The Brain Tumor Segmentation (BraTS) Challenge 2023:
AI
BraTS
Sub-Saharan Africa
artificial intelligence
brain
challenge
machine learning
segmentation
tumor
Journal
ArXiv
ISSN: 2331-8422
Titre abrégé: ArXiv
Pays: United States
ID NLM: 101759493
Informations de publication
Date de publication:
30 May 2023
30 May 2023
Historique:
pubmed:
3
7
2023
medline:
3
7
2023
entrez:
3
7
2023
Statut:
epublish
Résumé
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.
Types de publication
Preprint
Langues
eng
Subventions
Organisme : NCI NIH HHS
ID : U01 CA242871
Pays : United States