  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 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.
