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Related Experiment Video

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

Joshua Paciocco1, Ahmed Hasan1, Jason Chan1

  • 1Department of Mechanical and Industrial Engineering, Faculty of Applied Science and Engineering, University of Toronto, 5 King's College Road, Toronto, ON, M5S 3G8, Canada.

Scientific Reports
|July 19, 2025
PubMed
Summary

Automated analysis of histological images using semantic segmentation can accurately quantify cell seeding coverage (CSC) in recellularized lungs. The patch-based U-Net model achieved high accuracy, improving lung transplantation research.

Keywords:
Bioengineered lungCell seeding coverageLung re-endothelializationMachine learningMedical imagesSemantic segmentation

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Area of Science:

  • Biomedical Engineering
  • Regenerative Medicine
  • Computational Pathology

Background:

  • Lung transplantation aims to reduce rejection via recellularized grafts, but achieving adequate cell coverage is challenging.
  • Cell seeding coverage (CSC) is a critical metric for evaluating recellularization efficacy, requiring accurate quantification of lung scaffold and seeded cell areas.
  • Current histological image analysis for CSC is manual and time-consuming, limiting scalability and reproducibility.

Purpose of the Study:

  • To investigate the efficacy of semantic segmentation models (U-Net and LinkNet) for automated pixel-wise analysis of histological images.
  • To accurately quantify lung scaffold and seeded cell areas for calculating cell seeding coverage (CSC).
  • To compare the performance of U-Net and LinkNet models, including training on full images versus image patches.

Main Methods:

  • Application of U-Net and LinkNet semantic segmentation models to histological images of re-endothelialized mouse lungs.
  • Pixel-wise classification of lung scaffold and seeded cell areas to enable automated CSC calculation.
  • Comparative analysis of model performance based on training strategy (full images vs. image patches).

Main Results:

  • The patch-based U-Net model demonstrated superior performance in predicting CSC, achieving a root mean square error of 2.23 ± 0.36%.
  • High intersection over union (IoU) scores were obtained for classifying lung scaffold (77.8 ± 1.4%) and seeded cell pixels (69.5 ± 1.1%).
  • Automated analysis significantly improved the accuracy and efficiency of CSC quantification compared to manual methods.

Conclusions:

  • Semantic segmentation, particularly the patch-based U-Net model, offers a robust and accurate method for quantifying cell seeding coverage in lung recellularization.
  • This automated approach facilitates the evaluation of recellularization efficacy, potentially accelerating advancements in lung transplantation research.
  • Accurate CSC measurement is crucial for developing improved strategies to reduce graft rejection and post-transplant complications.