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<p>Transplantation of donor grafts recellularized with recipient-derived or non-immunogenic universal cells is a potential means of reducing the graft rejection and post-transplant complications in lung transplantation. Achieving a fully recellularized lung, however, remains a far-off goal and has several limitations, including inadequate cell coverage of the acellular scaffold. A key parameter for evaluating recellularization efficacy is the cell seeding coverage (CSC); the ratio of seeded cell area to the total area of the lung scaffold. To calculate the CSC from a histological image, the lung scaffold and the seeded cell areas must be quantified. In this work, the ability of semantic segmentation to accurately automate the pixel-wise analysis of histological images is investigated. Specifically, the U-Net and LinkNet models are applied to re-endothelialized mouse lung images, generating pixel-wise classifications of the lung scaffold and the seeded cell areas to calculate the CSC. Model performance when trained on complete images and on image patches is compared. The patch-based U-Net model outperformed the other models, predicting CSC with a root mean square error of 2.23 ± 0.36%, in addition to classifying lung scaffold pixels and seeded cell pixels with intersection over union scores of 77.8 ± 1.4% and 69.5 ± 1.1% respectively.</p>

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Computer vision to predict cell seeding coverage in re-endothelialized mouse lungs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月19日

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  • Joshua Paciocco,
  • Ahmed Hasan,
  • Jason Chan,
  • Daisuke Taniguchi,
  • Cristina Amon,
  • Golnaz Karoubi,
  • Aimy Bazylak

摘要

Transplantation of donor grafts recellularized with recipient-derived or non-immunogenic universal cells is a potential means of reducing the graft rejection and post-transplant complications in lung transplantation. Achieving a fully recellularized lung, however, remains a far-off goal and has several limitations, including inadequate cell coverage of the acellular scaffold. A key parameter for evaluating recellularization efficacy is the cell seeding coverage (CSC); the ratio of seeded cell area to the total area of the lung scaffold. To calculate the CSC from a histological image, the lung scaffold and the seeded cell areas must be quantified. In this work, the ability of semantic segmentation to accurately automate the pixel-wise analysis of histological images is investigated. Specifically, the U-Net and LinkNet models are applied to re-endothelialized mouse lung images, generating pixel-wise classifications of the lung scaffold and the seeded cell areas to calculate the CSC. Model performance when trained on complete images and on image patches is compared. The patch-based U-Net model outperformed the other models, predicting CSC with a root mean square error of 2.23 ± 0.36%, in addition to classifying lung scaffold pixels and seeded cell pixels with intersection over union scores of 77.8 ± 1.4% and 69.5 ± 1.1% respectively.