Computed tomography-based nnU-Net for region-specific brain structural changes across the alzheimer's continuum and frontotemporal dementia subtypes.

ABSTRACT: Quantifying structural brain changes is critical for diagnosing and monitoring neurodegenerative diseases. Although magnetic resonance imaging (MRI) is the silver standard, limited accessibility and cost hamper routine use. We developed a deep learning-based framework using the nnU-Net for brain segmentation using computed tomography (CT) to assess cerebrospinal fluid (CSF) volume changes, as an indirect marker of tissue loss, and evaluated its utility across Alzheimer's disease (AD) stages and frontotemporal dementia (FTD) subtypes. We included 2357 participants: cognitively unimpaired (CU, n = 595), mild cognitive impairment (MCI, n = 954), dementia of Alzheimer's type (DAT, n = 663), and FTD subtypes (FTD, n = 145, behavioral variant FTD (bvFTD, n = 66), nonfluent variant primary progre

SUBMITTER: Park S 

PROVIDER: S-EPMC12663231 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature