ArticleNeurocomputing2017
Concatenated Spatially-localized Random Forests for Hippocampus Labeling in Adult and Infant MR Brain Images.
Article in Neurocomputing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Neuroimage-Based Consciousness Evaluation of Patients with Secondary Doubtful Hydrocephalus Before and After Lumbar Drainage.Neuroscience bulletin · 2020Article
- Weighted Graph Regularized Sparse Brain Network Construction for MCI Identification.Pattern recognition · 2019Article
- Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages.Scientific reports · 2019Article
- Adaptive Bayesian label fusion using kernel-based similarity metrics in hippocampus segmentation.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2018Article
- Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation.Frontiers in neuroinformatics · 2018Article
- Learning-based structurally-guided construction of resting-state functional correlation tensors.Magnetic resonance imaging · 2017Article
- Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution.Pattern recognition · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold.
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Registered trials
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