Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이종민 | - |
dc.date.accessioned | 2022-09-19T05:51:48Z | - |
dc.date.available | 2022-09-19T05:51:48Z | - |
dc.date.issued | 2020-12 | - |
dc.identifier.citation | FRONTIERS IN NEUROSCIENCE, v. 14, article no. 591683, page. 1-13 | en_US |
dc.identifier.issn | 1662-453X | - |
dc.identifier.uri | https://www.frontiersin.org/articles/10.3389/fnins.2020.591683/full | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/172979 | - |
dc.description.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 (R-2 ˃ 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. | en_US |
dc.description.sponsorship | This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C0755); Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) [No. 2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)]; Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health (NIH) (R21HD094130, U01HD087211, R01HD100009, and K23HD079605); National Institute of Neurological Disorders and Stroke of the National Institutes of Health (R01NS114087); American Heart Association (19IPLOI34660336); National Institutes of Health/National Heart, Lung, and Blood Institute (K23HL141602); National Institute of Neurological Disorders and Stroke (K23-NS101120); National Institute of Biomedical Imaging and Bioengineering (R01EB017337); and the Susan Saltonstall Foundation. | en_US |
dc.language.iso | en | en_US |
dc.publisher | FRONTIERS MEDIA SA | en_US |
dc.subject | deep learning | en_US |
dc.subject | fetal brain | en_US |
dc.subject | cortical plate | en_US |
dc.subject | segmentation | en_US |
dc.subject | hybrid loss | en_US |
dc.subject | MR | en_US |
dc.title | Fetal Cortical Plate Segmentation Using Fully Convolutional Networks With Multiple Plane Aggregation | en_US |
dc.type | Article | en_US |
dc.relation.volume | 14 | - |
dc.identifier.doi | 10.3389/fnins.2020.591683 | - |
dc.relation.page | 1-13 | - |
dc.relation.journal | FRONTIERS IN NEUROSCIENCE | - |
dc.contributor.googleauthor | Hong, Jinwoo | - |
dc.contributor.googleauthor | Yun, Hyuk Jin | - |
dc.contributor.googleauthor | Park, Gilsoon | - |
dc.contributor.googleauthor | Kim, Seonggyu | - |
dc.contributor.googleauthor | Laurentys, Cynthia T. | - |
dc.contributor.googleauthor | Siqueira, Leticia C. | - |
dc.contributor.googleauthor | Tarui, Tomo | - |
dc.contributor.googleauthor | Rollins, Caitlin K. | - |
dc.contributor.googleauthor | Ortinau, Cynthia M. | - |
dc.contributor.googleauthor | Lee, Jong-Min | - |
dc.relation.code | 2020047224 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | SCHOOL OF ELECTRICAL AND BIOMEDICAL ENGINEERING | - |
dc.identifier.pid | ljm | - |
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