A dataset of 53 multiple sclerosis patients with manual lesion delineations by 7 experts was created to serve as a reference for evaluating MS lesion segmentation methods.
Provides a reference dataset with expert consensus segmentation for evaluating automated multiple sclerosis lesion segmentation methods.
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.
Commowick et al. (2021) studied Multiple sclerosis (n=53). Dataset for MS lesions segmentation was evaluated on MS lesions segmentation evaluation. A dataset of 53 multiple sclerosis patients with manual lesion delineations by 7 experts was created to serve as a reference for evaluating MS lesion segmentation methods.
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