ArticleFrontiers in computational neuroscience2019
Multi-Disease Segmentation of Gliomas and White Matter Hyperintensities in the BraTS Data Using a 3D Convolutional Neural Network.
Article in Frontiers in computational neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review.Frontiers in oncology · 2021Pooled it
- Clinical and MRI factors influencing interobserver delineation variability of brain metastases before stereotactic radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Article
- Brain Tumors, AI and Psychiatry: Predicting Tumor-Associated Psychiatric Syndromes with Machine Learning and Biomarkers.International journal of molecular sciences · 2025Review
- The University of California San Francisco Adult Longitudinal Post-Treatment Diffuse Glioma MRI Dataset.Radiology. Artificial intelligence · 2024Article
- Non-invasive prediction of overall survival time for glioblastoma multiforme patients based on multimodal MRI radiomics.International journal of imaging systems and technology · 2023Article
- External Validation and Retraining of DeepBleed: The First Open-Source 3D Deep Learning Network for the Segmentation of Spontaneous Intracerebral and Intraventricular Hemorrhage.Journal of clinical medicine · 2023Article
- 3D Capsule Networks for Brain Image Segmentation.AJNR. American journal of neuroradiology · 2023Article
- Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- Federated learning enables big data for rare cancer boundary detection.Nature communications · 2022Article
- Longitudinal Assessment of Posttreatment Diffuse Glioma Tissue Volumes with Three-dimensional Convolutional Neural Networks.Radiology. Artificial intelligence · 2022Article
- Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm.Radiology. Artificial intelligence · 2022Article
- Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.Journal of digital imaging · 2021Article
- Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema.European radiology · 2021Article
- Three-dimensional U-Net Convolutional Neural Network for Detection and Segmentation of Intracranial Metastases.Radiology. Artificial intelligence · 2021Article
- Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock.BMC biology · 2020Article
- Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI.Radiology · 2020Article
- Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
An important challenge in segmenting real-world biomedical imaging data is the presence of multiple disease processes within individual subjects. Most adults above age 60 exhibit a variable degree of small vessel ischemic disease, as well as chronic infarcts, which will manifest as white matter hyperintensities (WMH) on brain MRIs. Subjects diagnosed with gliomas will also typically exhibit some degree of abnormal T2 signal due to WMH, rather than just due to tumor. We sought to develop a fully automated algorithm to distinguish and quantify these distinct disease processes within individual subjects' brain MRIs. To address this multi-disease problem, we trained a 3D U-Net to distinguish between abnormal signal arising from tumors vs. WMH in the 3D multi-parametric MRI (mpMRI, i.e., native T1-weighted, T1-post-contrast, T2, T2-FLAIR) scans of the International Brain Tumor Segmentation (BraTS) 2018 dataset (
Indexed as
Identifiers
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.