It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open databases of segmented fetal brains. Here we introduce a publicly available database of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, deep grey matter, brainstem/spinal cord). In addition, we quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain. Four research groups participated, submitting a total of 10 algorithms, demonstrating the benefits the database for the development of automatic algorithms.
@article{arxiv.2010.15526,
title = {An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset},
author = {Kelly Payette and Priscille de Dumast and Hamza Kebiri and Ivan Ezhov and Johannes C. Paetzold and Suprosanna Shit and Asim Iqbal and Romesa Khan and Raimund Kottke and Patrice Grehten and Hui Ji and Levente Lanczi and Marianna Nagy and Monika Beresova and Thi Dao Nguyen and Giancarlo Natalucci and Theofanis Karayannis and Bjoern Menze and Meritxell Bach Cuadra and Andras Jakab},
journal= {arXiv preprint arXiv:2010.15526},
year = {2021}
}
Comments
This is a preprint of an article published in Nature Scientific Data. The final authenticated version is available online at: https://doi.org/10.1038/s41597-021-00946-3