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Author Notes:

bfei@emory.edu; web: https://fei-lab.org.

Subject:

Research Funding:

This research is supported in part by NIH grants (CA176684, CA156775, and CA204254) and by the National Cancer Institute (NCI) via NRG Oncology, a member of the NCI National Clinical Trials Network with Federal funds from the Department of Health and Human Services under Grant Number U10 CA37422.

Keywords:

  • Science & Technology
  • Technology
  • Physical Sciences
  • Engineering, Biomedical
  • Optics
  • Engineering
  • Cardiac imaging
  • Image segmentation
  • Deep Learning
  • Whole heart segmentation
  • CT imaging
  • Convolutional neural networks
  • Heart chamber segmentation
  • DISEASE
  • MODELS
  • ATLAS

Heart Chamber Segmentation from CT Using Convolutional Neural Networks

Tools:

Journal Title:

Proceedings of SPIE

Volume:

Volume 10578

Publisher:

Type of Work:

Article | Post-print: After Peer Review

Abstract:

CT is routinely used for radiotherapy planning with organs and regions of interest being segmented for diagnostic evaluation and parameter optimization. For cardiac segmentation, many methods have been proposed for left ventricular segmentation, but few for simultaneous segmentation of the entire heart. In this work, we present a convolutional neural networks (CNN)-based cardiac chamber segmentation method for 3D CT with 5 classes: left ventricle, right ventricle, left atrium, right atrium, and background. We achieved an overall accuracy of 87.2% ± 3.3% and an overall chamber accuracy of 85.6 ± 6.1%. The deep learning based segmentation method may provide an automatic tool for cardiac segmentation on CT images.

Copyright information:

© 2018 SPIE.

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