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Image Segmentation Technology Based on Attention Mechanism and ENet.

Ling Ma1, Xiaomao Hou1, Zhi Gong1

  • 1School of Computer Science and Engineering, Hunan University of Information Technology, Changsha 410151, Hunan, China.

Computational Intelligence and Neuroscience
|August 15, 2022
PubMed
Summary

This study introduces an improved method for segmenting tooth CT images using an attention mechanism with ENet. The novel approach enhances segmentation accuracy and efficiency compared to traditional methods.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Increasing volumes of medical imaging data (CT, MRI) necessitate automated analysis.
  • Manual segmentation of medical images is time-consuming and prone to errors.
  • Automated segmentation techniques are crucial for efficient medical diagnosis and treatment.

Purpose of the Study:

  • To develop an automated tooth CT image segmentation method.
  • To improve segmentation accuracy and efficiency using an attention mechanism and ENet.
  • To evaluate the performance of the proposed method against traditional algorithms.

Main Methods:

  • Utilized dilated convolution with a spatial information path to preserve image resolution.
  • Integrated an attention mechanism into the segmentation network to enhance feature recognition.
  • Employed a feature fusion module for final tooth CT image segmentation.
  • Validated the method on a tooth CT image dataset from West China Hospital.

Main Results:

  • Achieved a Mean Intersection over Union (MIOU) of 83.47% and accuracy of 95.28% on the dataset.
  • Demonstrated significant improvements of 3.3% in MIOU and 8.09% in accuracy over traditional models.
  • Showcased superior performance compared to watershed, Chan-Vese, and graph cut algorithms in terms of accuracy and calculation time.

Conclusions:

  • The proposed attention-based ENet method offers a significant advancement in tooth CT image segmentation.
  • The method provides higher accuracy and efficiency than existing segmentation algorithms.
  • This automated approach addresses the growing demand for rapid and precise medical image analysis.