ArticleNeuroImage. Clinical2013
A comprehensive approach to the segmentation of multichannel three-dimensional MR brain images in multiple sclerosis.
Article in NeuroImage. Clinical, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning.Magnetic resonance imaging · 2020Trial
- BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation.Human brain mapping · 2023Article
- Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2021Article
- Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.BMJ open · 2021Article
- Deep-Learning-Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size.Journal of magnetic resonance imaging : JMRI · 2020Article
- Automated Detection and Segmentation of Multiple Sclerosis Lesions Using Ultra-High-Field MP2RAGE.Investigative radiology · 2019Article
- Longitudinal analysis of white matter and cortical lesions in multiple sclerosis.NeuroImage. Clinical · 2019Article
- Partial volume-aware assessment of multiple sclerosis lesions.NeuroImage. Clinical · 2018Article
- Dual-Sensitivity Multiple Sclerosis Lesion and CSF Segmentation for Multichannel 3T Brain MRI.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2018Article
- GRAPE: a graphical pipeline environment for image analysis in adaptive magnetic resonance imaging.International journal of computer assisted radiology and surgery · 2017Article
- Multimodal MEMPRAGE, FLAIR, and [Formula: see text] Segmentation to Resolve Dura and Vessels from Cortical Gray Matter.Frontiers in neuroscience · 2017Article
- Contrast enhancement by combining T1- and T2-weighted structural brain MR Images.Magnetic resonance in medicine · 2015Article
- A toolbox for multiple sclerosis lesion segmentation.Neuroradiology · 2015Article
- White matter lesion filling improves the accuracy of cortical thickness measurements in multiple sclerosis patients: a longitudinal study.BMC neuroscience · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Accurate classification and quantification of brain tissues is important for monitoring disease progression, measurement of atrophy, and correlating magnetic resonance (MR) measures with clinical disability. Classification of MR brain images in the presence of lesions, such as multiple sclerosis (MS), is particularly challenging. Images obtained with lower resolution often suffer from partial volume averaging leading to false classifications. While partial volume averaging can be reduced by acquiring volumetric images at high resolution, image segmentation and quantification can be technically challenging. In this study, we integrated the brain anatomical knowledge with non-parametric and parametric statistical classifiers for automatically classifying tissues and lesions on high resolution multichannel three-dimensional images acquired on 60 MS brains. The results of automatic lesion segmentation were reviewed by the expert. The agreement between results obtained by the automated analysis and the expert was excellent as assessed by the quantitative metrics, low absolute volume difference percent (36.18 ± 34.90), low average symmetric surface distance (1.64 mm ± 1.30 mm), high true positive rate (84.75 ± 12.69), and low false positive rate (34.10 ± 16.00). The segmented results were also in close agreement with the corrected results as assessed by Bland-Altman and regression analyses. Finally, our lesion segmentation was validated using the MS lesion segmentation grand challenge dataset (MICCAI 2008).
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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.