Brain segmentation from neonatal MRI images is a very challenging task due to large changes in the shape of cerebral structures and variations in signal intensities reflecting the gestational process. In this context, there is a clear need for segmentation techniques that are robust to variations in image contrast and to the spatial configuration of anatomical structures. In this work, we evaluate the potential of synthetic learning, a contrast-independent model trained using synthetic images generated from the ground truth labels of very few subjects.We base our experiments on the dataset released by the developmental Human Connectome Project, for which high-quality T1- and T2-weighted images are available for more than 700 babies aged between 26 and 45 weeks post-conception. First, we confirm the impressive performance of a standard Unet trained on a few T2-weighted volumes, but also confirm that such models learn intensity-related features specific to the training domain. We then evaluate the synthetic learning approach and confirm its robustness to variations in image contrast by reporting the capacity of such a model to segment both T1- and T2-weighted images from the same individuals. However, we observe a clear influence of the age of the baby on the predictions. We improve the performance of this model by enriching the synthetic training set with realistic motion artifacts and over-segmentation of the white matter. Based on extensive visual assessment, we argue that the better performance of the model trained on real T2w data may be due to systematic errors in the ground truth. We propose an original experiment combining two definitions of the ground truth allowing us to show that learning from real data will reproduce any systematic bias from the training set, while synthetic models can avoid this limitation. Overall, our experiments confirm that synthetic learning is an effective solution for segmenting neonatal brain MRI. Our adapted synthetic learning approach combines key features that will be instrumental for large multi-site studies and clinical applications.

翻译：从新生儿MRI图像中进行脑部分割是一项极具挑战性的任务，这主要源于脑部结构形态的巨大变化以及反映妊娠过程的信号强度差异。在此背景下，亟需对图像对比度变化和解剖结构空间配置具有鲁棒性的分割技术。本研究评估了合成学习的潜力——这是一种基于极少受试者真实标签生成的合成图像训练的对比度无关模型。我们基于发展性人类连接组项目发布的数据集开展实验，该数据集包含700余名孕后26至45周婴儿的高质量T1加权和T2加权图像。首先，我们确认了在少量T2加权体素上训练的标准Unet模型的卓越性能，但也证实此类模型会学习特定于训练领域的强度相关特征。随后，我们评估了合成学习方法，通过报告该模型对同一受试者T1加权和T2加权图像的分割能力，确认其对图像对比度变化的鲁棒性。然而，我们观察到婴儿年龄对预测结果存在显著影响。通过向合成训练集中添加真实的运动伪影和白质过度分割，我们改进了模型性能。基于广泛的视觉评估，我们认为在真实T2加权数据上训练的模型性能更优，可能是由于真实标签存在系统性误差。我们设计了一项创新实验，结合两种真实标签定义，证明从真实数据学习会复制训练集中的任何系统性偏差，而合成模型可避免这一局限。总体而言，我们的实验证实合成学习是分割新生儿脑MRI的有效方案。改进后的合成学习方法融合了关键特征，将有助于开展大规模多中心研究和临床应用。