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Related Experiment Video

Updated: Jun 20, 2026

A Rat Lung Transplantation Model of Warm Ischemia/Reperfusion Injury: Optimizations to Improve Outcomes
07:37

A Rat Lung Transplantation Model of Warm Ischemia/Reperfusion Injury: Optimizations to Improve Outcomes

Published on: October 28, 2021

Transplant-ready? Evaluating AI lung segmentation models in candidates with severe lung disease.

Jisoo Lee1, Michael R Harowicz1, Yuwen Chen2

  • 1Duke University, Department of Biostatistics and Bioinformatics, Durham, North Carolina, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|June 19, 2026
PubMed
Summary

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This summary is machine-generated.

The Unet-R231 deep learning model offers the most accurate lung segmentation for lung transplant planning, though all models struggle with severe lung disease. Further fine-tuning is needed for complex cases.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Thoracic Surgery

Background:

  • Accurate lung segmentation is crucial for preoperative planning in lung transplantation.
  • Deep learning models offer potential for automated lung segmentation.
  • Evaluating model performance across diverse pathologies and disease severities is essential.

Purpose of the Study:

  • To assess the performance of publicly available deep learning lung segmentation models.
  • To compare models (Unet-R231, TotalSegmentator, MedSAM) in transplant-eligible patients.
  • To identify limitations for preoperative planning in lung transplantation.

Main Methods:

  • Retrospective analysis of 32 patients' chest CT scans (3645 slices).
  • Lung segmentation using Unet-R231, TotalSegmentator, and MedSAM.
Keywords:
CTdeep learninglungtransplant

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Last Updated: Jun 20, 2026

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Published on: October 28, 2021

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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  • Performance evaluation via quantitative metrics (volumetric similarity, Dice, Hausdorff) and qualitative assessment.

    • Main Results:

    • Unet-R231 demonstrated superior performance compared to TotalSegmentator and MedSAM (p < 0.05).
    • All models exhibited significant performance decline in moderate-to-severe cases, particularly volumetric similarity (p < 0.05).
    • No significant differences in performance were observed between lung sides or pathology types.

    Conclusions:

    • Unet-R231 achieved the most accurate automated lung segmentation.
    • Model performance significantly degrades with increasing disease severity.
    • Specialized model fine-tuning is necessary for cases with severe lung pathology.