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Automatic segmentation of ventricular volume by 3D ultrasonography in post haemorrhagic ventricular dilatation among preterm infants

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URI: http://hdl.handle.net/10498/24765

DOI: 10.1038/s41598-020-80783-3

ISSN: 2045-2322

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2021_242.pdf (2.370Mb)
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Author/s
Cervera Gontard, LionelAuthority UCA; Pizarro Junquera, JoaquínAuthority UCA; Sanz Peña, Borja; Lubián López, Simón PedroAuthority UCA; Benavente Fernández, IsabelAuthority UCA
Date
2021-01
Department
Física de la Materia Condensada; Ingeniería Informática; Materno-Infantil y Radiología
Source
Scientific Reports (2021) 11:567
Abstract
To 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.
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