Evidence map›Paper›PMID 41559506›Full record

ArticleJournal of imaging informatics in medicine2026

Comparison of Three Automated Measurements of Ventricular Volumes of the Brain.

Xue Zhang, Yonggang Li, Ping Mu, Xiaotong Yu, Xiu Zhang, Shulin Liu, Baishi Wang, Ning Li, Fu Ren

Abstract read
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In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Xue ZhangDepartment of Human Anatomy, Shenyang Medical College, Shenyang, Liaoning, 110034, People's Republic of China.
Yonggang LiLiaoning Province Key Laboratory for Phenomics of Human Ethnic Specificity and Critical Illness (LPKL-PHESCI), Shenyang, Liaoning, 110034, People's Republic of China.
Ping MuLiaoning Province Key Laboratory for Phenomics of Human Ethnic Specificity and Critical Illness (LPKL-PHESCI), Shenyang, Liaoning, 110034, People's Republic of China.
Xiaotong YuDepartment of Human Anatomy, Shenyang Medical College, Shenyang, Liaoning, 110034, People's Republic of China.
Xiu ZhangLiaoning Province Key Laboratory for Phenomics of Human Ethnic Specificity and Critical Illness (LPKL-PHESCI), Shenyang, Liaoning, 110034, People's Republic of China.
Shulin LiuHarbin Medical University, Daqing, Heilongjiang, 163711, People's Republic of China.
Baishi WangLiaoning Province Key Laboratory for Phenomics of Human Ethnic Specificity and Critical Illness (LPKL-PHESCI), Shenyang, Liaoning, 110034, People's Republic of China.
Ning LiLiaoning Province Key Laboratory for Phenomics of Human Ethnic Specificity and Critical Illness (LPKL-PHESCI), Shenyang, Liaoning, 110034, People's Republic of China.
Fu RenDepartment of Human Anatomy, Shenyang Medical College, Shenyang, Liaoning, 110034, People's Republic of China. rf@symc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate segmentation and measurement of ventricular volume are critical for neuroscience research and neurological disease diagnosis. In resource-limited settings, free and open-source automated tools offer accessible solutions. However, the lack of comparative evaluations limits their application. This study aims to identify reliable free tools for automated ventricular volume measurement to support clinical and research utilization. Magnetic resonance imaging (MRI) data from 150 healthy adults were collected with informed consent. Ventricular volumes were segmented using three open-source tools (3D Slicer, FreeSurfer, ITK-SNAP) and compared with manual segmentation as the reference standard. Pearson's correlation coefficient, intraclass correlation coefficient (ICC), and the Bland-Altman analysis were employed to evaluate consistency and reliability. All three automated tools showed significant correlations with manual measurements (P < 0.01). ITK-SNAP had the highest Pearson correlation and ICC values, followed by 3D Slicer, while FreeSurfer had the lowest. All tools demonstrated strong reliability, with ICCs greater than 0.9. The Bland-Altman analysis showed that ITK-SNAP had the closest consistency with manual results, again followed by 3D Slicer, with FreeSurfer performing least consistently. ITK-SNAP demonstrates higher accuracy and reliability for ventricular volumetry compared to 3D Slicer and FreeSurfer. Its open-source nature supports broader implementation in resource-constrained environments, enhancing neuroimaging accessibility for clinical and research applications.

Indexed as

Automated segmentationMethod validationNeuroimagingOpen-source softwareVentricular volumetry

Identifiers

PMID41559506

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Registered trials

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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.