An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset.
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6 juillet 2021
- doi: 10.1038/s41597-021-00946-3
- issn: 2052-4463
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info:eu-repo/semantics/dataset/doi/10.5281/zenodo.4541606
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K. Payette et al., « An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset. », Serveur académique Lausannois, ID : 10.1038/s41597-021-00946-3
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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.