ArticleNeuroImage2022
CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.
- Cerebellar gray matter volume changes in patients with schizophrenia: A voxel-based meta-analysis.Frontiers in psychiatry · 2022Pooled it
- Lobular and Voxel-Wise Genetic Influences on Cerebellar Gray Matter Volume and Associations With Social and Trait Anxiety in Twins.Human brain mapping · 2026Article
- Dual Dopaminergic and Limbic-Cognitive Contributions to Gait Parameters in De Novo Parkinson Disease.Neurology · 2026Article
- Advancing Cerebro-cerebellar Network Imaging with Ultra-high Field (7T) MRI: Progress and Future Challenges.Cerebellum (London, England) · 2026Review
- Multiscale brain development across the human lifespan.Communications biology · 2026Review
- Early and Progressive Spinal Cord Atrophy in Spinocerebellar Ataxia Type 1.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Value of MRI Outcomes for Preventive and Early-Stage Trials in Spinocerebellar Ataxias 1 and 3.Annals of clinical and translational neurology · 2026Article
- Brainstem and Cerebellar Volume Loss and Associated Clinical Features in Progressive Supranuclear Palsy.Annals of clinical and translational neurology · 2026Observational
- The relationship between cerebellar functional connectivity alterations and hand dexterity impairment in multiple sclerosis.Journal of neurology · 2026Article
- A Multi-Subject Tractography Atlas of Human Cerebellar Connections.Research square · 2026Article
- Quantifying cerebellar signal detectability in MEG and EEG in epilepsy using anatomically informed source modeling.NeuroImage · 2026Article
- Article
- Explainable machine learning identifies candidate shared neuroanatomical features in Alzheimer's and Parkinson's via importance inversion transfer.bioRxiv : the preprint server for biology · 2026Article
- White matter changes in reward circuits of amyotrophic lateral sclerosis: a fixel-based study of appetite loss.BMC medicine · 2026Article
- Genotype and Age at Onset Drive Vermis Atrophy in CACNA1A- and GAA-FGF14-related Ataxias.Cerebellum (London, England) · 2026Article
- Early-Life Clinical Factors Shape Cerebellar Structure in Preterm-Born Children.Annals of the Child Neurology Society · 2026Article
- A Multi-parametric MRI and Machine Learning Study of Cerebellar Structure in Youth with Neurofibromatosis Type 1.Cerebellum (London, England) · 2026Article
- Quantifying Cerebellar Signal Detectability in MEG and EEG in Epilepsy Using Anatomically Informed Source Modeling.bioRxiv : the preprint server for biology · 2026Article
- Cerebellar subregional structural changes across the Alzheimer's disease continuum: a longitudinal analysis of cognitive and behavioural correlates.Brain communications · 2026Article
- Brain atrophy staging in spinocerebellar ataxia type 3 for clinical prognosis and trial enrichment.EBioMedicine · 2026Article
Corrections and comments
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Authors and funding
17 authors at 13 institutions in 5 countries.
Funding
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 matter. CerebNet combines FastSurferCNN, a UNet-based 2.5D segmentation network, with extensive data augmentation, e.g. realistic non-linear deformations to increase the anatomical variety, eliminating additional preprocessing steps, such as spatial normalization or bias field correction. CerebNet demonstrates a high accuracy (on average 0.87 Dice and 1.742mm Robust Hausdorff Distance across all structures) outperforming state-of-the-art approaches. Furthermore, it shows high test-retest reliability (average ICC >0.97 on OASIS and Kirby) as well as high sensitivity to disease effects, including the pre-ataxic stage of spinocerebellar ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer and FastSurfer and can analyze a 3D volume within seconds on a consumer GPU in an end-to-end fashion, thus providing an efficient and validated solution for assessing cerebellum sub-structure volumes. We make CerebNet available as source-code (https://github.com/Deep-MI/FastSurfer).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.