Fetal Cortical Plate Segmentation Using Fully Convolutional Networks With Multiple Plane Aggregation.

ABSTRACT: Fetal magnetic resonance imaging (MRI) has the potential to advance our understanding of human brain development by providing quantitative information of cortical plate (CP) development in vivo. However, for a reliable quantitative analysis of cortical volume and sulcal folding, accurate and automated segmentation of the CP is crucial. In this study, we propose a fully convolutional neural network for the automatic segmentation of the CP. We developed a novel hybrid loss function to improve the segmentation accuracy and adopted multi-view (axial, coronal, and sagittal) aggregation with a test-time augmentation method to reduce errors using three-dimensional (3D) information and multiple predictions. We evaluated our proposed method using the ten-fold cross-validation of 52 fetal bra

SUBMITTER: Hong J 

PROVIDER: S-EPMC7738480 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature