| Autor: |
Jinwoo Hong, Hyuk Jin Yun, Gilsoon Park, Seonggyu Kim, Cynthia T. Laurentys, Leticia C. Siqueira, Tomo Tarui, Caitlin K. Rollins, Cynthia M. Ortinau, P. Ellen Grant, Jong-Min Lee, Kiho Im |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Frontiers in Neuroscience, Vol 14 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
1662-453X |
| DOI: |
10.3389/fnins.2020.591683 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
