Ripplinger, B. Webb, I. R. Efimov, and A. M. Rollins, “Quantification of cardiac fiber orientation using optical coherence tomography,” J. Biomed. Opt. 13(3), 030505 (2008).
[Crossref]

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. on Image Process. 16(8), 2080–2095 (2007).
[Crossref]

Freeman, J. W.

F. H. Silver, J. W. Freeman, and G. P. Seehra, “Collagen self-assembly and the development of tendon mechanical properties,” J. Biomech. 36(10), 1529–1553 (2003).
[Crossref]

Fumene Feruglio, P.

C. Vinegoni, P. Fumene Feruglio, G. Courties, S. Schmidt, M. Hulsmans, S. Lee, R. Wang, D. Sosnovik, M. Nahrendorf, and R. Weissleder, “Fluorescence microscopy tensor imaging representations for large-scale dataset analysis,” Sci. Rep. 10(1), 5632 (2020).
[Crossref]

Gan, Y.

Georgakoudi, I.

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G. Yao and D. Duan, “High-resolution 3D tractography of fibrous tissue based on polarization-sensitive optical coherence tomography,” Exp. Biol. Med. 245(4), 273–281 (2020).
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H. Yan, O. Carmichael, D. Paul, and J. Peng, “Estimating fiber orientation distribution from diffusion MRI with spherical needlets,” Med. Image Anal. 46, 57–72 (2018).
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H. Wang, A. J. Black, J. Zhu, T. W. Stigen, M. K. Al-Qaisi, T. I. Netoff, A. Abosch, and T. Akkin, “Reconstructing micrometer-scale fiber pathways in the brain: Multi-contrast optical coherence tomography based tractography,” NeuroImage 58(4), 984–992 (2011).
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[Crossref]

Reproduction (1)

J. Hao, W. Yao, W. B. R. Harris, J. Y. Vink, K. M. Myers, and E. Donnelly, “Characterization of the collagen microstructural organization of human cervical tissue,” Reproduction 156(1), 71–79 (2018).
[Crossref]

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C. Vinegoni, P. Fumene Feruglio, G. Courties, S. Schmidt, M. Hulsmans, S. Lee, R. Wang, D. Sosnovik, M. Nahrendorf, and R. Weissleder, “Fluorescence microscopy tensor imaging representations for large-scale dataset analysis,” Sci. Rep. 10(1), 5632 (2020).
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Supplementary Material (8)

NameDescription
» Visualization 1       Fiber tractography for a 1.4 x 1.4 x 0.75 mm sub-volume of non-pregnant uterine tissue. The fiber tracts (blue) are overlayed on the OCT image volume for visual comparison. The video shows an en-face view beginning at the tissue surface and progresse
» Visualization 2       Fiber tractography visualization of an anterior uterine tissue block from a non-pregnant human specimen. The 3-D fiber tracts show the collagen fiber network detected using OCT imaging and particle filtering. The colors differentiate two distinct, in
» Visualization 3       Fiber tractography visualization of an anterior uterine tissue block from a non-pregnant human specimen. The 3-D fiber tracts show the collagen fiber network detected using OCT imaging and particle filtering. The colors differentiate two distinct, in
» Visualization 4       Video through depth of an OCT mosaic volume of a fundus uterine tissue slice from Patient 1 (non-pregnant). The video shows an en-face view beginning at the tissue surface and progressing through the tissue depth. Bright fibrous structures are collag
» Visualization 5       Video through depth of an OCT mosaic volume of a posterior uterine tissue slice from Patient 2 (non-pregnant). The video shows an en-face view beginning at the tissue surface and progressing through the tissue depth. Bright fibrous structures are col
» Visualization 6       Video through depth of an OCT mosaic volume of a fundus uterine tissue slice from Patient 3 (non-pregnant). The video shows an en-face view beginning at the tissue surface and progressing through the tissue depth. Bright fibrous structures are collag
» Visualization 7       Video through depth of an OCT mosaic volume of an anterior uterine tissue slice from Patient 4 (pregnant). The video shows an en-face view beginning at the tissue surface and progressing through the tissue depth. Bright fibrous structures are collage
» Visualization 8       Video through depth of an OCT mosaic volume of a posterior uterine tissue slice from Patient 5 (pregnant). The video shows an en-face view beginning at the tissue surface and progressing through the tissue depth. Bright fibrous structures are collage

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Figures (14)

