paper

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

arXiv:2010.15526 · doi: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 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.

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

References in corpus (1)

Cited by in corpus (10)