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 (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 dataset for the development of automatic algorithms.
Measurement(s)
regional part of brain • T2 (Observed)-Weighted Imaging
Technology Type(s)
Image Segmentation
Sample Characteristic - Organism
Homo sapiens
Machine-accessible metadata file describing the reported data:
https://doi.org/10.6084/m9.figshare.14039327...

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

10.1038/s41597-021-00946-3