ArticleScientific reports2022
Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.Neuroinformatics · 2025Pooled it
- Performance of deep learning models for automatic histopathological grading of meningiomas: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- Detection and Early Detection of Pathological Motor Evoked Potentials: A Novel Approach for Intraoperative Neuromonitoring Assistance.Journal of healthcare informatics research · 2026Article
- Automated deep learning-based segmentation and volumetric analysis of meningiomas.Neuroradiology · 2026Article
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- An advanced automated pipeline for brain tumour segmentation on magnetic resonance imaging for gamma knife radiosurgery.Physics and imaging in radiation oncology · 2026Article
- RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset.PLoS computational biology · 2025Article
- Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation.Frontiers in oncology · 2025Article
- Machine learning-based reconstruction of 2D MRI for quantitative morphometry in epilepsy.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Brainchop: Providing an Edge Ecosystem for Deployment of Neuroimaging Artificial Intelligence Models.Aperture neuro · 2024Article
- Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets.Journal of medical Internet research · 2023Article
- Lightweight image steganalysis with block-wise pruning.Scientific reports · 2023Article
- Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence pipeline predicts the integrated molecular-morphologic risk score of meningiomas from routine preoperative MRI.Neuro-oncology advancesArticle
- A deep learning algorithm for fully automated volumetric measurement of meningioma burden.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
Abstract
Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional convolutional neural network (3D-CNN) that performs expert-level, automated meningioma segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the network was then specifically trained on meningioma segmentation using 806 expert-labeled MRIs. The final model achieved a median performance of 88.2% reaching the spectrum of current inter-expert variability (82.6-91.6%). We demonstrate in a simulated clinical scenario that a deep learning approach to meningioma segmentation is feasible, highly accurate and has the potential to improve current clinical practice.
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Registered trials
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.