Fetal Tissue Annotation Dataset FeTA

Abstract

The Fetal Tissue Annotation Dataset (FeTA) consist of manually annotated, T2-weighted, super-resolution reconstructed fetal cerebral magnetic resonance images. It is a mixture of normally developing cases and pathologies. The dataset is a valuable source for developing automated image segmentation algorithms as it provides open source MRI data and expert manual annotations, which is a particularly time consuming process. Each fetal brains were labeled for 7 tissue categories: grey matter, white matter, external CSF spaces, ventricle system, deep gray matter, cerebellum and brainstem. From May 2021, access to the FeTA dataset is only possible on the Synapse platform. We released the second version with 80 cases, which must be used for participants of the MICCAI Fetal Tissue Annotation Challenge in 2021. Please visit the following sites for further information: https://feta-2021.grand-challenge.org/ https://www.synapse.org/#!Synapse:syn25649159/wiki/610007 <strong>Background</strong> Congenital disorders are one of the leading causes of infant mortality worldwide. Recently, fetal MRI has started to emerge as a valuable tool for investigating the neurological development of fetuses with congenital disorders in order to aid in prenatal planning. Moreover, fetal MRI is a powerful tool to portray the complex neurodevelopmental events during human gestation, which remain to be completely characterized. Automated segmentation and quantification of the highly complex and rapidly changing brain morphology in MRI data would improve the diagnostic process, as manual segmentation is both time consuming and prone to human error and inter-rater variability. The automatic segmentation of the developing human brain would be a first step in being able to perform such an analysis. The FeTA Dataset and the Challenge we plan to organize are important steps in the development of reproducible methods of analyzing high resolution MR images of the developing fetal brain. Such new algorithms will have the potential to better understand the underlying causes of congenital disorders and ultimately to support decision-making and prenatal planning.

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