Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net

Christopher T. Tran, Martin Halicek, James D. Dormer, Animesh Tandon, Mohammad T Hussain, Baowei Fei

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

Abstract

Cardiac magnetic resonance (CMR) imaging is considered the standard imaging modality for volumetric analysis of the right ventricle (RV), an especially important practice in the evaluation of heart structure and function in patients with repaired Tetralogy of Fallot (rTOF). In clinical practice, however, this requires time-consuming manual delineation of the RV endocardium in multiple 2-dimensional (2D) slices at multiple phases of the cardiac cycle. In this work, we employed a U-Net based 2D convolutional neural network (CNN) classifier in the fully automatic segmentation of the RV blood pool. Our dataset was comprised of 5,729 short-axis cine CMR slices taken from 100 individuals with rTOF. Training of our CNN model was performed on images from 50 individuals while validation was performed on images from 10 individuals. Segmentation results were evaluated by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Use of the CNN model on our testing group of 40 individuals yielded a median DSC of 90% and a median 95th percentile HD of 5.1 mm, demonstrating good performance in these metrics when compared to literature results. Our preliminary results suggest that our deep learning-based method can be effective in automating RV segmentation.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsAndrzej Krol, Barjor S. Gimi
PublisherSPIE
ISBN (Electronic)9781510634015
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, United States
Duration: Feb 18 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11317
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Country/TerritoryUnited States
CityHouston
Period2/18/202/20/20

Keywords

  • Cardiac magnetic resonance imaging
  • Convolutional neural network (CNN)
  • Deep learning
  • Heart
  • Image segmentation
  • Left ventricle
  • Tetralogy of Fallot

ASJC Scopus subject areas

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

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