2,000 brain FLAIR MRI scans with expert MS lesion segmentation for relapsing-remitting multiple sclerosis

Overview

Our research group is building a large-scale benchmark dataset for automated multiple sclerosis (MS) lesion detection, segmentation, and longitudinal volume tracking over time. We require brain MRI examinations from patients with clinically confirmed relapsing-remitting MS (RRMS) or clinically isolated syndrome (CIS), acquired on either 1.5T or 3T scanners using a standardised protocol wherever site-specific constraints allow. Each study must include at minimum a 3D FLAIR sequence with slice thickness ≤1.5 mm, TR 9000–11000 ms, TE 120–140 ms, and TI 2500 ms, and a 3D T1-weighted MPRAGE or SPGR sequence with TI 900 ms and 1 mm isotropic resolution. Additional sequences such as proton density-weighted (PDw), T2-weighted, and double inversion recovery (DIR) are strongly encouraged and will receive positive weighting during case selection. Magnetisation transfer ratio (MTR) sequences and diffusion tensor imaging (DTI) with fractional anisotropy maps are optional but add significant scientific value and will be purchased at a per-sequence premium. Raw DICOM files are the preferred primary format; NIfTI conversion with corresponding JSON sidecar files containing acquisition metadata including field strength, TR, TE, TI, flip angle, and scanner manufacturer is equally acceptable and facilitates automated quality assurance pipelines. All data must be fully anonymised per DICOM standard PS3.15 Profile and additionally defaced using PyDeface or MRI Deface to eliminate any residual facial surface reconstruction risk. Segmentation masks of T2 white matter lesions must be provided as binary or multi-label NIfTI volumes, delineated on the FLAIR sequence by a neuroradiologist with ≥3 years of dedicated MS-specific reading experience. Lesion-level topographic attributes including periventricular, juxtacortical, infratentorial, and spinal cord location should be encoded in accompanying JSON metadata per the 2016 McDonald criteria framework. Whole-brain and lesion-filling T1-hypointense black hole masks are optional but compensated separately. Baseline clinical metadata required per patient: age at scan date, sex, disease duration in years, EDSS score, current disease-modifying therapy (DMT) class, and whether gadolinium-enhancing lesions were identified on the corresponding post-contrast T1 sequence. QA exclusion criteria include severe motion artefact (ghosting grade ≥2), Gibbs ringing affecting lesion boundaries, field-of-view cropping cutting the cortex, and signal dropout in the posterior fossa that impairs infratentorial lesion detection. Scanner vendor diversity across Siemens, Philips, and GE platforms is required with no single vendor exceeding 50% of the total cohort. The dataset will power supervised and semi-supervised learning models for automated lesion segmentation pipelines integrated into MRI post-processing platforms used by academic MS centres globally. Longitudinal acquisition pairs from the same patient captured ≥6 months apart are particularly valuable for training change-detection and lesion evolution algorithms and will command a 30% per-case premium over cross-sectional studies.

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