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Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning.

Hyo Min Lee1, Young Jae Kim1, Kwang Gi Kim2

  • 1Department of Biomedical Engineering, College of Health Science, Gachon University, Incheon 21936, Korea.

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|May 20, 2022
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Summary

Deep learning models show a 6-7% difference in rib segmentation performance on chest X-rays due to regional characteristics. Understanding these variations is crucial for developing more precise medical imaging algorithms.

Keywords:
chest X-raydeep learningproperties by areassegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Chest radiography is a common diagnostic tool, but overlapping tissues complicate rib visibility.
  • Current deep learning models struggle with elaborate rib segmentation due to regional variations.

Purpose of the Study:

  • To investigate deep learning performance differences in rib segmentation across various chest X-ray regions.
  • To determine if deep learning models adequately capture regional rib characteristics.
  • To identify factors contributing to performance disparities in medical image segmentation.

Main Methods:

  • Utilized 195 normal chest X-ray datasets with data augmentation.
  • Employed 5-fold cross-validation for robust evaluation.
  • Segmented ribs by dividing images vertically and horizontally based on anatomical landmarks (spine, clavicle, heart, lower organs).

Main Results:

  • Deep learning models exhibited a 6-7% variation in rib segmentation performance based on anatomical region.
  • Significant performance differences were observed across different rib regions.
  • These regional disparities in performance are statistically significant and cannot be overlooked.

Conclusions:

  • Deep learning performance in rib segmentation is influenced by regional characteristics on chest X-rays.
  • Further development is needed to address these regional variations for improved accuracy.
  • This study provides insights for creating more precise and practical deep learning algorithms for medical image analysis.