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Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging

Current Cardiology Reports volume 22, Article number: 65 (2020) Cite this article

Abstract

Purpose of Review

Myocardial fibrosis (MF) arises due to myocardial infarction and numerous cardiac diseases. MF may lead to several heart disorders, such as heart failure, arrhythmias, and ischemia. Cardiac magnetic resonance (CMR) imaging techniques, such as late gadolinium enhancement (LGE) CMR, enable non-invasive assessment of MF in the left ventricle (LV). Manual assessment of MF on CMR is a tedious and time-consuming task that is subject to high observer variability. Automated segmentation and quantification of MF is important for risk stratification and treatment planning in patients with heart disorders. This article aims to review the machine learning (ML)-based methodologies developed for MF quantification in the LV using CMR images.

Recent Findings

With the availability of relatively large labeled datasets supervised learning methods based on both conventional ML and state-of-the-art deep learning (DL) methods have been successfully applied for automated segmentation of MF. The incorporation of ML algorithms into imaging techniques such as 3D LGE CMR permits fast characterization of MF on CMR imaging and may enhance the diagnosis and prognosis of patients with heart disorders. Concurrently, the studies using cine CMR images have revealed that accurate segmentation of MF on non-contrast CMR imaging might be possible.

Summary

The application of ML/DL tools in CMR image interpretation is likely to result in accurate and efficient quantification of MF.

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Acknowledgments

F. Zabihollahy acknowledges the Ontario Graduate Scholarship (OGS).

Funding

This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant (E. Ukwatta).

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Affiliations

  1. Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada

    Fatemeh Zabihollahy & S. Rajan

  2. School of Engineering, University of Guelph, Guelph, ON, Canada

    E. Ukwatta

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  1. Fatemeh Zabihollahy
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Correspondence to Fatemeh Zabihollahy.

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F. Zabihollahy, S. Rajan, and E. Ukwatta declare that they have no conflict of interest.

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Zabihollahy, F., Rajan, S. & Ukwatta, E. Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging. Curr Cardiol Rep 22, 65 (2020). https://doi.org/10.1007/s11886-020-01321-1

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Keywords

  • Myocardial fibrosis
  • Cardiac magnetic resonance imaging
  • Late gadolinium enhancement
  • Machine learning
  • Deep learning