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Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed

Ted Shi1, Maysam Shahedi1,2, Kayla Caughlin1

  • 1Department of Bioengineering, The University of Texas at Dallas, Richardson, TX.

Proceedings of Spie--The International Society for Optical Engineering
|February 16, 2023
PubMed
Summary

This study introduces a semi-automated deep learning method for segmenting cardiac chambers in CT scans. The point-guided approach improves accuracy and efficiency, offering a promising alternative to manual segmentation for cardiovascular disease analysis.

Keywords:
Heartcardiovascular diseasescomputed tomography (CT)deep learningimage segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiology

Background:

  • Cardiovascular diseases (CVDs) necessitate accurate heart segmentation in cardiac CT scans.
  • Manual segmentation is laborious and prone to observer variability, impacting results.
  • Fully automated deep learning methods for cardiac segmentation still require improvement to match expert accuracy.

Purpose of the Study:

  • To develop a semi-automated deep learning approach for cardiac segmentation that balances accuracy and efficiency.
  • To bridge the gap between manual segmentation's precision and fully automated methods' speed.
  • To enhance the delineation of heart chambers in CT images.

Main Methods:

  • A semi-automated deep learning strategy using a fixed number of points on the cardiac surface was employed.
  • Points-distance maps were generated from user-selected points.
  • A three-dimensional (3D) fully convolutional neural network (FCNN) was trained on these maps for segmentation prediction.

Main Results:

  • The method achieved Dice scores ranging from 0.742 to 0.917 across the four heart chambers.
  • Average Dice scores were 0.846 ± 0.059 (left atrium), 0.857 ± 0.052 (left ventricle), 0.826 ± 0.062 (right atrium), and 0.824 ± 0.062 (right ventricle).
  • The point-guided, image-independent approach demonstrated strong performance in chamber-by-chamber segmentation.

Conclusions:

  • The developed semi-automated deep learning method shows promising performance for cardiac segmentation in CT images.
  • This approach offers a viable alternative to manual segmentation, improving efficiency without sacrificing significant accuracy.
  • The point-guided technique facilitates accurate delineation of individual heart chambers.