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dc.contributor.author
Curiale, Ariel Hernán
dc.contributor.author
Colavecchia, Flavio Dario
dc.contributor.author
Mato, German
dc.date.available
2020-03-18T14:18:26Z
dc.date.issued
2019-02
dc.identifier.citation
Curiale, Ariel Hernán; Colavecchia, Flavio Dario; Mato, German; Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI; Elsevier; Computer Methods And Programs In Biomedicine; 169; 2-2019; 37-50
dc.identifier.issn
0169-2607
dc.identifier.uri
http://hdl.handle.net/11336/99988
dc.description.abstract
Objective: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN). Methods: The general framework consists of one CNN for detecting the LV, and another for tissue classification. Also, three new deep learning architectures were proposed for LV quantification. These new CNNs introduce the ideas of sparsity and depthwise separable convolution into the U-net architecture, as well as, a residual learning strategy level-to-level. To this end, we extend the classical U-net architecture and use the generalized Jaccard distance as optimization objective function. Results: The CNNs were trained and evaluated with 140 patients from two public cardiovascular magnetic resonance datasets (Sunnybrook and Cardiac Atlas Project) by using a 5-fold cross-validation strategy. Our results demonstrate a suitable accuracy for myocardial segmentation (∼ 0.9 Dice's coefficient), and a strong correlation with the most relevant physiological measures: 0.99 for end-diastolic and end-systolic volume, 0.97 for the left myocardial mass, 0.95 for the ejection fraction and 0.93 for the stroke volume and cardiac output. Conclusion: Our simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN to estimate different structural and functional features such as LV mass and EF which are commonly used for both diagnosis and treatment of different pathologies. Significance: This paper suggests a new approach for automatic LV quantification based on deep learning where errors are comparable to the inter- and intra-operator ranges for manual contouring.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
CONVOLUTIONAL NEURAL NETWORK
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DEEP LEARNING
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LEFT VENTRICLE QUANTIFICATION
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MYOCARDIAL SEGMENTATION
dc.subject.classification
Otras Ciencias de la Salud
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Ciencias de la Salud
dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
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Otras Ciencias Físicas
dc.subject.classification
Ciencias Físicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-10-15T17:54:02Z
dc.journal.volume
169
dc.journal.pagination
37-50
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Curiale, Ariel Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina
dc.description.fil
Fil: Colavecchia, Flavio Dario. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina
dc.description.fil
Fil: Mato, German. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina
dc.journal.title
Computer Methods And Programs In Biomedicine
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0169260718311696
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.cmpb.2018.12.002
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