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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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COVID-19 CT image segmentation based on improved Res2Net.

Shangwang Liu1,2, Xiufang Tang1, Tongbo Cai1

  • 1School of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China.

Medical Physics
|August 2, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new segmentation network for COVID-19 detection in CT scans. The method accurately identifies infected regions, improving upon existing models and aiding in rapid screening of coronavirus disease 2019 (COVID-19).

Keywords:
COVID-19 CT image segmentationRes2Netcontext explorationedge attention

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Artificial Intelligence in Healthcare

Background:

  • Coronavirus disease 2019 (COVID-19) poses a significant global health and economic threat.
  • Computed tomography (CT) image segmentation is crucial for identifying COVID-19 infected areas.
  • Accurate segmentation aids in the screening and management of confirmed COVID-19 cases.

Purpose of the Study:

  • To design and evaluate a novel segmentation network for precise identification of COVID-19 infected regions in CT images.
  • To improve the accuracy of COVID-19 segmentation compared to existing methods.
  • To develop a tool that assists clinicians in screening and managing COVID-19 patients.

Main Methods:

  • A segmentation network utilizing Res2Net for multilayered feature extraction.
  • Incorporation of an edge attention module to extract low-level edge features.
  • Development of an attention position module (APM) for high-level feature extraction and region detection.
  • Implementation of a context exploration module to minimize false positives and negatives.

Main Results:

  • The proposed method achieved a Dice similarity coefficient of 0.755, sensitivity of 0.751, and specificity of 0.959.
  • Compared to Inf-Net, the Dice similarity coefficient increased by 7.3% and sensitivity by 5.9%.
  • The mean absolute error (MAE) was reduced by 2.2%.

Conclusions:

  • The developed method demonstrates strong performance in segmenting COVID-19 infected regions on CT images.
  • The network's portability allows integration with various popular deep learning architectures.
  • This approach offers an effective tool for screening COVID-19, potentially reducing clinician workload.