Evidence map›Paper›PMID 26408861›Full record

ArticleNeuroImage2016

Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease.

Zhen Yang, Chuyang Ye, John A Bogovic, Aaron Carass, Bruno M Jedynak, Sarah H Ying, Jerry L Prince

Open access · greenAbstract read
In one paragraph

Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 12% of its field
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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.

  1. Pooled it
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  15. Hierarchical Parcellation of the Cerebellum.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2019
    Article
  16. Cerebellum Parcellation with Convolutional Neural Networks.Proceedings of SPIE--the International Society for Optical Engineering · 2019
    Article
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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

7 authors at 3 institutions in 2 countries.

Zhen YangDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA. Electronic address: zyang11@jhu.edu.
Chuyang YeBrainnetome Center and National Laboratory of Pattern Recognition Institute of Automation, The Chinese Academy of Sciences, Beijing 100190, China.
John A BogovicJanelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
Aaron CarassDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
Bruno M JedynakDepartment of Applied Math and Statistics, The Johns Hopkins University, Baltimore, MD 21218, USA.
Sarah H YingDepartment of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
Jerry L PrinceDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Applied Math and Statistics, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
Johns Hopkins University · USChinese Academy of Sciences · CNJanelia Research Campus · US

Funding

Automatic Cerebellar MRI Labeling In Health and DiseaseR01NS056307 · NINDS · JOHNS HOPKINS UNIVERSITY · PI PRINCE, JERRY L · 2006 to 2015
$3.0M
NINDS NIH HHS 5R01NS056307-08NINDS NIH HHS R01 NS056307
6 · The paper itself

Abstract

The cerebellum plays an important role in both motor control and cognitive function. Cerebellar function is topographically organized and diseases that affect specific parts of the cerebellum are associated with specific patterns of symptoms. Accordingly, delineation and quantification of cerebellar sub-regions from magnetic resonance images are important in the study of cerebellar atrophy and associated functional losses. This paper describes an automated cerebellar lobule segmentation method based on a graph cut segmentation framework. Results from multi-atlas labeling and tissue classification contribute to the region terms in the graph cut energy function and boundary classification contributes to the boundary term in the energy function. A cerebellar parcellation is achieved by minimizing the energy function using the α-expansion technique. The proposed method was evaluated using a leave-one-out cross-validation on 15 subjects including both healthy controls and patients with cerebellar diseases. Based on reported Dice coefficients, the proposed method outperforms two state-of-the-art methods. The proposed method was then applied to 77 subjects to study the region-specific cerebellar structural differences in three spinocerebellar ataxia (SCA) genetic subtypes. Quantitative analysis of the lobule volumes shows distinct patterns of volume changes associated with different SCA subtypes consistent with known patterns of atrophy in these genetic subtypes.

Indexed as

CerebellumFemaleHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedPattern Recognition, AutomatedSpinocerebellar AtaxiasCerebellar lobule segmentationCerebellumGraph cutsMagnetic resonance imagingMulti-atlas labelingRandom forest classifierSpinocerebellar ataxia

Identifiers

PMID26408861
PMCPMC4755820
OpenAlexW2119800915

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.