Automated deep learning pipeline for callosal angle quantification.

ABSTRACT:

Background

Normal pressure hydrocephalus (NPH) is a potentially treatable neurodegenerative disorder that remains underdiagnosed due to its clinical overlap with other conditions and the labor-intensive nature of manual imaging analyses. Imaging biomarkers, such as the callosal angle (CA), Evans Index (EI), and Disproportionately Enlarged Subarachnoid Space Hydrocephalus (DESH), play a crucial role in NPH diagnosis but are often limited by subjective interpretations. To address these challenges, we developed a fully automated and robust deep learning framework for measuring the CA directly from raw T1 MPRAGE scans.

Methods

Our method integrates two complementary modules. First, a BrainSignsNET model is employed to accurately detect key anatomical landmarks, notably the anter

SUBMITTER: Shirzadeh Barough S 

PROVIDER: S-EPMC12853608 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature