Details

Autor(en) / Beteiligte

• Payette, Kelly
• de Dumast, Priscille
• Kebiri, Hamza
• Ezhov, Ivan
• Paetzold, Johannes C
• Shit, Suprosanna
• Iqbal, Asim
• Khan, Romesa
• Kottke, Raimund
• Grehten, Patrice
• Ji, Hui
• Lanczi, Levente
• Nagy, Marianna
• Beresova, Monika
• Nguyen, Thi Dao
• Natalucci, Giancarlo
• Karayannis, Theofanis
• Menze, Bjoern
• Bach Cuadra, Meritxell
• Jakab, Andras

Titel

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

Ist Teil von

• Scientific data, 2021-07, Vol.8 (1), p.167-167, Article 167

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2021

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.