Segmentation of the cortical plate in fetal brain MRI with a topological loss

TL;DR

This paper introduces a novel topological loss function in deep learning to improve the accuracy and morphological correctness of fetal cortical plate segmentation in MRI, across various gestational ages.

Contribution

It is the first to incorporate a topological constraint as a loss function in fetal brain MRI segmentation, enhancing morphological consistency.

Findings

Significant improvement over baseline across all gestational ages.

Effective regardless of MRI reconstruction quality.

Validated on 18 fetal brain atlases and expert qualitative assessments.

Abstract

The fetal cortical plate undergoes drastic morphological changes throughout early in utero development that can be observed using magnetic resonance (MR) imaging. An accurate MR image segmentation, and more importantly a topologically correct delineation of the cortical gray matter, is a key baseline to perform further quantitative analysis of brain development. In this paper, we propose for the first time the integration of a topological constraint, as an additional loss function, to enhance the morphological consistency of a deep learning-based segmentation of the fetal cortical plate. We quantitatively evaluate our method on 18 fetal brain atlases ranging from 21 to 38 weeks of gestation, showing the significant benefits of our method through all gestational ages as compared to a baseline method. Furthermore, qualitative evaluation by three different experts on 130 randomly selected slices from 26 clinical MRIs evidences the out-performance of our method independently of the MR reconstruction quality.