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Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset

Abstract : 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.
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Contributor : Olivier Commowick Connect in order to contact the contributor
Submitted on : Wednesday, September 29, 2021 - 5:15:29 PM
Last modification on : Saturday, September 24, 2022 - 2:44:04 PM
Long-term archiving on: : Thursday, December 30, 2021 - 7:50:46 PM


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Olivier Commowick, Michaël Kain, Romain Casey, Roxana Ameli, Jean-Christophe Ferré, et al.. Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset. NeuroImage, Elsevier, 2021, 244, pp.1-8. ⟨10.1016/j.neuroimage.2021.118589⟩. ⟨hal-03358961⟩



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