Evidence map›Paper›PMID 31085465›Full record

ArticleNeuroImage. Clinical2019

Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging.

Paul Schmidt, Viola Pongratz, Pascal Küster, Dominik Meier, Jens Wuerfel, Carsten Lukas, Barbara Bellenberg, Frauke Zipp, Sergiu Groppa, Philipp G Sämann and 8 more

Open access · goldAbstract read
In one paragraph

Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 2 pooled it
7.8field-weighted citation impact, top 2% 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

65 citing papers in PubMed, 2 syntheses or guidelines pooled it, 115 citations in OpenAlex.

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  6. Brain atrophy rates vary with age in relapsing-remitting multiple sclerosis.Journal of neurology, neurosurgery, and psychiatry · 2026
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5 more citing papers are in PubMed but not listed here.

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

18 authors at 10 institutions in 2 countries.

Paul SchmidtNeurology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany; TUM-Neuroimaging Center, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany.
Viola PongratzNeurology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany; TUM-Neuroimaging Center, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany.
Pascal KüsterMedical Image Analysis Center, MIAC AG, Mittlere Strasse 83, CH-4031 Basel, Switzerland; Biomedical Engineering, University Basel, Switzerland.
Dominik MeierMedical Image Analysis Center, MIAC AG, Mittlere Strasse 83, CH-4031 Basel, Switzerland.
Jens WuerfelMedical Image Analysis Center, MIAC AG, Mittlere Strasse 83, CH-4031 Basel, Switzerland; Biomedical Engineering, University Basel, Switzerland.
Carsten LukasDiagnostic and Interventional Radiology, St. Josef Hospital, Ruhr-University of Bochum, Gudrunstr. 56, 44791 Bochum, Germany.
Barbara BellenbergDiagnostic and Interventional Radiology, St. Josef Hospital, Ruhr-University of Bochum, Gudrunstr. 56, 44791 Bochum, Germany.
Frauke ZippNeurology, University Medical Centre of the Johannes Gutenberg University Mainz and Neuroimaging Center of the Focus Program Translational Neuroscience (FTN-NIC), Langenbeckstr. 1, 55131 Mainz, Germany.
Sergiu GroppaNeurology, University Medical Centre of the Johannes Gutenberg University Mainz and Neuroimaging Center of the Focus Program Translational Neuroscience (FTN-NIC), Langenbeckstr. 1, 55131 Mainz, Germany.
Philipp G SämannMax Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany.
Frank WeberMax Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany; Neurology, Sana Kliniken des Landkreises Cham, August-Holz-Straße 1, 93413 Cham, Germany.
Christian GaserDepartment of Psychiatry and Department of Neurology, Jena University Hospital, Jena, Germany.
Thomas FrankeMedical Informatics, University Medical Center Göttingen, Germany.
Matthias BussasNeurology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany; TUM-Neuroimaging Center, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany.
Jan KirschkeNeuroradiology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany.
Claus ZimmerNeuroradiology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany.
Bernhard HemmerNeurology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Feodor-Lynen-Str. 17, 81377 Munich, Germany.
Mark MühlauNeurology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany; TUM-Neuroimaging Center, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany. Electronic address: mark.muehlau@tum.de.
Technical University of Munich · DEJohannes Gutenberg University Mainz · DESt. Josef-Hospital · DEUniversity of Basel · CHJena University Hospital · DEKliniken des Landkreises Lörrach · DEMax Planck Institute of Psychiatry · DEMettler-Toledo (Switzerland) · CHMunich Cluster for Systems Neurology · DEUniversitätsmedizin Göttingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Longitudinal analysis of white matter lesion changes on serial MRI has become an important parameter to study diseases with white-matter lesions. Here, we build on earlier work on cross-sectional lesion segmentation; we present a fully automatic pipeline for serial analysis of FLAIR-hyperintense white matter lesions. Our algorithm requires three-dimensional gradient echo T1- and FLAIR- weighted images at 3 Tesla as well as available cross-sectional lesion segmentations of both time points. Preprocessing steps include lesion filling and intrasubject registration. For segmentation of lesion changes, initial lesion maps of different time points are fused; herein changes in intensity are analyzed at the voxel level. Significance of lesion change is estimated by comparison with the difference distribution of FLAIR intensities within normal appearing white matter. The method is validated on MRI data of two time points from 40 subjects with multiple sclerosis derived from two different scanners (20 subjects per scanner). Manual segmentation of lesion increases served as gold standard. Across all lesion increases, voxel-wise Dice coefficient (0.7) as well as lesion-wise detection rate (0.8) and false-discovery rate (0.2) indicate good overall performance. Analysis of scans from a repositioning experiment in a single patient with multiple sclerosis did not yield a single false positive lesion. We also introduce the lesion change plot as a descriptive tool for the lesion change of individual patients with regard to both number and volume. An open source implementation of the algorithm is available at http://www.statistical-modeling.de/lst.html.

Indexed as

AdultCohort StudiesCross-Sectional StudiesFemaleFollow-Up StudiesHumansLongitudinal StudiesMagnetic Resonance ImagingMaleMiddle AgedMultiple SclerosisYoung AdultLesion segmentationMagnetic resonance imagingMultiple sclerosisWhite matter lesions

Identifiers

PMID31085465
PMCPMC6517532
OpenAlexW2943358380

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.