CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation.

ABSTRACT: Quantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene silencing trial has recently started, there is an urgent need for quantitative, sensitive imaging markers at pre-symptomatic stages for stratification and treatment assessment. This work introduces CerebNet, a fully automated, extensively validated, deep learning method for the lobular segmentation of the cerebellum, including the separation of gray and white matter. For training, validation, and testing, T1-weighted images from 30 participants were manually annotated into cerebellar lobules and vermal sub-segments, as well as cerebellar white m

SUBMITTER: Faber J 

PROVIDER: S-EPMC9771831 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature