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Rapid Quantification of Microvessels of Three-Dimensional Blood-Brain Barrier Model Using Optical Coherence

Huiting Zhang1,2, Dong-Hee Kang2, Marie Piantino2

  • 1AIST-Osaka University Advanced Photonics and Biosensing Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology (AIST), 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan.

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|August 25, 2023
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Summary

We developed a rapid, non-invasive optical coherence tomography (OCT) method to quantify microvessels in a 3D blood-brain barrier (BBB) model. Deep learning analysis accurately measured vessel networks, aiding BBB model evaluation and drug delivery research.

Keywords:
3D BBB modelOCT image processingvessel quantification

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Optical Imaging

Background:

  • The blood-brain barrier (BBB) protects the central nervous system (CNS) and is crucial for homeostasis.
  • In vitro BBB models are vital for studying diseases and drug delivery.
  • A previously developed 3D BBB model with perfusable microvasculature replicates key native BBB features.

Purpose of the Study:

  • To develop and validate a rapid, non-invasive optical coherence tomography (OCT)-based approach for quantifying microvessel networks in a 3D in vitro BBB model.
  • To evaluate image processing strategies including morphological image processing (MIP), random forest machine learning (RF-TWS), and deep learning (pix2pix cGAN).
  • To establish a crucial quality control and permeability evaluation method for 3D in vitro BBB models.

Main Methods:

  • Acquisition of 3D OCT images of the 3D in vitro BBB model.
  • Image processing using MIP, RF-TWS, and deep learning (pix2pix cGAN).
  • Quantitative comparison of processed images against manually selected ground truth images for vessel counts and surface areas.

Main Results:

  • Successful acquisition of 3D OCT images of the 3D in vitro BBB model.
  • Deep learning (pix2pix cGAN) demonstrated superior performance in object identification compared to MIP and RF-TWS.
  • Deep learning-based quantification of vessel counts and surface areas closely approximated ground truth values.

Conclusions:

  • The developed OCT-based approach provides a rapid, non-invasive method for quantifying microvessel networks in 3D in vitro BBB models.
  • Deep learning analysis of OCT images is highly effective for evaluating BBB model integrity and permeability.
  • This technique offers a valuable tool for quality control and research involving various 3D in vitro models beyond the BBB.