Fig. 1.
Fig. 1. Graphical representation of the 3-D fiber analysis pipeline. (a) Sample preparation, OCT volumetric imaging, and stitching to acquire mosaic volumes. (b) Image enhancement pipeline for reducing noise and artifacts while improving fiber contrast. (c) 3-D fiber orientation maps produced through a combination of b-scan and en-face analysis to obtain $\theta$ and $\phi$. (d) Histogram analysis of $\theta$ and $\phi$ reveals fiber groups while a 3-D particle filter technique is used to create fiber tracts.
Fig. 2.
Fig. 2. Coordinate system and fiber orientation diagram. (a) 3-D image volumes are imaged as a series of b-scans (XZ) with uniform spacing in Y to capture the entire uterine tissue slice. The solid red arrow represents a collagen fiber of unit length which is defined by the angle it makes with the positive Z-axis ($\phi$) and the positive X-axis ($\theta$). $\theta$ is measured directly from en-face image slices, while $\phi$ is calculated from $\theta$ and its projection onto either either transverse plane, i.e. $\phi _x$ or $\phi _y$ (Eq. (1)). (b) The measured $\theta$ and $\phi$ orientations are displayed in this manuscript according to the shown HSV colorwheels. Based on the orientation of the tissue during imaging, an angle of $\phi = 0^{\circ }$ (red) indicates fiber alignment perpendicular to the tissue surface while $\phi = 90^{\circ }$ (cyan) indicates fiber alignment parallel to the tissue surface.
Fig. 3.
Fig. 3. Matching OCT and histology to verify tissue composition in a non-pregnant uterine sample. (a) Masson’s trichrome stain histology section from anterior location. The orange stain indicates smooth muscle cells (SMCs) and the blue stain indicates collagen fibers. (b) OCT en-face image from the matching uterine tissue sample. Visual comparison of (a) and (b) indicates that the bright intensity fibers in the OCT image are collagen and the darker intensity areas between the fibers are SMCs.
Fig. 4.
Fig. 4. Step-by-step process of b-scan image enhancement. (a) Un-processed b-scan from a non-pregnant (NP) anterior uterine tissue sample. (b) Resulting image from vertical reflection artifact removal. (c) VBM3D denoised image which smooths the image and fiber tracks by mitigating speckle noise. (d) The fully enhanced b-scan which results from homomorphic filtering of the denoised image. Image enhancement improves the Radon-based fiber orientation analysis. (e) Un-processed image patch from marked in (a) by the green square. (f) Processing image patch from the same location as (e). (g) Radon-based fiber orientation distribution for image patch (e). (h) Radon-based fiber orientation distribution for image patch (f). Reflection artifact removal has reduced the peak at $90^\circ$ while denoising and contrast enhancement have exaggerated the fiber-based peaks at $10^\circ$ and $60^\circ$. Scale Bar = 0.5 mm
Fig. 5.
Fig. 5. Example of the b-scan pre-processing pipeline with edge detection and image patching. (a) Enhanced b-scan from pregnant (PG) fundus specimen (Scale bar = 0.5 mm). Upper and lower tissue edges are drawn in red and processing patches are displayed as the blue squares. (b) Interpolated fiber orientation across the full b-scan. Red circles indicate the fiber orientation measured in each blue patch. (c) Inset of the green patch in (a) (Scale bar = 200 $\mu$m). (d) Fiber orientation distribution for the image patch in (c). The peak at $20^{\circ }$ corresponds to the dominant fiber orientation captured in the image patch.
Fig. 6.
Fig. 6. 3-D fiber orientation mapping example for simulation image volume. (a) Simulated fibers generated at $\theta = 20^{\circ }$ and $\phi = 70^{\circ }$ and evenly distributed throughout an image volume. (b) En-Face (XY) view of image volume (red plane in (a)). (c) Side view (XZ) of image volume (green plane in (a)). (d) Map of the depth-averaged $\theta$ orientation measured using the proposed fiber orientation algorithm. Total average orientation measured was $22.58^{\circ }$ ($2.58^{\circ }$ error). (e) Map of the depth-averaged $\phi$ orientation measured using the proposed fiber orientation algorithm. Total average orientation measured was $69.96^{\circ }$ degrees ($0.04^{\circ }$ error).
Fig. 7.
