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Found 5 papers, 1 papers with code
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery.
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0. 976, 0. 976, and 0. 964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0. 899, 0. 904, and 0. 871, respectively.
A personalized Uncertainty Quantification framework for patient survival models: estimating individual uncertainty of patients with metastatic brain tumors in the absence of ground truth
We evaluated our method on multiple clinically-relevant endpoints, including time to intracranial progression (ICP), progression-free survival (PFS) after SRS, overall survival (OS), and time to ICP and/or death (ICPD), on a variety of both statistical and non-statistical models, including CoxPH, conditional survival forest (CSF), and neural multi-task linear regression (NMTLR).
The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting
The challenge is organized as part of the ASNR-BraTS MICCAI challenge.
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality.
