Abstract
Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.
Original language | English (US) |
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Article number | 496 |
Journal | Scientific Data |
Volume | 11 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2024 |
Funding
We are grateful to everyone who contributed to the development and review of the tumor volume labels including volunteer annotators/approvers from the American Society of Neuroradiology (ASNR). Spyridon Bakas and Ujjwal Baid conducted part of the work reported in this manuscript at their current affiliation, as well as while they were affiliated with the Center for Biomedical Image Computing and Analytics (CBICA), the Department of Radiology, and the Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA. Research reported in this publication was partly supported by the National Institutes of Health (NIH), under award numbers U24CA279629, U01CA242871, NCI K08CA256045, and NCI/ITCR U01CA242871. The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH.
ASJC Scopus subject areas
- Statistics and Probability
- Information Systems
- Education
- Computer Science Applications
- Statistics, Probability and Uncertainty
- Library and Information Sciences