ReviewNeuro-oncology advances
Brain tumor segmentation in Sub-Saharan Africa patient population: The BraTS-Africa challenge.
Review in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Predicting brain tumour enhancement from non-contrast MRI with artificial intelligence: a multicohort, retrospective, diagnostic accuracy study.The Lancet medical imaging & theranostics · 2026Article
- Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- GDNet: A Robust 2.5D Multimodal MRI Brain Tumor Segmentation Framework with EMA Stabilization and Tumor-Aware Sampling.Journal of imaging · 2026Article
- ABR-UNet3D: Aspect-Aware Boundary-Resilient Attention for Robust Cardiac MRI Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- In vivo characterization of the redox balance in IDH-wildtype glioblastomas: a J-difference edited MEGA-sLASER MRS study at 3T.Cancer & metabolism · 2026Article
- A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.European radiology · 2026Article
- Distilling population specific expertise into a unified model for generalizable brain tumor segmentation.Scientific reports · 2026Article
- Source-Free Active Domain Adaptation for Brain Tumor Segmentation via Mamba and Region-Level Uncertainty.Brain sciences · 2026Article
- Contrast-free identification of glioma blood-brain barrier status via generative diffusion AI and non-contrast MRI.Nature communications · 2026Article
- Assessing Impact of Data Quality in Early Post-Operative Glioblastoma Segmentation.Journal of imaging · 2026Article
- The 2024 Brain Tumor Segmentation Challenge Meningioma Radiotherapy (BraTS-MEN-RT) dataset.Scientific data · 2026Article
- A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation.PloS one · 2026Article
- Improving Pre-trained Adult Glioma Segmentation Models using only Post-processing Techniques.Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,... · 2026Article
- A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development.Journal of imaging · 2025Review
- Optimized Long-TEJournal of magnetic resonance imaging : JMRI · 2025Article
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives.Journal of imaging · 2025Review
- The BraTS-Africa Dataset: Expanding the Brain Tumor Segmentation Data to Capture African Populations.Radiology. Artificial intelligence · 2025Article
- Advancing Precision: A Comprehensive Review of MRI Segmentation Datasets from BraTS Challenges (2012-2025).Sensors (Basel, Switzerland) · 2025Review
- MD-SA2: optimizing Segment Anything 2 for multimodal, depth-aware brain tumor segmentation in sub-Saharan populations.Journal of medical imaging (Bellingham, Wash.) · 2025Article
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30 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Automated brain tumor segmentation on multi-parametric magnetic resonance imaging (mpMRI) is crucial in assessing patient outcomes and remains a challenge across Sub-Saharan Africa (SSA). Since 2012, the Brain Tumor Segmentation (BraTS) Challenge has evaluated state-of-the-art artificial intelligence (AI) methods to detect, characterize, and classify tumors. However, it is unclear if these methods can generalize, and hence be widely implemented, in SSA populations. To address this, the BraTS-Africa challenge was conducted in 2023 and 2024 to evaluate population-specific AI models for automated segmentation of brain tumors on mpMRI from the region. Methods: Preoperative T1-weighted, T1-contrast enhanced, T2-weighted, and T2-FLAIR brain MRI scans of 115 patients diagnosed with adult diffuse glioma were curated, annotated, and utilized for the Challenge. Participants were invited to develop and validate their methods. The participating teams were evaluated using weighted Dice Score Coefficient (DSC) and 95% Hausdorff Distance (HD95). Results: In the 2023 challenge a total of 9 teams with participants from 12 countries met the submission criteria and were ranked. The 2024 challenge featured 6 teams from 7 countries. Compared to 2023, the six top ranked methods from 2024 had higher DSC (9.4%) and lower HD95 (57.21%). Conclusions: Together, the 2023 and 2024 challenges provided a unique opportunity to include brain mpMRI glioma cases from SSA in global efforts to develop and evaluate AI methods for the detection of glioma boundaries and their quantification, where the potential of AI solutions to transform healthcare into resource-limited settings is more likely.
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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.