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Updated: Apr 30, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Tiny Lungs, Big Challenges: Pediatric and Premature Lung Segmentation using Deep Learning.

Hareem Nisar1, Syed Muhammad Anwar1, Maria C Rujana2

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|December 12, 2025
PubMed
Summary
This summary is machine-generated.

Accurate lung segmentation in premature infant X-rays is challenging. A novel two-step deep learning method improves lung delineation, achieving high accuracy for pediatric and premature chest imaging.

Keywords:
Lung segmentationbronchopulmonary dysplasia (BPD)chest x-raysdeep learningpremature infants

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pediatric Radiology

Background:

  • Lung segmentation is crucial for diagnosing pulmonary diseases from X-rays.
  • Segmenting lungs in premature infant X-rays presents unique challenges due to anatomical variations and artifacts.

Purpose of the Study:

  • To develop and validate a robust deep learning method for accurate lung segmentation in pediatric and premature X-rays.
  • To address the difficulties in segmenting small, variably presented lungs in premature infants.

Main Methods:

  • A two-step deep learning approach involving lung detection followed by retro-cardiac region segmentation was proposed.
  • The UNETR segmentation model was fine-tuned on pediatric and premature cohorts using a weighted loss, after pretraining on a large dataset of chest X-rays.

Main Results:

  • The proposed method achieved high accuracy in lung segmentation for both pediatric and premature X-rays.
  • Mean Dice scores of 0.960 for pediatric and 0.946 for premature cohorts were obtained.
  • Mean Hausdorff distances were 6.576 pixels for pediatric and 8.124 pixels for premature cohorts.

Conclusions:

  • The developed two-step deep learning strategy effectively segments lungs in challenging pediatric and premature X-ray images.
  • This method offers a significant advancement for automated clinical diagnosis and severity grading in neonates and children.