ArticleAnnals of neurosciences2023
Reproducibility and Reliability of Computing Models in Segmentation and Volumetric Measurement of Brain.
Article in Annals of neurosciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Traumatic Brain Injury and Alzheimer's Disease Biomarkers: A Systematic Review of Findings from Amyloid and Tau Positron Emission Tomography.Journal of neurotrauma · 2025Pooled it
- The Impact and Reliability of Tissue Segmentation on In Vivo Magnetic Resonance Spectroscopy Metabolite Quantification.Magnetic resonance in medicine · 2026Article
- Scan-rescan reliability assessment of brain volumetric analysis across scanners and software solutions.Scientific reports · 2025Article
- Analysis of Factors Affecting Quality in Structural Magnetic Resonance Images.Human brain mapping · 2025Article
- Morphometric Evidence of a U-Shaped Relationship Between Loss Aversion and Posterior Insular/Somatosensory Cortical Features.Human brain mapping · 2025Article
- Loss of control eating in children is associated with altered cortical and subcortical brain structure.Frontiers in psychology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Background: Segmentation and morphometric measurement of brain tissue and regions from non-invasive magnetic resonance images have clinical and research applications. Several software tools and models have been developed by different research groups which are increasingly used for segmentation and morphometric measurements. Variability in results has been observed in the imaging data processed with different neuroimaging pipelines which have increased the focus on standardization. Purpose: The availability of several tools and models for brain morphometry poses challenges as an analysis done on the same set of data using different sets of tools and pipelines may result in different results and interpretations and there is a need for understanding the reliability and accuracy of such models. Methods: T1-weighted (T1-w) brain volumes from the publicly available OASIS3 dataset have been analysed using recent versions of FreeSurfer, FSL-FAST, CAT12, and ANTs pipelines. grey matter (GM), white matter (WM), and estimated total intracranial volume (eTIV) have been extracted and compared for inter-method variability and accuracy. Results: All four methods are consistent and strongly reproducible in their measurement across subjects however there is a significant degree of variability between these methods. Conclusion: CAT12 and FreeSurfer methods have the highest degree of agreement in tissue class segmentation and are most reproducible compared to others.
Indexed as
Identifiers
What OpenQuestion holds
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.