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Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays
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A multi-scale data fusion framework for bone age assessment with convolutional neural networks.

Yu Liu1, Chao Zhang1, Juan Cheng1

  • 1Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China.

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|April 21, 2019
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Summary

This study introduces a novel deep learning framework for automated bone age assessment (BAA) using X-ray images. The multi-scale fusion method significantly improves accuracy compared to traditional approaches.

Keywords:
Bone age assessment (BAA)Convolutional neural networks (CNNs)Data fusionFeature extractionNon-subsampled contourlet transform (NSCT)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Bone age assessment (BAA) is crucial for diagnosing endocrine disorders and predicting adolescent height.
  • Conventional BAA methods are often manual and time-consuming.
  • Deep learning offers potential for automated and accurate BAA.

Purpose of the Study:

  • To propose a multi-scale data fusion framework for automated BAA using X-ray images.
  • To leverage Non-Subsampled Contourlet Transform (NSCT) and Convolutional Neural Networks (CNNs) for enhanced feature extraction.
  • To compare feature-level and decision-level fusion strategies for BAA.

Main Methods:

  • A novel framework combining NSCT for multi-scale feature extraction and CNNs for BAA.
  • Pre-extraction of image features using NSCT to overcome limited annotated data.
  • Individual processing of NSCT coefficient maps at different scales, followed by information merging.

Main Results:

  • The proposed multi-scale data fusion framework achieved promising results on the Digital Hand Atlas dataset.
  • The method outperformed existing state-of-the-art BAA techniques.
  • Significant improvements were observed compared to spatial domain approaches, with over 0.1 years reduction in mean absolute error.

Conclusions:

  • The developed NSCT-CNN framework demonstrates high potential for automated bone age assessment.
  • Multi-scale feature extraction and fusion enhance BAA accuracy, especially with limited data.
  • This approach offers a robust alternative to conventional BAA methods.