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

TL;DR

This paper introduces a new publicly available dataset of manually segmented fetal brain MRIs across various gestational ages, and evaluates multiple automatic segmentation algorithms to advance neurodevelopmental analysis.

Contribution

It provides the first open database of fetal brain MRIs with detailed tissue segmentation and assesses the performance of several automatic segmentation methods.

Findings

The database includes 50 fetal brain MRIs with 7 tissue categories.

Multiple algorithms were evaluated, showing the dataset's utility for development.

Automatic segmentation accuracy varied across methods.

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.