ArticleFrontiers in radiology2021
Meningioma Segmentation in T1-Weighted MRI Leveraging Global Context and Attention Mechanisms.
Article in Frontiers in radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.Neuroinformatics · 2025Pooled it
- Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- Article
- Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation.Frontiers in oncology · 2025Article
- A semi-automatic segmentation method for meningioma developed using a variational approach model.The neuroradiology journal · 2024Article
- Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.BMC medical imaging · 2024Article
- Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network.Dento maxillo facial radiology · 2024Article
- Interpolation-split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance.Journal of big data · 2024Article
- M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks.Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference) · 2024Article
- AeroPath: An airway segmentation benchmark dataset with challenging pathology and baseline method.PloS one · 2024Article
- Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.Scientific reports · 2023Article
- Predictive IDH Genotyping Based on the Evaluation of Spatial Metabolic Heterogeneity by Compartmental Uptake Characteristics in Preoperative Glioma UsingJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting.Scientific reports · 2023Article
- H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images.Frontiers in medicine · 2022Article
- Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting.Frontiers in neurology · 2022Article
- Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task.Cancers · 2021Article
- Article
- 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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What OpenQuestion holds
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.