Heart chamber segmentation from CT using convolutional neural networks

James D. Dormer, Ling Ma, Martin Halicek, Carolyn M. Reilly, Eduard Schreibmann, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor Gimi, Andrzej Krol
PublisherSPIE
ISBN (Electronic)9781510616455
DOIs
StatePublished - Jan 1 2018
Externally publishedYes
EventMedical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, United States
Duration: Feb 11 2018Feb 13 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10578
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CityHouston
Period2/11/182/13/18

Keywords

  • CT imaging
  • Cardiac imaging
  • Convolutional neural networks
  • Deep Learning
  • Heart chamber segmentation
  • Image segmentation
  • Whole heart segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Dormer, J. D., Ma, L., Halicek, M., Reilly, C. M., Schreibmann, E., & Fei, B. (2018). Heart chamber segmentation from CT using convolutional neural networks. In B. Gimi, & A. Krol (Eds.), Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging [105782S] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10578). SPIE. https://doi.org/10.1117/12.2293554