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

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalMagnetic Resonance Materials in Physics, Biology and Medicine
Publication statusAccepted/In press - Jan 1 2018


  • CMR-LGE images
  • Deep learning
  • Fully-convolutional neural networks
  • Scar segmentation

ASJC Scopus subject areas

  • Biophysics
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging


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