Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset - Inria - Institut national de recherche en sciences et technologies du numérique
Journal Articles NeuroImage Year : 2021

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

Thomas Tourdias
  • Function : Author
Tristan Glatard
Christian Barillot
Michel Dojat

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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Medical Imaging
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Origin Publication funded by an institution

Dates and versions

hal-03358961 , version 1 (29-09-2021)

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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, 2021, 244, pp.1-8. ⟨10.1016/j.neuroimage.2021.118589⟩. ⟨hal-03358961⟩
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