A validation dataset for Macaque brain MRI segmentation

Yael Balbastre (1) , Denis Rivière (2) , Nicolas Souedet (1) , Clara Fischer (3) , Anne-Sophie Herard (1) , Susannah Williams (1, 4) , Michel Vandenberghe (1) , Julien Flament (4) , Romina Aron-Badin (1, 4) , Philippe Hantraye (5) , Jean-François Mangin (6) , Thierry Delzescaux (4)

1 LMN - Laboratoire des Maladies Neurodégénératives - UMR 9199
2 NEUROSPIN - Service NEUROSPIN
3 CATI - Centre d'Acquisition et de Traitement des Images [Paris] = CATI Multicenter Neuroimaging Platform
4 MIRCEN - Molecular Imaging Research Center [Fontenay-aux-Roses]
5 I2BM - Institut d'Imagerie BioMédicale
6 UNATI - Unité Analyse et Traitement de l'Information

Validation data for segmentation algorithms dedicated to preclinical images is fiercely lacking, especially when compared to the large number of databases of Human brain images and segmentations available to the academic community. Not only is such data essential for validating methods, it is also needed for objectively comparing concurrent algorithms and detect promising paths, as segmentation challenges have shown for clinical images. The dataset we present here is a first step in this direction. It comprises 10 T2-weighted MRIs of healthy adult macaque brains, acquired on a 7 T magnet, along with corresponding manual segmentations into 17 brain anatomic labelled regions spread over 5 hierarchical levels based on a previously published macaque atlas (Calabrese et al., 2015) [1]. By giving access to this unique dataset, we hope to provide a reference needed by the non-human primate imaging community. This dataset was used in an article presenting a new primate brain morphology analysis pipeline, Primatologist (Balbastre et al., 2017) [2]. Data is available through a NITRC repository (https://www.nitrc.org/projects/mircen_macset).

Domaines: Neurosciences [q-bio.NC]; Imagerie

Yael Balbastre, Denis Rivière, Nicolas Souedet, Clara Fischer, Anne-Sophie Herard, et al.. A validation dataset for Macaque brain MRI segmentation. Data in Brief, 2018, 16, pp.37-42. ⟨10.1016/j.dib.2017.11.008⟩. ⟨hal-02155748⟩