1 Univ Rennes, Inria, CNRS, Inserm - IRISA UMR 6074, Empenn ERL U1228, F-35000 Rennes, France. Electronic address: https://olivier.commowick.org.
2 Univ Rennes, Inria, CNRS, Inserm - IRISA UMR 6074, Empenn ERL U1228, F-35000 Rennes, France.
3 Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France.
4 Univ Rennes, Inria, CNRS, Inserm - IRISA UMR 6074, Empenn ERL U1228, F-35000 Rennes, France; CHU Rennes, Department of Neuroradiology, F-35033, Rennes, France.
5 CHU Rennes, Department of Neurology, Rennes, F-35033, France.
6 CHU de Bordeaux, Service de Neuro-Imagerie, Bordeaux, France.
7 Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon, U1206, F-69621, France.
8 Department of Computer Science and Software Engineering, Concordia university, Montreal, Canada.

MRI plays a crucial role in multiple sclerosis diagnostic and patient follow-up. In particular, the delineation of T2-FLAIR hyperintense lesions is crucial although mostly performed manually - a tedious task. Many methods have thus been proposed to automate this task. However, sufficiently large datasets with a thorough expert manual segmentation are still lacking to evaluate these methods. We present a unique dataset for MS lesions segmentation evaluation. It consists of 53 patients acquired on 4 different scanners with a harmonized protocol. Hyperintense lesions on FLAIR were manually delineated on each patient by 7 experts with control on T2 sequence, and gathered in a consensus segmentation for evaluation. We provide raw and preprocessed data and a split of the dataset into training and testing data, the latter including data from a scanner not present in the training dataset. We strongly believe that this dataset will become a reference in MS lesions segmentation evaluation, allowing to evaluate many aspects: evaluation of performance on unseen scanner, comparison to individual experts performance, comparison to other challengers who already used this dataset, etc.