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Related Experiment Video

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Semi-automated micro-computed tomography lung segmentation and analysis in mouse models.

Jonathan D Luisi1, Jonathan L Lin1, Lorenzo F Ochoa1

  • 1University of Texas Medical Branch, 301 University Blvd., Galveston, TX 77555, United States.

Methodsx
|May 8, 2023
PubMed
Summary

This study introduces a semi-automated segmentation method for microCT lung imaging in animal models. This approach enhances efficiency and reduces reader variability in preclinical toxicity assessments.

Keywords:
Automated Image SegmentationBleomycinImage analysisLung tissue densityManual image segmentationPulmonary toxicologySemiautomated Micro-CT Lung Segmentation and Analysis in Mouse Models

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Area of Science:

  • Preclinical imaging analysis
  • Toxicology and drug development
  • Medical image processing

Background:

  • Computed Tomography (CT) is crucial for diagnosing lung pathologies but requires extensive manual segmentation for preclinical animal models.
  • Manual segmentation in microCT lung analysis is time-consuming, inefficient, and prone to reader bias, hindering preclinical trial data acquisition.
  • Existing commercial software and manual methods for microCT lung segmentation present significant limitations in preclinical research.

Purpose of the Study:

  • To develop an efficient and unbiased semi-automated segmentation method for microCT lung imaging in animal models.
  • To improve the throughput and reduce reader variance in the analysis of experimental injury and toxicity models.
  • To provide a cost-effective and accessible image analysis solution for preclinical research.

Main Methods:

  • Development of a streamlined semi-manual and semi-automated segmentation process for microCT lung data.
  • Implementation using a cross-platform, open-source ImageJ program with low computational demands.
  • Validation of the method for assessing experimental injury and toxicity models in animal studies.

Main Results:

  • Achieved quantifiable segmentations through both manual and semi-automated analysis.
  • Demonstrated deterministic results and increased efficiency via an unbiased, parameter-free automated process.
  • Significantly reduced reader variance, user time, and increased data analysis throughput.

Conclusions:

  • The developed semi-automated segmentation method offers a significant improvement over traditional manual approaches for microCT lung analysis in preclinical settings.
  • This open-source, cost-effective solution enhances efficiency, reduces bias, and increases throughput for toxicity and injury assessments in animal models.
  • The method provides a valuable tool for researchers needing comprehensive datasets for evaluating experimental treatments in preclinical trials.