Deep Neural Network for Cardiac Magnetic Resonance Image Segmentation

David Chen, Huzefa Bhopalwala, Nakeya Dewaswala, Shivaram P. Arunachalam, Moein Enayati, Nasibeh Zanjirani Farahani, Kalyan S Pasupathy, Sravani Lokineni, J. Martijn Bos, Peter A. Noseworthy, Reza Arsanjani, Bradley J Erickson, Jeffrey B. Geske, Michael John Ackerman, Philip A. Araoz, Adelaide M. Arruda-Olson

Research output: Contribution to journalArticlepeer-review

Abstract

The analysis and interpretation of cardiac magnetic resonance (CMR) images are often time-consuming. The automated segmentation of cardiac structures can reduce the time required for image analysis. Spatial similarities between different CMR image types were leveraged to jointly segment multiple sequences using a segmentation model termed a multi-image type UNet (MI-UNet). This model was developed from 72 exams (46% female, mean age 63 ± 11 years) performed on patients with hypertrophic cardiomyopathy. The MI-UNet for steady-state free precession (SSFP) images achieved a superior Dice similarity coefficient (DSC) of 0.92 ± 0.06 compared to 0.87 ± 0.08 for a single-image type UNet (p < 0.001). The MI-UNet for late gadolinium enhancement (LGE) images also had a superior DSC of 0.86 ± 0.11 compared to 0.78 ± 0.11 for a single-image type UNet (p = 0.001). The difference across image types was most evident for the left ventricular myocardium in SSFP images and for both the left ventricular cavity and the left ventricular myocardium in LGE images. For the right ventricle, there were no differences in DCS when comparing the MI-UNet with single-image type UNets. The joint segmentation of multiple image types increases segmentation accuracy for CMR images of the left ventricle compared to single-image models. In clinical practice, the MI-UNet model may expedite the analysis and interpretation of CMR images of multiple types.

Original languageEnglish (US)
Article number149
JournalJournal of Imaging
Volume8
Issue number5
DOIs
StatePublished - May 2022

Keywords

  • cardiac magnetic resonance imaging
  • deep learning
  • hypertrophic cardiomyopathy
  • image segmentation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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