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Magnetic Resonance Materials in Physics, Biology and Medicine

Published in:

01-04-2019 | Research Article

Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images

Authors: Sara Moccia, Riccardo Banali, Chiara Martini, Giuseppe Muscogiuri, Gianluca Pontone, Mauro Pepi, Enrico Gianluca Caiani

Published in: Magnetic Resonance Materials in Physics, Biology and Medicine | Issue 2/2019

Abstract

Objective

The aim of this paper is to investigate the use of fully convolutional neural networks (FCNNs) to segment scar tissue in the left ventricle from cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) images.

Methods

A successful FCNN in the literature (the ENet) was modified and trained to provide scar-tissue segmentation. Two segmentation protocols (Protocol 1 and Protocol 2) were investigated, the latter limiting the scar-segmentation search area to the left ventricular myocardial tissue region. CMR-LGE from 30 patients with ischemic-heart disease were retrospectively analyzed, for a total of 250 images, presenting high variability in terms of scar dimension and location. Segmentation results were assessed against manual scar-tissue tracing using one-patient-out cross validation.

Results

Protocol 2 outperformed Protocol 1 significantly (p value < 0.05), with median sensitivity and Dice similarity coefficient equal to 88.07% [inter-quartile range (IQR) 18.84%] and 71.25% (IQR 31.82%), respectively.

Discussion

Both segmentation protocols were able to detect scar tissues in the CMR-LGE images but higher performance was achieved when limiting the search area to the myocardial region. The findings of this paper represent an encouraging starting point for the use of FCNNs for the segmentation of nonviable scar tissue from CMR-LGE images.

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Title: Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images
Authors: Sara Moccia
Riccardo Banali
Chiara Martini
Giuseppe Muscogiuri
Gianluca Pontone
Mauro Pepi
Enrico Gianluca Caiani
Publication date: 01-04-2019
Publisher: Springer International Publishing
Published in: Magnetic Resonance Materials in Physics, Biology and Medicine / Issue 2/2019
Print ISSN: 0968-5243
Electronic ISSN: 1352-8661
DOI: https://doi.org/10.1007/s10334-018-0718-4

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images

Abstract

Objective

Methods

Results

Discussion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Objective

Methods

Results

Discussion

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