# The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)

arXiv: https://arxiv.org/abs/2305.19369 · HTML: https://mlsanity.com/paper/2305.19369/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 5 of 5)

- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging](https://mlsanity.com/paper/2103.01006/) (2021)
- [Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator](https://mlsanity.com/paper/1909.06763/) (2019)

## Built on by (most cited first, 30 of 51)

- [The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI](https://mlsanity.com/paper/2405.18368/) (2024)
- [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024)
- [How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation](https://mlsanity.com/paper/2402.17317/) (2024)
- [AutoRG-Brain: Grounded Report Generation for Brain MRI](https://mlsanity.com/paper/2407.16684/) (2024)
- [Multi-modal Vision Pre-training for Medical Image Analysis](https://mlsanity.com/paper/2410.10604/) (2024)
- [UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis](https://mlsanity.com/paper/2510.15710/) (2025)
- [In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review](https://mlsanity.com/paper/2501.10727/) (2025)
- [Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data](https://mlsanity.com/paper/2412.04111/) (2024)
- [Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge](https://mlsanity.com/paper/2405.18383/) (2024)
- [Federated brain tumor segmentation: an extensive benchmark](https://mlsanity.com/paper/2410.17265/) (2024)
- [A Large Scale Benchmark for Test Time Adaptation Methods in Medical Image Segmentation](https://mlsanity.com/paper/2512.02497/) (2025)
- [An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation](https://mlsanity.com/paper/2402.07008/) (2024)
- [3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge](https://mlsanity.com/paper/2403.15735/) (2024)
- [Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics](https://mlsanity.com/paper/2411.15872/) (2024)
- [On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks](https://mlsanity.com/paper/2405.02852/) (2024)
- [Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation](https://mlsanity.com/paper/2406.02125/) (2024)
- [SAM-aware Test-time Adaptation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2506.05221/) (2025)
- [The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning](https://mlsanity.com/paper/2512.06206/) (2025)
- [MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis](https://mlsanity.com/paper/2602.22955/) (2026)
- [Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)](https://mlsanity.com/paper/2311.14148/) (2023)
- [Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis](https://mlsanity.com/paper/2410.18698/) (2024)
- [Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence: a multi-cohort retrospective diagnostic accuracy study](https://mlsanity.com/paper/2508.16650/) (2025)
- [Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2409.13229/) (2024)
- [Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset](https://mlsanity.com/paper/2412.14100/) (2024)
- [Multi-encoder nnU-Net outperforms transformer models with self-supervised pretraining](https://mlsanity.com/paper/2504.03474/) (2025)
- [Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models](https://mlsanity.com/paper/2505.02753/) (2025)
- [Dual Path Learning -- learning from noise and context for medical image denoising](https://mlsanity.com/paper/2507.19035/) (2025)
- [EMedNeXt: An Enhanced Brain Tumor Segmentation Framework for Sub-Saharan Africa using MedNeXt V2 with Deep Supervision](https://mlsanity.com/paper/2507.23256/) (2025)
- [Resource-Efficient Glioma Segmentation on Sub-Saharan MRI](https://mlsanity.com/paper/2509.09469/) (2025)
- [A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development](https://mlsanity.com/paper/2510.20196/) (2025)

## Abstract (arXiv)

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.