AUTHOR=Hong Jinwoo , Yun Hyuk Jin , Park Gilsoon , Kim Seonggyu , Laurentys Cynthia T. , Siqueira Leticia C. , Tarui Tomo , Rollins Caitlin K. , Ortinau Cynthia M. , Grant P. Ellen , Lee Jong-Min , Im Kiho TITLE=Fetal Cortical Plate Segmentation Using Fully Convolutional Networks With Multiple Plane Aggregation JOURNAL=Frontiers in Neuroscience VOLUME=14 YEAR=2020 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.591683 DOI=10.3389/fnins.2020.591683 ISSN=1662-453X 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 brain MR images (22.9–31.4 weeks of gestation). The proposed method obtained Dice coefficients of 0.907 ± 0.027 and 0.906 ± 0.031 as well as a mean surface distance error of 0.182 ± 0.058 mm and 0.185 ± 0.069 mm for the left and right, respectively. In addition, the left and right CP volumes, surface area, and global mean curvature generated by automatic segmentation showed a high correlation with the values generated by manual segmentation (R2 > 0.941). We also demonstrated that the proposed hybrid loss function and the combination of multi-view aggregation and test-time augmentation significantly improved the CP segmentation accuracy. Our proposed segmentation method will be useful for the automatic and reliable quantification of the cortical structure in the fetal brain.