ArticleNeuroImage2019
3D whole brain segmentation using spatially localized atlas network tiles.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 167 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
167 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Volumetric segmentation in the context of posterior fossa-related pathologies: a systematic review.Neurosurgical review · 2024Pooled it
- Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.Biomedical engineering online · 2023Pooled it
- A Randomized Trial of Shunting for Idiopathic Normal-Pressure Hydrocephalus.The New England journal of medicine · 2025Trial
- Bile acid metabolites predict multiple sclerosis progression and supplementation is safe in progressive disease.Med (New York, N.Y.) · 2025Trial
- Ultrasound-mediated blood-brain barrier opening uncovers an intracerebral perivenous fluid network in persons with Alzheimer's disease.Fluids and barriers of the CNS · 2023Trial
- Threshold Effects, Cognitive Decline, and Longitudinal Changes in White Matter Hyperintensity Volume.Stroke · 2026Article
- Beyond the LUMIR challenge: The pathway to foundational registration models.Medical image analysis · 2026Article
- Alzheimer's Disease Selectively Perturbs Age-Sensitive Brain Radiomic Features Across the Disease Continuum.bioRxiv : the preprint server for biology · 2026Article
- Fast surface reconstruction of human brain MRI: benchmarking deep-learning based morphometry tools.Scientific reports · 2026Article
- Hippocampal Total, Subregion, and Subfield Volume Alterations in Late-Life Depression.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- Informed Dictionary-Guided Monte Carlo Inversion for Robust and Reproducible Multidimensional MRI.Magnetic resonance in medicine · 2026Article
- Memory Consolidation and ARIA in Individuals Receiving Anti-amyloid Monoclonal Antibodies.The journal of prevention of Alzheimer's disease · 2026Article
- Association of Changes in Activity Patterns With Brain Atrophy and Disability Progression in People With Multiple Sclerosis.Neurology · 2026Article
- Article
- ECLARE: efficient cross-planar learning for anisotropic resolution enhancement.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- A 3D feature fusion model integrating multi-scale MRI feature for interpretable Glioblastoma prediction.Medical & biological engineering & computing · 2026Article
- Unsupervised learning of spatially varying regularization for diffeomorphic image registration.Medical image analysis · 2026Article
- Characterizing Continuous and Discrete Hybrid Latent Spaces for Structural Connectomes.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- CNN-based detection of pediatric lymphoma on whole body [American journal of nuclear medicine and molecular imaging · 2026Article
- EVALUATION OF NEUROCOMBAT AND DEEP LEARNING HARMONIZATION FOR MULTI-SITE MAGNETIC RESONANCE NEUROIMAGING IN YOUTH WITH PRENATAL ALCOHOL EXPOSURE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
107 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Detailed whole brain segmentation is an essential quantitative technique in medical image analysis, which provides a non-invasive way of measuring brain regions from a clinical acquired structural magnetic resonance imaging (MRI). Recently, deep convolution neural network (CNN) has been applied to whole brain segmentation. However, restricted by current GPU memory, 2D based methods, downsampling based 3D CNN methods, and patch-based high-resolution 3D CNN methods have been the de facto standard solutions. 3D patch-based high resolution methods typically yield superior performance among CNN approaches on detailed whole brain segmentation (>100 labels), however, whose performance are still commonly inferior compared with state-of-the-art multi-atlas segmentation methods (MAS) due to the following challenges: (1) a single network is typically used to learn both spatial and contextual information for the patches, (2) limited manually traced whole brain volumes are available (typically less than 50) for training a network. In this work, we propose the spatially localized atlas network tiles (SLANT) method to distribute multiple independent 3D fully convolutional networks (FCN) for high-resolution whole brain segmentation. To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location. To address the second challenge, auxiliary labels on 5111 initially unlabeled scans were created by multi-atlas segmentation for training. Since the method integrated multiple traditional medical image processing methods with deep learning, we developed a containerized pipeline to deploy the end-to-end solution. From the results, the proposed method achieved superior performance compared with multi-atlas segmentation methods, while reducing the computational time from >30 h to 15 min. The method has been made available in open source (https://github.com/MASILab/SLANTbrainSeg).
Indexed as
Identifiers
What OpenQuestion holds
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.