ReviewSensors (Basel, Switzerland)2025
Advancing Precision: A Comprehensive Review of MRI Segmentation Datasets from BraTS Challenges (2012-2025).
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Glioblastoma MRI Dataset with Standardized Preprocessing, Expert-Validated Segmentation, and MGMT Profiling.Scientific data · 2026Article
- Artificial intelligence-enabled pediatric radiology in low-resource settings: addressing resource constraints in the African healthcare system.Pediatric radiology · 2026Review
- Knowledge-guided brain tumor segmentation via synchronized visual-semantic-topological prior fusion.BMC medical imaging · 2026Article
- U-ConvNext: A Robust Approach to Glioma Segmentation in Intraoperative Ultrasound.Journal of imaging informatics in medicine · 2026Article
- PF-CMNet: Progressive Frequency-Aware Cross-Modal Network with Missing-Modality Distillation for 3D Brain Tumor Segmentation.Brain sciences · 2026Article
- The explainability of radiomic-based machine learning models for brain glioma grading on amide proton transfer-weighted images.Scientific reports · 2026Article
- A Review of U-Net Based Deep Learning Frameworks for MRI-Based Brain Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2026Review
- Performance enhancement method by using the probabilistic estimation for kidney tumor segmentation.Frontiers in oncology · 2026Article
- Computational Pipeline in Neuroradiomics.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development.Journal of imaging · 2025Review
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives.Journal of imaging · 2025Review
- Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.Scientific reports · 2025Article
- Validation of open-source deep learning segmentation tools for automated glioma volumetry: a narrative review of Dice scores, workflow efficiency, and clinical RANO 2.0 implementation.Frontiers in neurologyReview
- GMANet: a CPU-only ultra-lightweight 3D CNN for real-time glioma triage in resource-limited environments.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain Tumor Segmentation (BraTS) challenges have significantly advanced research in brain tumor segmentation and related medical imaging tasks. This paper provides a comprehensive review of the BraTS datasets from 2012 to 2024, examining their evolution, challenges, and contributions to MRI-based brain tumor segmentation. Over the years, the datasets have grown in size, complexity, and scope, incorporating refined pre-processing and annotation protocols. By synthesizing insights from over a decade of BraTS challenges, this review elucidates the progression of dataset curation, highlights the impact on state-of-the-art segmentation approaches, and identifies persisting limitations and future directions. Crucially, it provides researchers, clinicians, and industry stakeholders with a single, in-depth resource on the evolution and practical utility of BraTS datasets-demonstrating year-by-year improvements in the field and discussing their potential for enabling robust, clinically relevant segmentation methods that can further advance precision medicine. Additionally, an overview of the upcoming BraTS 2025 Challenge-currently in planning-is presented, highlighting its expanded focus across further clinical needs.
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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.