Automatic Measurement of the Volume of Brain Ventricles in Preterm Infants from 3D Ultrasound Datasets

Gontard, Lionel C.; Pizarro, J.; Benavente‐Fernández, Isabel; Lubián‐López, Simón Pedro

doi:10.1007/978-3-030-32040-9_34

Cited by 1 publication

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References 12 publications

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“…We developed a dedicated software tool for accelerating the labelling step of the ventricles by doing a search of an optimum intensity threshold. The algorithm was previously described by our group 14 and consists of three steps: (1) the 3D US image is preprocessed for noise reduction using a median 3D filter; (2) the filtered dataset is segmented for darker intensities (low density in the brain associated with the fluid in the ventricles) using a global threshold calculated iteratively over the histogram of the 3D volume. In each iteration segmented blobs are classified according to geometrical parameters.…”

Section: Segmentation Of Ventricles Using Deep Learning For the Automentioning

confidence: 99%

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

Gontard

Pizarro

Sanz-Peña

et al. 2021

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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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Section: Segmentation Of Ventricles Using Deep Learning For the Automentioning

confidence: 99%

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

Gontard

Pizarro

Sanz-Peña

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

Automatic Measurement of the Volume of Brain Ventricles in Preterm Infants from 3D Ultrasound Datasets

Cited by 1 publication

References 12 publications

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

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

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