Evidence map›Paper›PMID 41792040›Full record

SynthesisEuropean stroke journal2026

Deep learning-based automated segmentation of intracerebral haemorrhage, intraventricular haemorrhage and perihaematomal oedema on non-contrast CT.

Floor N H Wilting, Jules P J Douwes, Ajay Patel, Floris H B M Schreuder, Ruben Dammers, Gerjon Hannink, Wilmar M T Jolink, Sjoert A H Pegge, Lotte Sondag, Marieke J H Wermer and 3 more

Abstract readMeta-Analysis
In one paragraph

Synthesis in European stroke journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Floor N H WiltingDepartment of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID 0000-0002-1964-1962
Jules P J DouwesDepartment of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0002-8232-576X
Ajay PatelDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Floris H B M SchreuderDepartment of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID 0000-0002-7815-0207
Ruben DammersDepartment of Neurosurgery, Erasmus University Medical Center, Erasmus MC Stroke Center, Center for Complex Microvascular Surgery, Rotterdam, The Netherlands.ORCID 0000-0001-7033-0644
Gerjon HanninkDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID 0000-0001-9526-3775
Wilmar M T JolinkDepartment of Neurology, Isala Hospital, Zwolle, The Netherlands.
Sjoert A H PeggeDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID 0000-0001-9314-6137
Lotte SondagDepartment of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID 0000-0002-2037-7484
Marieke J H WermerDepartment of Neurology, University Medical Center Groningen, Groningen, The Netherlands.ORCID 0000-0002-6838-9521
H Bart van der WorpDepartment of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0001-9891-2136
Frederick J A MeijerDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID 0000-0001-5921-639X
Catharina J M KlijnDepartment of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID 0000-0002-8495-4578

Funding

Collaboration for New Treatments of Acute Stroke (CONTRAST)Dutch Heart Foundation 2012 T077Dutch Heart Foundation 2016 T086Netherlands Cardiovascular Research InitiativeNetherlands Organization for Health Research and Development 015.008.048
6 · The paper itself

Abstract

introductionPrecise volumetric evaluation of intracerebral haemorrhage (ICH), intraventricular haemorrhage (IVH) and perihaematomal oedema (PHO) is essential but manual segmentation is time-consuming and susceptible to variability. We aimed to develop and externally validate a deep learning model for simultaneous segmentation of ICH, IVH and PHO on non-contrast CT (NCCT) in patients with spontaneous ICH. PATIENTS AND

methodsA 3D U-net model was trained with 5-fold cross-validation on baseline NCCTs from 301 patients included in 2 prospective multicentre studies. External validation was performed on 141 baseline NCCTs from another multicentre study. Model performance was evaluated against manual ground truth segmentations using the Dice similarity coefficient (DSC), intraclass correlation coefficients (ICC) and Bland-Altman analyses.

resultsThe model achieved a median DSC of 0.93 (IQR 0.91-0.94) for ICH, 0.75 (IQR 0.57-0.82) for IVH and 0.53 (IQR 0.34-0.65) for PHO. Volume correlations were excellent for ICH (mean absolute and consistency ICC both 0.98 [95% CI 0.98-0.99]) and IVH (absolute ICC 0.97 [95% CI 0.92-0.98]; consistency ICC 0.98 [95% CI 0.96-0.99]), and moderate for PHO (absolute ICC 0.60 [95% CI -0.08-0.85]; consistency ICC 0.82 [95% CI 0.76-0.87]). Bland-Altman analyses demonstrated a bias for ICH of -0.48 mL (LoA -8.21 to 7.26), for IVH of -1.68 mL (LoA -7.35 to 3.99) and for PHO of 13.91 mL (LoA -4.85 to 32.68). DISCUSSION AND

conclusionThe model enables accurate automated segmentation of ICH, while IVH and PHO segmentation remain more challenging. Automated segmentations may already serve as reliable pre-segmentations in research, but require visual assessment and correction, in particular for IVH and PHO.

Indexed as

Brain EdemaCerebral HemorrhageCerebral Intraventricular HemorrhageDeep LearningTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedObservational Studies as TopicProspective StudiesRandomized Controlled Trials as Topicartificial intelligenceautomated segmentationdeep learningintracerebral haemorrhageintraventricular haemorrhagenon-contrast computed tomographyperihaematomal oedema

Identifiers

PMID41792040
PMCPMC12965810

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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.