Segmentation of the cortical plate in fetal brain MRI with a topological loss
This work addresses the need for topologically correct segmentation of fetal cortical gray matter for quantitative brain development analysis, representing an incremental advancement in medical imaging.
The paper tackled the problem of accurately segmenting the cortical plate in fetal brain MRI by integrating a topological constraint as an additional loss function, resulting in significant improvements across gestational ages and outperforming a baseline method in both quantitative and qualitative evaluations.
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.