AUTHORS: de Dumast P, Kebiri H, Atat C, Dunet V, Koob M, Bach Cuadra M

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. UNSURE 2021, PIPPI 2021, : , Strasbourg, France, October 2021

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 26 clinical MRIs evidences the out-performance of our method independently of the MR reconstruction quality. Finally, as a proof of concept, 3 fetal brains with abnormal cortical development were assessed. The proposed topologically-constrained framework outperforms the baseline, thus, suggesting its additional value to also depict pathology.

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