Abstract
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.
Abstract (translated)
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不断变化、运动伪影以及高质量标注数据稀缺等因素，这一任务仍面临重大挑战。在这项工作中，我们提出了LODi框架，利用成人脑部MRI分割模型中的先验知识来提高婴儿扫描的分割性能。鉴于公开可用的成人脑部MRI数据丰富，我们首先在一个大型成人数据集上预训练了一个分割模型作为起点。通过迁移学习和领域适应策略，逐步将该模型调整适用于0至2岁儿童群体，使其能够应对婴儿扫描中常见的解剖学和成像变异。 我们将成年人模型应用到婴儿脑部扫描的过程中采用了弱监督学习，并利用FreeSurfer获得的银标准地面真实标签进行训练。通过引入一种结合层级特征精炼和多级一致性约束的新颖训练策略，我们的方法实现了快速、精确且适应年龄变化的分割效果，同时减少了特定扫描仪和地点带来的偏差。 在内部和外部数据集上的广泛实验表明，相较于传统的监督学习和领域特化模型，我们所提出的方法表现更优。我们的发现强调了利用成人脑部先验知识作为跨年龄段灵活神经影像分析基础的重要性，并为整个生命周期内更加可靠且通用的脑部MRI分割铺平了道路。
URL
https://arxiv.org/abs/2510.09306
