ArticleNeuroImage2022
FastSurferVINN: Building resolution-independence into deep learning segmentation methods-A solution for HighRes brain MRI.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Deep learning segmentation of the cerebral ventricular system and brainstem for pediatric radiotherapy planning.Physics and imaging in radiation oncology · 2026Article
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- Value of MRI Outcomes for Preventive and Early-Stage Trials in Spinocerebellar Ataxias 1 and 3.Annals of clinical and translational neurology · 2026Article
- NeuroLangSeg: Language-Guided Subcortical Segmentation with Pseudo-Supervision and Anatomical-Linguistic Validation.Proceedings of machine learning research · 2026Article
- Hyperelastic Regularization for Near-Diffeomorphic Transformer-Based Brain MRI Registration.Journal of imaging · 2026Article
- From Low Field to High Value: Robust Cortical Mapping From Low-Field MRI.Human brain mapping · 2026Article
- Regression Is All You Need for Medical Image Translation.IEEE transactions on medical imaging · 2026Article
- The Myokine Irisin Represents an Indirect Pathway Linking Exercise to Hippocampal Subfields Relevant to Alzheimer's Disease and Neurogenesis.Aging cell · 2026Article
- Article
- A Multi-parametric MRI and Machine Learning Study of Cerebellar Structure in Youth with Neurofibromatosis Type 1.Cerebellum (London, England) · 2026Article
- Multimodal connectivity-based cortical segmentation with graph neural networks.Frontiers in neuroscience · 2026Article
- Multimodal Connectivity-based Cortical Segmentation with Graph Neural Networks.bioRxiv : the preprint server for biology · 2025Article
- ENIGMA's advanced guide for parcellation error identification (EAGLE-I): An implementation in the context of brain lesions.MethodsX · 2025Article
- Comparative analysis of multiple deep learning models with mitigation-driven approaches for enhanced Alzheimer's disease classification.Scientific reports · 2025Article
- An unsupervised XAI framework for dementia detection with context enrichment.Scientific reports · 2025Article
- Comparison of brain volumetry in patients with non-lesional epilepsy on 3 and 7 T MPRAGE.Quantitative imaging in medicine and surgery · 2025Article
- A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans.Journal of Alzheimer's disease : JAD · 2025Article
- The Influence of Sex and Sex-Steroid Hormones on Cerebellar Structure and Functional Connectivity Across Adulthood.Human brain mapping · 2025Article
- RAVEN: Robust, generalizable, multi-resolution structural MRI upsampling using Autoencoders.bioRxiv : the preprint server for biology · 2025Article
- "Recon-all-clinical": Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI.Medical image analysis · 2025Article
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3 authors.
Funding
Abstract
Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below 1.0 mm for improved structure definition and morphometry. Yet, only few, time-intensive automated image analysis pipelines have been validated for high-resolution (HiRes) settings. Efficient deep learning approaches, on the other hand, rarely support more than one fixed resolution (usually 1.0 mm). Furthermore, the lack of a standard submillimeter resolution as well as limited availability of diverse HiRes data with sufficient coverage of scanner, age, diseases, or genetic variance poses additional, unsolved challenges for training HiRes networks. Incorporating resolution-independence into deep learning-based segmentation, i.e., the ability to segment images at their native resolution across a range of different voxel sizes, promises to overcome these challenges, yet no such approach currently exists. We now fill this gap by introducing a Voxel-size Independent Neural Network (VINN) for resolution-independent segmentation tasks and present FastSurferVINN, which (i) establishes and implements resolution-independence for deep learning as the first method simultaneously supporting 0.7-1.0 mm whole brain segmentation, (ii) significantly outperforms state-of-the-art methods across resolutions, and (iii) mitigates the data imbalance problem present in HiRes datasets. Overall, internal resolution-independence mutually benefits both HiRes and 1.0 mm MRI segmentation. With our rigorously validated FastSurferVINN we distribute a rapid tool for morphometric neuroimage analysis. The VINN architecture, furthermore, represents an efficient resolution-independent segmentation method for wider application.
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