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Updated: Jun 26, 2025
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
Published on: December 15, 2023
A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation
Dominic LaBella1, Omaditya Khanna2, Shan McBurney-Lin3
1Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
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.
Insights
The BraTS Pre-operative Meningioma Dataset offers the largest collection of expert-annotated brain MRI scans for meningioma research. This resource aims to advance automated segmentation tools for improved patient care.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Oncology
Background:
- Meningiomas are common primary brain tumors causing significant morbidity and mortality.
- Accurate diagnosis and monitoring rely heavily on multi-sequence brain MRI.
- Current limitations exist in automated, objective, and quantitative tools for meningioma assessment.
Purpose of the Study:
- To introduce the BraTS Pre-operative Meningioma Dataset, the largest multi-institutional expert-annotated dataset for meningioma research.
- To facilitate the development of automated computational methods for meningioma segmentation.
- To expedite the clinical integration of these automated tools for improved patient management.
Main Methods:
- Compiled 1,141 multi-sequence MR images from six institutions.
- Included four structural MRI sequences: T2, T2/FLAIR, pre-contrast T1, and post-contrast T1-weighted.
- Provided expert-annotated segmentations for enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity, along with demographic data and CNS WHO grade.
Main Results:
- Established the largest multi-institutional, expert-annotated, multi-sequence meningioma MRI dataset to date.
- The dataset comprises 1,141 cases with detailed segmentation of tumor sub-compartments.
- Includes demographic and clinical data to support comprehensive research.
Conclusions:
- The BraTS Pre-operative Meningioma Dataset is a valuable resource for advancing automated meningioma analysis.
- Its release aims to spur innovation in computational methods for segmentation.
- Ultimately, the goal is to enhance the clinical care of patients with meningiomas.
