aTranslational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, Malet Place Engineering Building, London, WC1E 6BT, UKbDepartment of Development and Regeneration, Woman and Child Cluster, Group Biomedical Sciences, KU Leuven University of Leuven, BelgiumcmoSAIC Facility, Biomedical MRI, Department of Imaging and Pathology, KU Leuven, BelgiumdUniversité Côte d'Azur, Inria, FranceeWellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, UKfInstitute for Women's Health, University College London, UK
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The rabbit model has become increasingly popular in neurodevelopmental studies as it is best suited to bridge the gap in translational research between small and large animals. In the context of preclinical studies, high-resolution magnetic resonance imaging (MRI) is often the best modality to investigate structural and functional variability of the brain, both in vivo and ex vivo. In most of the MRI-based studies, an important requirement to analyze the acquisitions is an accurate parcellation of the considered anatomical structures. Manual segmentation is time-consuming and typically poorly reproducible, while state-of-the-art automated segmentation algorithms rely on available atlases. In this work we introduce the first digital neonatal rabbit brain atlas consisting of 12 multi-modal acquisitions, parcellated into 89 areas according to a hierarchical taxonomy. Delineations were performed iteratively, alternating between segmentation propagation, label fusion and manual refinements, with the aim of controlling the quality while minimizing the bias introduced by the chosen sequence. Reliability and accuracy were assessed with cross-validation and intra- and inter-operator test-retests. Multi-atlas, versioned controlled segmentations repository and supplementary materials download links are available from the software repository documentation at https://github.com/gift-surg/SPOT-A-NeonatalRabbit.HighlightsIn magnetic resonance image analysis accurate automatic segmentations tools are critically important.Multi-atlas based methods are valuable tools to obtain an automatic MRI segmentation.The rabbit model has become increasingly popular in neuro developmental studies.Currently there is no multi-atlas of the newborn rabbit available in literature.In this manuscript we provide the first high resolution MRI multi atlas for the newborn rabbit brain.