Automated Subregional Hippocampus Segmentation Using 3D CNNs: A Computational Framework for Brain Aging Biomarker Analysis

The hippocampus is a critical brain structure involved in episodic memory, spatial orientation, and stress regulation. Its volumetric shrinkage is among the earliest and most reliable indicators of both physiological brain aging and pathological neurodegeneration. Accurate segmentation and measurement of the hippocampal subregions from magnetic resonance imaging (MRI) is therefore essential for neurobiological age estimation and the early identification of at-risk individuals. In this study, we present a fully automated pipeline that leverages nnU-Net, a self-configuring deep learning framework, to segment the hippocampus from high-resolution 3D T1-weighted brain MRI scans. The primary objective of this work is to enable accurate estimation of brain age through quantitative analysis of hippocampal volume. By fusing domain knowledge in neuroanatomy with data-driven learning through a highly expressive and self-optimizing model, this work advances the methodological frontier for neuroimaging-based brain-age estimation. The proposed approach demonstrates that deep learning can serve as a reliable segmentation tool as well as a foundational layer in predictive neuroscience, supporting early detection of accelerated aging and subclinical neurodegenerative processes.