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It is critical to quantitatively analyse the developing human fetal brain inorder to fully understand neurodevelopment in both normal fetuses and thosewith congenital disorders. To facilitate this analysis, automatic multi-tissuefetal brain segmentation algorithms are needed, which in turn requires opendatabases of segmented fetal brains. Here we introduce a publicly availabledatabase of 50 manually segmented pathological and non-pathological fetalmagnetic resonance brain volume reconstructions across a range of gestationalages (20 to 33 weeks) into 7 different tissue categories (externalcerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, deepgrey matter, brainstem/spinal cord). In addition, we quantitatively evaluatethe accuracy of several automatic multi-tissue segmentation algorithms of thedeveloping human fetal brain. Four research groups participated, submitting atotal of 10 algorithms, demonstrating the benefits the database for thedevelopment of automatic algorithms.

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