Breakfast & Science Seminar 26

Abstract: The quantitative assessment of the developing human brain in utero is crucial to fully understand neurodevelopment. T2-weighted magnetic resonance imaging offers a good contrast between brain tissues, hence allowing to assess the brain growth and detect abnormalities in utero. In the clinical context, fetal brain MRI is performed with fast, 2D orthogonal series in order to minimize the effect of unpredictable fetal motion but results in low out-of-plane spatial resolution and significant partial volume effect. To combine these multiple series, advanced imaging techniques based on super-resolution (SR) algorithms allow the reconstruction of 3D high-resolution motion-free isotropic volumes. Such high-resolution volume opens up to the possibility of advanced quantitative analysis of improved accuracy. Accurate MR image segmentation, and more importantly a topologically correct delineation of the structure, is a key baseline to perform further morphometric and volumetric analysis of brain development. Nevertheless, the development of automatic machine learning based methods is hampered by the scarcity of the data and their multiple sources of variability. In this talk, I will discuss my work on the introduction of a topological constraint in the segmentation of the developing fetal cortical plate.

PhD Supervisors: Dr. Meritxell Bach Cuadra (CIBM SP CHUV-UNIL)