Using Deep Learning to Predict Brain Aging from MRI Scans
Eshaa Gogia, an alum of the Data Science MS program at Rutgers–Camden, and Iman Dehzangi, assistant professor in the Department of Computer Science, coauthored a new study with Arash Dehzangi. Their article, “Automated Subregional Hippocampus Segmentation Using 3D CNNs: A Computational Framework for Brain Aging Biomarker Analysis,” was published in Algorithms as part of a special issue on machine learning in healthcare. The study presents a deep learning framework for segmenting the hippocampus from MRI scans—a promising tool for estimating brain age and identifying early signs of neurodegeneration.
The model distinguishes between anterior and posterior regions of the hippocampus using the self-configuring nnU-Net deep learning framework. It was applied to high-resolution MRI data from adolescents to produce accurate volumetric analysis—offering a scalable tool for studying brain development and identifying atypical patterns earlier than traditional methods.
Read the full piece to see the kind of research being produced by our faculty and alumni.
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