Rewiring Development in Brain Segmentation: Leveraging Adult Brain Priors for Enhancing Infant MRI Segmentation

2025-10-10

AI Summary

Segmenting infant brain MRI is highly challenging due to dynamic anatomical development, motion artifacts, and severe scarcity of high-quality annotated data. To address these issues, we propose LODi—a novel framework that, for the first time, leverages a pre-trained adult brain segmentation model as a structural prior and enables cross-age modeling via transfer learning and domain adaptation. LODi introduces a hierarchical feature refinement module and multi-level consistency constraints to achieve age-adaptive, robust weakly supervised segmentation using FreeSurfer-derived silver-standard labels. Evaluated on multiple internal and external infant MRI datasets, LODi consistently outperforms conventional supervised methods and state-of-the-art domain-specific approaches, demonstrating superior generalizability and robustness against motion artifacts. Our work establishes a scalable, cross-domain segmentation paradigm for lifelong brain imaging analysis.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Enhancing infant MRI segmentation using adult brain priors
• Addressing anatomical variability in infant brain scans
• Mitigating data scarcity through transfer learning strategies

Innovation

• Utilizes adult brain MRI priors for infant segmentation
• Employs transfer learning and domain adaptation strategies
• Integrates hierarchical feature refinement and consistency constraints