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

Details

Ressource 1Download: s41597-021-00946-3.pdf (12545.73 [Ko])
State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_6EB3F06B58CA
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset.
Journal
Scientific data
Author(s)
Payette K., de Dumast P., Kebiri H., Ezhov I., Paetzold J.C., Shit S., Iqbal A., Khan R., Kottke R., Grehten P., Ji H., Lanczi L., Nagy M., Beresova M., Nguyen T.D., Natalucci G., Karayannis T., Menze B., Bach Cuadra M., Jakab A.
ISSN
2052-4463 (Electronic)
ISSN-L
2052-4463
Publication state
Published
Issued date
06/07/2021
Peer-reviewed
Oui
Volume
8
Number
1
Pages
167
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: epublish
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 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.
Pubmed
Web of science
Research datasets
Open Access
Yes
Funding(s)
Swiss National Science Foundation / Projects / 205321_182602
Create date
29/05/2021 8:38
Last modification date
26/08/2021 7:10
Usage data