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

Overview

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.

publication date

  • July 6, 2021

Research

keywords

  • Brain
  • Fetus
  • Neurogenesis

Identity

PubMed Central ID

  • PMC8260784

Scopus Document Identifier

  • 85109295400

Digital Object Identifier (DOI)

  • 10.1038/s41597-021-00946-3

PubMed ID

  • 34230489

Additional Document Info

volume

  • 8

issue

  • 1