ArticleFrontiers in neuroinformatics2014
Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline.
Article in Frontiers in neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
What it found
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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.
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Who cites it
80 citing papers in PubMed, 115 citations in OpenAlex.
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- A global multicohort study to map subcortical brain development and cognition in infancy and early childhood.Nature neuroscience · 2024Article
- Maternal Perinatal Stress Trajectories and Negative Affect and Amygdala Development in Offspring.The American journal of psychiatry · 2023Article
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- The role of puberty on physical and brain development: A longitudinal study in male Rhesus Macaques.Developmental cognitive neuroscience · 2023Article
- Effects of social rank and pubertal delay on brain structure in female rhesus macaques.Psychoneuroendocrinology · 2023Article
- DBSegment: Fast and robust segmentation of deep brain structures considering domain generalization.Human brain mapping · 2023Article
- High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning.NeuroImage · 2022Review
20 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 at 3 institutions in 1 country.
Funding
Abstract
Automated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has shown its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple atlases alongside label fusion techniques has been introduced using a set of individual "atlases" that encompasses the expected variability in the studied population. In our study, we proposed a multi-atlas segmentation scheme with a novel graph-based atlas selection technique. We first paired and co-registered all atlases and the subject MR scans. A directed graph with edge weights based on intensity and shape similarity between all MR scans is then computed. The set of neighboring templates is selected via clustering of the graph. Finally, weighted majority voting is employed to create the final segmentation over the selected atlases. This multi-atlas segmentation scheme is used to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline employing BatchMake for its pipeline scripting, developed at the Neuro Image Research and Analysis Laboratories of the University of North Carolina at Chapel Hill. AutoSeg performs N4 intensity inhomogeneity correction, rigid registration to a common template space, automated brain tissue classification based skull-stripping, and the multi-atlas segmentation. The multi-atlas-based AutoSeg has been evaluated on subcortical structure segmentation with a testing dataset of 20 adult brain MRI scans and 15 atlas MRI scans. The AutoSeg achieved mean Dice coefficients of 81.73% for the subcortical structures.
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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.