• Publications
  • Influence
Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge
  • L. Wang, D. Nie, +26 authors D. Shen
  • Computer Science, Medicine
  • IEEE Transactions on Medical Imaging
  • 27 February 2019
TLDR
We review the eight top-ranked teams, in terms of Dice ratio, modified Hausdorff distance, and average surface distance and introduce their pipelines, implementations, as well as source codes. Expand
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Discriminative confidence estimation for probabilistic multi‐atlas label fusion
TLDR
We propose a probabilistic label fusion approach for multi‐atlas segmentation based on atlas label confidences computed at each voxel of the structure of interest. Expand
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Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation
TLDR
The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images using sparse representation and discriminative dictionary learning . Expand
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Learning non‐linear patch embeddings with neural networks for label fusion
TLDR
We use neural networks to learn optimal embeddings of image patches using neural networks so as to improve discriminative abilities of patch‐based label fusion. Expand
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BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets
TLDR
We present a compact workflow and open-access toolbox that allows for (i) the identification of gradients (from structural or functional imaging data), (ii) their alignment (across subjects or modalities), and (iii) their visualization (in embedding or cortical space). Expand
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BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets
TLDR
We present BrainSpace, a Python/Matlab toolbox for (i) the identification of gradients, (ii) their alignment, and (iii) their visualization. Expand
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Building an Ensemble of Complementary Segmentation Methods by Exploiting Probabilistic Estimates
TLDR
We propose an ensemble method that combines the complementary features of intensity-based modelling and multi-atlas label fusion for atlas-based segmentation of brain MRI. Expand
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Toward the automatic quantification of in utero brain development in 3D structural MRI: A review
TLDR
We present a review of the major building blocks of the pipeline toward performing quantitative analysis of in vivo MRI of the developing brain and its potential applications in clinical settings. Expand
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Discriminative Dimensionality Reduction for Patch-Based Label Fusion
TLDR
In this last decade, multiple-atlas segmentation MAS has emerged as a promising technique for medical image segmentation. Expand
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Early Prediction of Alzheimer's Disease with Non-local Patch-Based Longitudinal Descriptors
TLDR
We present a novel descriptor that uses the similarity between local image patches to encode local displacements due to atrophy between a pair of longitudinal MRI scans to predict which MCI patients will progress to AD up to 3 years before conversion. Expand
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