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Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment.

Rui Liu1,2, Yuanyuan Jia1, Xiangqian He1

  • 1Department of Medical Informatics, Chongqing Medical University, Chongqing 401331, China.

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|November 12, 2020
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Summary
This summary is machine-generated.

This study introduces an efficient automatic hand radiograph segmentation method for pediatric bone age assessment (BAA). The novel approach improves BAA accuracy by at least 13%, offering a precise and fast solution for clinical use.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Pediatric Radiology

Background:

  • Accurate bone age assessment (BAA) in children is crucial for diagnosing growth disorders.
  • Hand radiograph segmentation is a critical but challenging step in automated BAA.
  • Existing segmentation methods lack the required precision and efficiency for clinical application.

Purpose of the Study:

  • To develop a highly precise and efficient automatic segmentation method for pediatric hand radiographs.
  • To improve the accuracy of bone age assessment (BAA) through enhanced radiograph segmentation.

Main Methods:

  • Hand radiograph segmentation framed as a classification problem, predicting optimal thresholds.
  • Utilized normalized histogram, mean, and variance as input features for ensemble learning classifiers.
  • Trained and validated the model on 600 pediatric left-hand radiographs (age 1-18).

Main Results:

  • The proposed method outperformed traditional techniques and U-Net in precision and computational load.
  • Achieved high performance metrics: PSNR (52.43 dB), SSIM (0.97), DSC (0.97), and JSI (0.91).
  • Demonstrated an average BAA performance improvement of at least 13% post-segmentation.

Conclusions:

  • The developed automatic segmentation method is suitable for clinical application in pediatric BAA.
  • High-precision hand radiograph segmentation significantly enhances bone age assessment accuracy.
  • This approach offers a promising advancement for automated radiological diagnostics in pediatrics.