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.

翻译：本研究提出了一种新型掩码引导注意力神经网络MGA-Net，该网络扩展了U-Net模型以实现精准的新生儿脑成像。MGA-Net旨在从其他结构中提取脑部并重建高质量的脑部图像。该网络采用一个共享编码器和两个解码器：一个用于脑部掩码提取，另一个用于脑区重建。MGA-Net的核心特点是其高层掩码引导注意力模块，该模块利用脑部掩码解码器的特征来增强图像重建质量。为使同一编码器-解码器架构能够同时处理MRI和超声（US）图像，MGA-Net集成了正弦位置编码技术。该编码方式为MRI和US图像分配不同的位置特征值，使模型能够有效学习两种模态的数据。因此，从单一模态学习到的特征可辅助数据量较少的模态（如超声）的学习过程。我们在来自不同临床环境和新生儿年龄段的多样化数据集上对MGA-Net进行了全面验证。评估指标包括：图像分割的DICE相似系数、召回率与准确率；图像重建的结构相似性；以及三维超声图像全脑体积估计的均方根误差。实验结果表明，MGA-Net在脑部提取和分割任务上显著优于传统方法，同时在图像重建和体积分析方面实现了高精度。因此，MGA-Net为MRI和三维超声图像提供了鲁棒高效的预处理工具，标志着神经影像学领域的重大进展，对新生儿期及后续阶段的科研与临床诊断具有重要促进作用。