RT journal article
T1 A deep sift convolutional neural networks for total brain volume estimation from 3D ultrasound images
A1 Jafrasteh, Bahram
A1 Lubián López, Simón
A1 Benavente Fernández, Isabel
A2 Materno-Infantil y Radiología
K1 Computer
K1 Neural Networks
K1 Longitudinal Studies
K1 Premature
K1 Infant
K1 Infant, Newborn
K1 Three-Dimensional
K1 Imaging
K1 Computer-Assisted
K1 Image Processing
K1 Humans
K1 Brain
AB Preterm infants are a highly vulnerable population. The total brain volume (TBV) of these infants can be accurately estimated by brain ultrasound (US) imaging which enables a longitudinal study of early brain growth during Neonatal Intensive Care (NICU) admission. Automatic estimation of TBV from 3D images increases the diagnosis speed and evades the necessity for an expert to manually segment 3D images, which is a sophisticated and time consuming task. We develop a deep-learning approach to estimate TBV from 3D ultrasound images. It benefits from deep convolutional neural networks (CNN) with dilated residual connections and an additional layer, inspired by the fuzzy c-Means (FCM), to further separate the features into different regions, i.e. sift layer. Therefore, we call this method deep-sift convolutional neural networks (DSCNN). The proposed method is validated against three state-of-the-art methods including AlexNet-3D, ResNet-3D, and VGG-3D, for TBV estimation using two datasets acquired from two different ultrasound devices. The results highlight a strong correlation between the predictions and the observed TBV values. The regression activation maps are used to interpret DSCNN, allowing TBV estimation by exploring those pixels that are more consistent and plausible from an anatomical standpoint. Therefore, it can be used for direct estimation of TBV from 3D images without needing further image segmentation.
PB Elsevier
SN 1872-7565
YR 2023
FD 2023
LK http://hdl.handle.net/10498/31577
UL http://hdl.handle.net/10498/31577
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-nov-2026