Optical coherence tomography of human fetal membrane sub-layers during loading

Kayvan Samimi; Emmanuel Contreras Guzman; May Wu; Lindsey Carlson; Helen Feltovich; Timothy J Hall; Kristin M Myers; Michelle L Oyen; Melissa C Skala

doi:10.1364/BOE.489691

Optical coherence tomography of human fetal membrane sub-layers during loading

Biomed Opt Express. 2023 May 25;14(6):2969-2985. doi: 10.1364/BOE.489691. eCollection 2023 Jun 1.

Authors

Kayvan Samimi¹, Emmanuel Contreras Guzman¹, May Wu¹, Lindsey Carlson², Helen Feltovich², Timothy J Hall³, Kristin M Myers⁴, Michelle L Oyen⁵, Melissa C Skala^{1

6}

Affiliations

¹ Morgridge Institute for Research, Madison, WI 53715, USA.
² Department of Obstetrics and Gynecology, Intermountain Healthcare, Provo, UT 84604, USA.
³ Department of Medical Physics, University of Wisconsin, Madison, WI 53705, USA.
⁴ Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA.
⁵ Department of Biomedical Engineering, Washington University, St. Louis, MO 63105, USA.
⁶ Department of Biomedical Engineering, University of Wisconsin, Madison, WI 53706, USA.

Abstract

Fetal membranes have important mechanical and antimicrobial roles in maintaining pregnancy. However, the small thickness (<800 µm) of fetal membranes places them outside the resolution limits of most ultrasound and magnetic resonance systems. Optical imaging methods like optical coherence tomography (OCT) have the potential to fill this resolution gap. Here, OCT and machine learning methods were developed to characterize the ex vivo properties of human fetal membranes under dynamic loading. A saline inflation test was incorporated into an OCT system, and tests were performed on n = 33 and n = 32 human samples obtained from labored and C-section donors, respectively. Fetal membranes were collected in near-cervical and near-placental locations. Histology, endogenous two photon fluorescence microscopy, and second harmonic generation microscopy were used to identify sources of contrast in OCT images of fetal membranes. A convolutional neural network was trained to automatically segment fetal membrane sub-layers with high accuracy (Dice coefficients >0.8). Intact amniochorion bilayer and separated amnion and chorion were individually loaded, and the amnion layer was identified as the load-bearing layer within intact fetal membranes for both labored and C-section samples, consistent with prior work. Additionally, the rupture pressure and thickness of the amniochorion bilayer from the near-placental region were greater than those of the near-cervical region for labored samples. This location-dependent change in fetal membrane thickness was not attributable to the load-bearing amnion layer. Finally, the initial phase of the loading curve indicates that amniochorion bilayer from the near-cervical region is strain-hardened compared to the near-placental region in labored samples. Overall, these studies fill a gap in our understanding of the structural and mechanical properties of human fetal membranes at high resolution under dynamic loading events.