An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset

Payette, Kelly; de Dumast, Priscille; Kebiri, Hamza; Ezhov, Ivan; Paetzold, Johannes C; Shit, Suprosanna; Iqbal, Asim; Khan, Romesa; Kottke, Raimund; Grehten, Patrice; Ji, Hui; Lanczi, Levente; Nagy, Marianna; Beresova, Monika; Nguyen, Thi Dao; Natalucci, Giancarlo; Karayannis, Theofanis; Menze, Bjoern; Bach Cuadra, Meritxell; Jakab, András

doi:10.1038/s41597-021-00946-3

Citations

Citation copied

Payette, K., de Dumast, P., Kebiri, H., Ezhov, I., Paetzold, J. C., Shit, S., Iqbal, A., Khan, R., Kottke, R., Grehten, P., Ji, H., Lanczi, L., Nagy, M., Beresova, M., Nguyen, T. D., Natalucci, G., Karayannis, T., Menze, B., Bach Cuadra, M., & Jakab, A. (2021). An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset. Scientific Data, 8(1), 167–181. https://doi.org/10.1038/s41597-021-00946-3

Abstract

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 datasets of segmented fetal brains. Here we introduce a publicly available dataset of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (