The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.
AI
BraTS
BraTS-METS
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:
01 Jun 2023
01 Jun 2023
Historique:
pubmed:
3
7
2023
medline:
3
7
2023
entrez:
3
7
2023
Statut:
epublish
Résumé
Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.
Types de publication
Preprint
Langues
eng
Subventions
Organisme : NCI NIH HHS
ID : R21 CA259964
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA242871
Pays : United States