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

• Published in: Frontiers in neuroscience 2020-12, Vol.14, p.591683-591683
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Brain; cortical plate; Deep learning; Diabetes; fetal brain; Fetuses; Gestation; hybrid loss; Magnetic resonance imaging; Methods; MRI; Neural networks; Neuroimaging; Neuroscience; Registration; Segmentation; Ultrasonic imaging
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2020.591683

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 . 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 ( > 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.