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

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.

Automated summary

Quantitatively analyzing the developing human fetal brain is crucial for understanding neurodevelopment in both normal and congenital disorder cases. To achieve this, automatic multi-tissue fetal brain segmentation algorithms are required, which in turn rely on open datasets of segmented fetal brains. Research has shown the positive impact of such datasets in the development of automatic algorithms.