Fig. 7. Fiber proximity simulation mapping test. A group of uniform randomly oriented fibers were simulated with mean angles $\mu _\theta = 20^\circ$ and $\mu _\phi = 20^\circ$ and standard of deviation $\sigma _\theta$ and $\sigma _\phi$ (a) Simulated fiber bundle with all fibers at the same orientation of $\theta = 20^\circ$ and $\phi = 70^\circ$. (b) Simulated fiber bundle with moderate deviation in orientation from the simulation in (a) ($\sigma _\phi = 20^\circ$, $\sigma _\theta = 30^\circ$). (c) Simulated fiber bundle with heavy deviation in orientation from the simulation in (a) ($\sigma _\phi = 45^\circ$, $\sigma _\theta = 90^\circ$). (d) Absolute value error in $\theta$, $e_\theta$. (e) Absolute value error in $\phi$, $e_\phi$. $e_\theta$ and $e_\phi$ both increase with $\sigma _\theta$ and $\sigma _\phi$. $e_\theta$ is nearly independent of $\sigma _\phi$, while $e_\phi$ is affected most significantly by $\sigma _\theta$.
Fig. 8.
Fig. 8. Length scale simulation test. Average orientation ($\mu _\theta$, $\mu _\phi$) and error ($e_\theta$, $e_\phi$) were measured for different window sizes and variance in local fiber orientation ($\sigma _\theta$, $\sigma _\phi$). The legend shows the three simulation cases that were tested. For large $\sigma _\theta$ and $\sigma _\phi$, $e_\theta$ and $e_\phi$ increase with window size (yellow), but remain constant with window size for small $\sigma _\theta$ and $\sigma _\phi$ (blue). $\mu _\theta$ and $\mu _\phi$ remain within $+/-5^\circ$ for all three simulation cases and window sizes greater than 21 x 21 pixels. (a) Average angle $\mu _\theta$. (b) Average error $e_\theta$. (c) Average angle $\mu _\phi$. (d) Average error $e_\phi$.
Fig. 9.
Fig. 9. Example of image enhancement pre-processing pipeline. (a) B-scan from a NP specimen (anterior, myometrium). (b) Enhanced version of (a). (c) B-scan from a PG specimen (fundus, myometrium). (d) Enhanced version of (c). Scalebar = 0.5 cm.
Fig. 10.
Fig. 10. 3-D fiber model validation using OCT uterus image sub-volume. (a) 3-D view of the OCT image sub-volume spanning 2.85 x 2.85 x 1.5 mm volume. (b) 3-D view of fiber tracts in (a) using particle filtering technique. (c) OCT sum-volume projection in the XY (en-face) plane. (d) Depth-averaged en-face ($\theta$) fiber orientation map. (e) En-Face (XY) view of the 3-D particle filter model. (f) OCT sum-volume projection in the XZ (transverse) plane. (g) Depth-averaged incline($\phi$) fiber orientation map. (h) Side (XZ) view of the 3-D particle filter model. Fiber tract colors were chosen randomly to enhance visual contrast. Scale bar = 0.5 mm.
Fig. 11.
Fig. 11. En-Face ($\theta$) and incline ($\phi$) collagen fiber orientation maps from a non-pregnant (NP) and pregnant (PG) uterine sample. There are two different visualizations of these maps shown: 2-D depth-averaged orientation maps which are overlayed onto the OCT sum-volume projection for visualization (a,b,e,f) and full 3-D block visualizations (c,d,g,h). The different colors indicate the collagen fiber’s en-face orientation (a,c,e,g) or its incline with respect to the tissue surface (b,d,f,h) according to the displayed colormaps where $\theta ,\phi \in [0^{\circ },180^{\circ }]$. Scalebar = 0.5 cm.
Fig. 12.
Fig. 12. Histogram analysis reveals fiber groups in the non-pregnant uterus sample. (a,b) Histogram of $\theta$ and $\phi$ orientations for full mosaic OCT image volume of an NP (anterior) uterus tissue specimen. (c) En-Face sum-volume image of the uterus specimen. Color overlay shows the depth-averaged $\theta$ orientation map. (d) Histogram analysis was divided into three separate sub-volumes, as indicated by the color outlines in (c). Each section covers a dominant fiber group with the corresponding histograms revealing the dominant orientation and dispersion of each group. Scale bar = 0.5 cm.
Fig. 13.
Fig. 13. Histogram analysis reveals fiber groups in the pregnant uterus sample. (a,b) Histogram of $\theta$ and $\phi$ orientations for full mosaic OCT image volume of an PG uterus tissue specimen. (c) En-Face sum-volume image of the uterus specimen. Color overlay shows the depth-averaged $\theta$ orientation map. (d) Histogram analysis was divided into three separate sub-volumes, as indicated by the color outlines in (c). Each section covers a dominant fiber group with the corresponding histograms revealing the dominant orientation and dispersion of each group. Scale bar = 0.5 cm.
Fig. 14.
Fig. 14. 3-D fiber model using particle filter technique for an NP sample. Color represents two dominate fiber groups identified via histogram analysis (see Fig. 12). a) View 1. b) View 2. Scale bar = 4 mm.

Tables (1)

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Table 1. Patient specific obstetric historya

Equations (2)

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ϕ = arctan cos ϕ x cos θ sin ϕ x = arctan cos ϕ y sin θ sin ϕ y
ϕ x , y = arctan ( α tan ϕ x , y )