Automatic Fetal Brain Extraction Using Multi-stage U-Net with Deep Supervision

Abstract

Fetal brain extraction is one of the most essential steps for prenatal brain MRI reconstruction and analysis. However, due to the fetal movement within the womb, it is a challenging task to extract fetal brains from sparsely-acquired imaging stacks typically with motion artifacts. To address this problem, we propose an automatic brain extraction method for fetal magnetic resonance imaging (MRI) using multi-stage 2D U-Net with deep supervision (DS U-net). Specifically, we initially employ a coarse segmentation derived from DS U-net to define a 3D bounding box for localizing the position of the brain. The DS U-net is trained with deep supervision loss to acquire more powerful discrimination capability. Then, another DS U-net focuses on the extracted region to produce finer segmentation. The final segmentation results are obtained by performing refined segmentation. We validate the proposed method on 80 stacks of training images and 43 testing stacks. The experimental results demonstrate the precision and robustness of our method with the average Dice coefficient of 91.69%, outperforming the existing methods.