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

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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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Notes

  1. https://www.circlecvi.com/.

  2. https://www.tensorflow.org/.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, Universitá Politecnica delle Marche, Ancona, Italy

    Sara Moccia

  2. Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy

    Sara Moccia

  3. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

    Riccardo Banali & Enrico Gianluca Caiani

  4. Diagnostic Department, Azienda Ospedaliera-Universitaria di Parma, Parma, Italy

    Chiara Martini

  5. Clinical Cardiology Unit and Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy

    Giuseppe Muscogiuri, Gianluca Pontone & Mauro Pepi

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  1. Sara Moccia
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  2. Riccardo Banali
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  4. Giuseppe Muscogiuri
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  5. Gianluca Pontone
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  6. Mauro Pepi
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  7. Enrico Gianluca Caiani
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Contributions

SM: study conception and design/analysis and interpretation of data/drafting of manuscript/Critical revision. RB: acquisition of data/analysis and interpretation of data/critical revision. CM: acquisition of data/analysis and interpretation of data. GM: acquisition of data/analysis and interpretation of data/critical revision. GP: acquisition of data/analysis and interpretation of data/critical revision. MP: acquisition of data/analysis and interpretation of data/critical revision. EGC: study conception and design/analysis and interpretation of data/drafting of manuscript/critical revision.

Corresponding author

Correspondence to Enrico Gianluca Caiani.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.

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Informed consent was obtained from all individual participants included in the study.

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Moccia, S., Banali, R., Martini, C. et al. Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images. Magn Reson Mater Phy 32, 187–195 (2019). https://doi.org/10.1007/s10334-018-0718-4

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  • DOI: https://doi.org/10.1007/s10334-018-0718-4

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