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Related Concept Videos

Classification of Bones01:18

Classification of Bones

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
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Updated: Dec 7, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Bone age assessment based on deep convolution neural network incorporated with segmentation.

Yunyuan Gao1,2, Tao Zhu3, Xiaohua Xu4

  • 1Intelligent Control and Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou, China. gyy@hdu.edu.cn.

International Journal of Computer Assisted Radiology and Surgery
|September 28, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an automated deep learning method for accurate bone age assessment, significantly reducing background noise interference. The new model achieves a mean absolute error of 9.997 months, outperforming existing techniques.

Keywords:
Attention mechanismBone age assessmentDeep learningSegmentation

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Pediatric Endocrinology

Background:

  • Bone age assessment is crucial for adolescent maturity evaluation and has applications in orthodontics, kinematics, pediatrics, and forensic science.
  • Existing bone age assessment methods often overlook the impact of background noise, potentially affecting accuracy.
  • Accurate bone age determination is vital for diagnosing and managing adolescent growth and development disorders.

Purpose of the Study:

  • To develop an automated bone age assessment method utilizing deep convolutional neural networks.
  • To address the challenge of background noise interference in bone age evaluation.
  • To improve the accuracy and reliability of bone age determination.

Main Methods:

  • A two-phase approach was employed: image segmentation using U-Net to isolate hand bone regions and remove background interference.
  • A classification phase utilized Visual Geometry Group Network (VGGNet) enhanced with an attention mechanism to focus on critical hand bone areas.
  • The model was trained and validated on the RSNA2017 Pediatric Bone Age dataset.

Main Results:

  • The developed deep learning model demonstrated superior performance compared to standard VGGNet.
  • The automated method achieved a mean absolute error of 9.997 months in bone age assessment.
  • The model effectively mitigated the impact of background noise, leading to more accurate evaluations.

Conclusions:

  • An automated deep learning-based bone age assessment method was successfully established.
  • The proposed method efficiently eliminates background interference, enhancing evaluation accuracy.
  • This approach offers significant reference value for bone age determination and aids in preventing adolescent growth-related diseases.