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

• URL: http://arxiv.org/abs/2010.15526v4
• Date: Wed, 7 Jul 2021 12:17:26 GMT
• Title: An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
• Authors: Kelly Payette, Priscille de Dumast, Hamza Kebiri, Ivan Ezhov, Johannes C. Paetzold, Suprosanna Shit, Asim Iqbal, Romesa Khan, Raimund Kottke, Patrice Grehten, Hui Ji, Levente Lanczi, Marianna Nagy, Monika Beresova, Thi Dao Nguyen, Giancarlo Natalucci, Theofanis Karayannis, Bjoern Menze, Meritxell Bach Cuadra, Andras Jakab
• Abstract summary: We introduce a publicly available database of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions. We quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain.
• Score: 10.486148937249837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 databases of segmented fetal brains. Here we introduce a publicly available database 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 database for the development of automatic algorithms.

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