1,500 multiparametric brain MRI studies with BraTS-style glioma segmentation masks

Overview

We are seeking a large cohort of multiparametric brain MRI examinations from adult patients (18+) with histologically confirmed glioma (WHO grades II–IV), including glioblastoma multiforme. Each study must include at minimum four MRI sequences acquired on a 3T scanner: pre-contrast T1-weighted (T1w), post-contrast T1-weighted with gadolinium enhancement (T1Gd), T2-weighted (T2w), and T2-FLAIR. Preferred acquisition parameters include slice thickness ≤1.5 mm for volumetric sequences, TR 2000–2500 ms and TE 20–30 ms for T2w, and isotropic or near-isotropic 1 mm voxel resolution for T1w MPRAGE. Flip angle for T1w is typically 9°; inversion time (TI) for MPRAGE is 900–1100 ms. All sequences must be acquired on the same scanner in a single session where possible to minimise inter-sequence registration error. Data must be delivered in NIfTI format after DICOM-to-NIfTI conversion; original DICOM files are also welcomed as a secondary format. Volumetric DICOM series with intact DICOM headers are required to allow retrospective review of acquisition metadata including manufacturer, model, field strength, and sequence name. All volumes must be skull-stripped and defaced using tools such as FreeSurfer mri_deface or FSL BET to prevent facial reconstruction and patient re-identification, in full compliance with HIPAA Safe Harbor de-identification and GDPR pseudonymisation standards under Article 89 research exemptions. Annotation requirements follow the BraTS challenge protocol: three sub-region segmentation masks per case — enhancing tumor (ET), tumor core (TC), and whole tumor (WT) — provided as integer-labeled NIfTI files co-registered to the T1Gd reference volume using affine or non-linear registration. Masks must be generated or verified by a board-certified neuroradiologist or neurosurgeon with at minimum five years of neuro-oncology reading experience. Inter-annotator agreement scores measured by Dice coefficient must reach ≥0.80 on at least a random 10% holdout subset, with cases below threshold re-adjudicated by a senior radiologist. Clinical metadata should include age, sex, WHO tumor grade, IDH mutation status if available, MGMT promoter methylation status, and ECOG performance score where recorded in the patient record. This dataset will be used to train and benchmark deep-learning segmentation models for surgical planning support tools and treatment response and progression monitoring applications. We particularly need representation from scanner vendors including Siemens Healthineers, GE Healthcare, Philips, and Canon to maximise model generalisability across heterogeneous clinical environments. Longitudinal studies capturing pre-operative, post-operative, and follow-up timepoints are highly desirable and will be compensated at a premium rate per case. Institutions contributing multi-site data from geographically diverse centres outside North America are especially encouraged to apply, as global demographic and scanner diversity is a key requirement for regulatory-grade AI model development.

Progress

Data Specifications

Use Cases

• Training and validating Medical imaging AI/ML models
• Benchmarking Medical imaging detection and segmentation algorithms
• Building de-identified Medical imaging research datasets for academic studies
• Augmenting existing Medical imaging datasets to reduce class imbalance