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

Kelly Payette1,2, Priscille de Dumast3,4, Hamza Kebiri3,4

  • 1Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland. kelly.payette@kispi.uzh.ch.

Scientific Data
|July 7, 2021
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Summary

We created a new dataset of 50 segmented fetal brains to advance the study of neurodevelopment. This resource aids in developing automatic segmentation algorithms for analyzing fetal brain development and disorders.

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Area of Science:

  • Neuroimaging
  • Developmental Neuroscience
  • Medical Image Analysis

Background:

  • Quantitative analysis of the developing human fetal brain is crucial for understanding neurodevelopment and congenital disorders.
  • Automatic multi-tissue fetal brain segmentation algorithms require open, segmented datasets for development and validation.

Purpose of the Study:

  • To introduce a publicly available dataset of manually segmented fetal brain MRI volumes.
  • To quantitatively evaluate the performance of automatic multi-tissue segmentation algorithms for the developing human fetal brain.

Main Methods:

  • Manual segmentation of 50 fetal brain MRI volumes (20-33 weeks gestation) into 7 tissue categories.
  • Dataset includes both pathological and non-pathological cases.
  • Evaluation of 10 automatic segmentation algorithms submitted by four research groups.

Main Results:

  • A new, manually segmented dataset of 50 fetal brain MRI volumes is now publicly available.
  • The dataset facilitates the quantitative evaluation of automatic segmentation algorithms.
  • Multiple algorithms were tested, showcasing the dataset's utility for algorithm development.

Conclusions:

  • The introduced dataset is a valuable resource for advancing research in fetal neurodevelopment.
  • The dataset enables the development and benchmarking of automatic fetal brain segmentation algorithms.
  • This work supports the quantitative analysis of fetal brain development in both typical and atypical cases.