Accurate segmentation of infant brain MRI is critical for studying early neurodevelopment and diagnosing neurological disorders. Yet, it remains a fundamental challenge due to continuously evolving anatomy of the subjects, motion artifacts, and the scarcity of high-quality labeled data. In this work, we present LODi, a novel framework that utilizes prior knowledge from an adult brain MRI segmentation model to enhance the segmentation performance of infant scans. Given the abundance of publicly available adult brain MRI data, we pre-train a segmentation model on a large adult dataset as a starting point. Through transfer learning and domain adaptation strategies, we progressively adapt the model to the 0-2 year-old population, enabling it to account for the anatomical and imaging variability typical of infant scans. The adaptation of the adult model is carried out using weakly supervised learning on infant brain scans, leveraging silver-standard ground truth labels obtained with FreeSurfer. By introducing a novel training strategy that integrates hierarchical feature refinement and multi-level consistency constraints, our method enables fast, accurate, age-adaptive segmentation, while mitigating scanner and site-specific biases. Extensive experiments on both internal and external datasets demonstrate the superiority of our approach over traditional supervised learning and domain-specific models. Our findings highlight the advantage of leveraging adult brain priors as a foundation for age-flexible neuroimaging analysis, paving the way for more reliable and generalizable brain MRI segmentation across the lifespan.

翻译：婴儿脑部MRI的精确分割对于研究早期神经发育和诊断神经系统疾病至关重要。然而，由于受试者解剖结构持续演变、运动伪影以及高质量标注数据的稀缺，这仍然是一项根本性挑战。在本工作中，我们提出了LODi，一种新颖的框架，利用来自成人脑部MRI分割模型的先验知识来提升婴儿扫描图像的分割性能。鉴于公开可用的成人脑部MRI数据资源丰富，我们首先在大型成人数据集上预训练一个分割模型作为起点。通过迁移学习和领域适应策略，我们逐步将该模型适配至0-2岁人群，使其能够适应婴儿扫描中典型的解剖结构和成像变异性。成人模型的适配过程采用对婴儿脑部扫描的弱监督学习实现，利用了通过FreeSurfer获得的银标准真实标签。通过引入一种整合了层次化特征精炼与多级一致性约束的新颖训练策略，我们的方法能够实现快速、准确、年龄自适应的分割，同时减轻扫描仪和站点特异性偏差。在内部和外部数据集上进行的大量实验表明，我们的方法优于传统的监督学习和特定领域模型。我们的研究结果凸显了利用成人脑先验作为年龄适应性神经影像分析基础的优势，为在整个生命周期内实现更可靠、更可泛化的脑部MRI分割铺平了道路。