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dc.contributor.authorCervera Gontard, Lionel 
dc.contributor.authorPizarro Junquera, Joaquín 
dc.contributor.authorSanz Peña, Borja
dc.contributor.authorLubián López, Simón Pedro 
dc.contributor.authorBenavente Fernández, Isabel 
dc.contributor.otherFísica de la Materia Condensadaes_ES
dc.contributor.otherIngeniería Informáticaes_ES
dc.contributor.otherMaterno-Infantil y Radiologíaes_ES
dc.date.accessioned2021-04-29T12:45:33Z
dc.date.available2021-04-29T12:45:33Z
dc.date.issued2021-01
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10498/24765
dc.description.abstractTo train, evaluate, and validate the application of a deep learning framework in three-dimensional ultrasound (3D US) for the automatic segmentation of ventricular volume in preterm infants with post haemorrhagic ventricular dilatation (PHVD). We trained a 2D convolutional neural network (CNN) for automatic segmentation ventricular volume from 3D US of preterm infants with PHVD. The method was validated with the Dice similarity coefficient (DSC) and the intra-class coefficient (ICC) compared to manual segmentation. The mean birth weight of the included patients was 1233.1 g (SD 309.4) and mean gestational age was 28.1 weeks (SD 1.6). A total of 152 serial 3D US from 10 preterm infants with PHVD were analysed. 230 ventricles were manually segmented. Of these, 108 were used for training a 2D CNN and 122 for validating the methodology for automatic segmentation. The global agreement for manual versus automated measures in the validation data (n=122) was excellent with an ICC of 0.944 (0.874-0.971). The Dice similarity coefficient was 0.8 (+/- 0.01). 3D US based ventricular volume estimation through an automatic segmentation software developed through deep learning improves the accuracy and reduces the processing time needed for manual segmentation using VOCAL. 3D US should be considered a promising tool to help deepen our current understanding of the complex evolution of PHVD.es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherNATURE RESEARCHes_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceScientific Reports (2021) 11:567es_ES
dc.titleAutomatic segmentation of ventricular volume by 3D ultrasonography in post haemorrhagic ventricular dilatation among preterm infantses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1038/s41598-020-80783-3


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Atribución 4.0 Internacional
This work is under a Creative Commons License Atribución 4.0 Internacional