ArticleNeuroinformatics2014
Software pipeline for midsagittal corpus callosum thickness profile processing : automated segmentation, manual editor, thickness profile generator, group-wise statistical comparison and results display.
Article in Neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Age-Dependent Corpus Callosum Thickness Abnormalities and Clinical Implications in Treatment-Naïve First-Episode Schizophrenia.Alpha psychiatry · 2026Article
- Hand preference and the corpus callosum: Is there really no association?Neuroimage. Reports · 2023Article
- Spectral-based thickness profiling of the corpus callosum enhances anomaly detection in fetal alcohol spectrum disorders.Frontiers in neuroscience · 2023Article
- Midsagittal corpus callosal thickness and cognitive impairment in Parkinson's disease.The European journal of neuroscience · 2022Article
- Morphometric mapping of the macrostructural abnormalities of midsagittal corpus callosum in Wilson's disease.Annals of movement disorders · 2021Article
- A Deformation-Based Shape Study of the Corpus Callosum in First Episode Schizophrenia.Frontiers in psychiatry · 2021Article
- Callosal thickness profiles for prognosticating conversion from mild cognitive impairment to Alzheimer's disease: A classification approach.Brain and behavior · 2018Article
- Caffeine for apnea of prematurity and brain development at 11 years of age.Annals of clinical and translational neurology · 2018Article
- Automatic Extraction of the Centerline of Corpus Callosum from Segmented Mid-Sagittal MR Images.Computational and mathematical methods in medicine · 2018Article
- Callosal circularity as an early marker for Alzheimer's disease.NeuroImage. Clinical · 2018Article
- Reliability of measuring regional callosal atrophy in neurodegenerative diseases.NeuroImage. Clinical · 2016Article
- The relationship between regional microstructural abnormalities of the corpus callosum and physical and cognitive disability in relapsing-remitting multiple sclerosis.NeuroImage. Clinical · 2015Article
- FastSurfer-CC: A robust, accurate, and comprehensive framework for corpus callosum morphometry.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a fully automated pipeline for thickness profile evaluation and analysis of the human corpus callosum (CC) in 3D structural T 1-weighted magnetic resonance images. The pipeline performs the following sequence of steps: midsagittal plane extraction, CC segmentation algorithm, quality control tool, thickness profile generation, statistical analysis and results figure generator. The CC segmentation algorithm is a novel technique that is based on a template-based initialisation with refinement using mathematical morphology operations. The algorithm is demonstrated to have high segmentation accuracy when compared to manual segmentations on two large, publicly available datasets. Additionally, the resultant thickness profiles generated from the automated segmentations are shown to be highly correlated to those generated from the ground truth segmentations. The manual editing tool provides a user-friendly environment for correction of errors and quality control. Statistical analysis and a novel figure generator are provided to facilitate group-wise morphological analysis of the CC.
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24968872What OpenQuestion holds
Registered trials
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