Evidence map›Paper›PMID 40420000›Full record

ArticleBMC medical imaging2025

Auto-segmentation of cerebral cavernous malformations using a convolutional neural network.

Chi-Jen Chou, Huai-Che Yang, Cheng-Chia Lee, Zhi-Huan Jiang, Ching-Jen Chen, Hsiu-Mei Wu, Chun-Fu Lin, I-Chun Lai, Syu-Jyun Peng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2025. 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.

Chi-Jen ChouDivision of Neurosurgery, Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
Huai-Che YangSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Cheng-Chia LeeSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Zhi-Huan JiangDepartment of Electrical Engineering, National Central University, Taoyuan, Taiwan.
Ching-Jen ChenUniversity of Texas Health Science Center at Houston, Houston, TX, USA.
Hsiu-Mei WuSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Chun-Fu LinDepartment of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.
I-Chun LaiSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Syu-Jyun PengIn-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, No.250, Wuxing St., Xinyi Dist, Taipei, 110, Taiwan. sjpeng2019@tmu.edu.tw.

Funding

National Science and Technology Council NSTC 112-2628-E-038-001-MY3
6 · The paper itself

Abstract

backgroundThis paper presents a deep learning model for the automated segmentation of cerebral cavernous malformations (CCMs).

methodsThe model was trained using treatment planning data from 199 Gamma Knife (GK) exams, comprising 171 cases with a single CCM and 28 cases with multiple CCMs. The training data included initial MRI images with target CCM regions manually annotated by neurosurgeons. For the extraction of data related to the brain parenchyma, we employed a mask region-based convolutional neural network (Mask R-CNN). Subsequently, this data was processed using a 3D convolutional neural network known as DeepMedic.

resultsThe efficacy of the brain parenchyma extraction model was demonstrated via five-fold cross-validation, resulting in an average Dice similarity coefficient of 0.956 ± 0.002. The segmentation models used for CCMs achieved average Dice similarity coefficients of 0.741 ± 0.028 based solely on T2W images. The Dice similarity coefficients for the segmentation of CCMs types were as follows: Zabramski Classification type I (0.743), type II (0.742), and type III (0.740). We also developed a user-friendly graphical user interface to facilitate the use of these models in clinical analysis.

conclusionsThis paper presents a deep learning model for the automated segmentation of CCMs, demonstrating sufficient performance across various Zabramski classifications.

trial registrationnot applicable.

Indexed as

Convolutional Neural NetworksDeep LearningHemangioma, Cavernous, Central Nervous SystemMagnetic Resonance ImagingAdultFemaleHumansMaleMiddle AgedRadiosurgeryCerebral cavernous malformationsDeep learningDeepMedicGamma knife (GK) treatment planningMask region-based convolutional neural network

Identifiers

PMID40420000
PMCPMC12107882

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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.