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Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL

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dc.contributor.author Chaves, Hernán
dc.contributor.author Dorr, Francisco
dc.contributor.author Costa, Martín Elías
dc.contributor.author Serra, María Mercedes
dc.contributor.author Fernández Slezak, Diego
dc.contributor.author Farez, Mauricio Franco
dc.contributor.author Sevlever, Gustavo Emilio
dc.contributor.author Yáñez, Paulina
dc.contributor.author Cejas, Claudia Patricia
dc.date.accessioned 2021-03-26T14:57:52Z
dc.date.available 2021-03-26T14:57:52Z
dc.date.issued 2020-10-01
dc.identifier.citation Chaves, H., Dorr, F., Costa, M.E., Serra, M.M., Slezak, D.F., Farez, M.F., Sevlever, G., Yañez, P., Cejas, C., 2020. Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL. J Neuroradiol. https://doi.org/10.1016/j.neurad.2020.10.001 es_ES
dc.identifier.uri https://repositorio.fleni.org.ar/xmlui/handle/123456789/393
dc.identifier.uri https://doi.org/10.1016/j.neurad.2020.10.001
dc.description.abstract Background and purpose: There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). Materials and methods: Sixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV). Results: Whole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94-0.97)) than FreeSurfer and CAT12 (0.92 (0.88-0.96)) and FSL (0.87 (0.79-0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20-3.13% vs. mean CV 1.05, range 0.21-3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49-5.91% vs. mean CV 3.84, range 2.62-5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively. Conclusion: Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice. es_ES
dc.language.iso eng es_ES
dc.publisher Masson es_ES
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/2.5/ar/
dc.subject Magnetic Resonance Imaging es_ES
dc.subject Imagen por Resonancia Magnética es_ES
dc.subject Deep Learning es_ES
dc.subject Aprendizaje Profundo es_ES
dc.title Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.type info:eu-repo/semantics/publishedVersion
dc.description.fil Fil: Chaves, Hernán. Fleni. Departamento de Diagnóstico por Imágenes; Argentina.
dc.description.fil Fil: Yáñez, Paulina. Fleni. Departamento de Diagnóstico por Imágenes; Argentina.
dc.description.fil Fil: Cejas, Claudia Patricia. Fleni. Departamento de Diagnóstico por Imágenes; Argentina.
dc.description.fil Fil: Dorr, Francisco. Entelai; Argentina.
dc.description.fil Fil: Costa, Martín Elías. Entelai; Argentina.
dc.description.fil Fil: Serra, María Mercedes. Fleni. Departamento de Diagnóstico por Imágenes; Argentina. Entelai; Argentina.
dc.description.fil Fil: Fernández Slezak, Diego. Entelai; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Instituto en Ciencias de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
dc.description.fil Fil: Sevlever, Gustavo Emilio. Fleni. Departamento de Neuropatología y Biología Molecular; Argentina.
dc.description.fil Fil: Farez, Mauricio Franco. Fleni. Departamento de Neurología; Argentina.
dc.relation.ispartofPAGINATION S0150-9861(20)30280-7
dc.relation.ispartofCOUNTRY Francia
dc.relation.ispartofCITY París
dc.relation.ispartofTITLE Journal of neuroradiology = Journal de neuroradiologie.
dc.relation.ispartofISSN 0150-9861
dc.type.snrd info:ar-repo/semantics/artículo es_ES


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