RT journal article
T1 MGA-Net: A novel mask-guided attention neural network for precision neonatal brain imaging
A1 Jafrasteh, Bahram
A1 Lubián López, Simón Pedro
A1 Trimarco, Emiliano
A1 Barrios, Carmen Rodríguez
A1 Román Ruiz, Macarena
A1 Rodríguez Barrios, Carmen
A1 Marín Almagro, Yolanda
A1 Benavente Fernández, Isabel
A2 Materno-Infantil y Radiología
A2 Psicología
K1 Brain volume estimation
K1 Deep learning
K1 Mask guided attention
K1 Multimodal image processing
K1 U-net architecture
AB In this study, we introduce MGA-Net, a novel mask-guided attention neural network, which extends the U-net model for precision neonatal brain imaging. MGA-Net is designed to extract the brain from other structures and reconstruct high-quality brain images. The network employs a common encoder and two decoders: one for brain mask extraction and the other for brain region reconstruction. A key feature of MGA-Net is its high-level mask-guided attention module, which leverages features from the brain mask decoder to enhance image reconstruction. To enable the same encoder and decoder to process both MRI and ultrasound (US) images, MGA-Net integrates sinusoidal positional encoding. This encoding assigns distinct positional values to MRI and US images, allowing the model to effectively learn from both modalities. Consequently, features learned from a single modality can aid in learning a modality with less available data, such as US. We extensively validated the proposed MGA-Net on diverse and independent datasets from varied clinical settings and neonatal age groups. The metrics used for assessment included the DICE similarity coefficient, recall, and accuracy for image segmentation; structural similarity for image reconstruction; and root mean squared error for total brain volume estimation from 3D ultrasound images. Our results demonstrate that MGA-Net significantly outperforms traditional methods, offering superior performance in brain extraction and segmentation while achieving high precision in image reconstruction and volumetric analysis. Thus, MGA-Net represents a robust and effective preprocessing tool for MRI and 3D ultrasound images, marking a significant advance in neuroimaging that enhances both research and clinical diagnostics in the neonatal period and beyond.
PB Elsevier
SN 1095-9572
YR 2024
FD 2024
LK http://hdl.handle.net/10498/35740
UL http://hdl.handle.net/10498/35740
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-nov-2026