Detailed delineation of the fetal brain in diffusion MRI via multi-task learning

TL;DR

This paper introduces a multi-task deep learning framework for automated, accurate, and reproducible segmentation and parcellation of fetal brain structures in diffusion MRI, addressing previous limitations due to data quality and rapid development.

Contribution

It presents a novel unified computational method that simultaneously segments tissues, white matter tracts, and cortical regions in fetal brain MRI, validated on a substantial dataset.

Findings

Achieved high Dice scores for tissue and structure segmentation

Demonstrated accurate delineation of 96 brain regions

Enabled improved fetal brain tractography and connectivity analysis

Abstract

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain in-utero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.