DeepParcellation: a novel deep learning method for robust brain magnetic resonance imaging parcellation in older East Asians

Abstract

Accurate parcellation of cortical regions is crucial for distinguishing morphometric changes in aged brains, particularly in degenerative brain diseases. Normal aging and neurodegeneration precipitate brain structural changes, leading to distinct tissue contrast and shape in people aged > 60 years. Manual parcellation by trained radiologists can yield a highly accurate outline of the brain; however, analyzing large datasets is laborious and expensive. Alternatively, newly-developed computational models can quickly and accurately conduct brain parcellation, although thus far only for the brains of Caucasian individuals. DeepParcellation, our novel deep learning model for 3D magnetic resonance imaging (MRI) parcellation, was trained on 5,035 brains of older East Asians (Gwangju Alzheimers & Related Dementia) and 2,535 brains of Caucasians. We trained full 3D models for N-way individual regions of interest using memory reduction techniques. Our method showed the highest similarity and robust reliability among age-ethnicity groups, especially when parcellating the brains of older East Asians.