Using Deep Learning to SegmentCardiovascular 4D Flow MRI : 3D U-Net for cardiovascular 4D flow MRI segmentation and Bayesian 3D U-Net for uncertainty estimation

Abstract

Deep convolutional neural networks (CNN’s) have achieved state-of-the-art accuraciesfor multi-class segmentation in biomedical image science. In this thesis, A 3D U-Net isused to segment 4D flow Magnetic Resonance Images that include the heart and its largevessels. The 4 dimensional flow MRI dataset has been segmented and validated using amulti-atlas based registration technique. This multi-atlas based technique resulted in highquality segmentations, with the disadvantage of long computation times typically requiredby three-dimensional registration techniques. The 3D U-Net framework learns to classifyvoxels by transforming the information about the segmentation into a latent feature spacein a contracting path and upsampling them to semantic segmentation in an expandingpath. A CNN trained using a sufficiently diverse set of volumes at different time intervalsof the diastole and systole should be able to handle more extreme morphological differencesbetween subjects. Evaluation of the results is based on metric for segmentation evaluationsuch as Dice coefficient. Uncertainty is estimated using a bayesian implementationof the 3D U-Net of similar architecture.The presentation was online over zoom due to covid19 restrictions.</p