Deep Learning for Cardiac Image Segmentation: A Review

Frontiers in Cardiovascular Medicine
Chen ChenDaniel Rueckert

Abstract

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria, and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.

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Sep 11, 2020·Current Cardiology Reports·Manoj MannilBettina Baessler
Oct 10, 2020·BMC Medical Imaging·Gerhard-Paul DillerGerman Competence Network for Congenital Heart Defects Investigators
Sep 25, 2020·Jiantao PuLei Wang

Related Concepts

Blood Vessel
Diagnostic Imaging
Heart
Heart Atrium
Learning
Magnetic Resonance Imaging
Research
Reversal Learning
X-Ray Computed Tomography
Ultrasonography

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