Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이종민 | - |
dc.date.accessioned | 2022-12-06T06:28:11Z | - |
dc.date.available | 2022-12-06T06:28:11Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.citation | NeuroImage, v. 237, article no. 118140 | en_US |
dc.identifier.issn | 1053-8119;1095-9572 | en_US |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S1053811921004171?via%3Dihub | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/178037 | - |
dc.description.abstract | White matter hyperintensities (WMHs) are abnormal signals within the white matter region on the human brain MRI and have been associated with aging processes, cognitive decline, and dementia. In the current study, we proposed a U-Net with multi-scale highlighting foregrounds (HF) for WMHs segmentation. Our method, U-Net with HF, is designed to improve the detection of the WMH voxels with partial volume effects. We evaluated the segmentation performance of the proposed approach using the Challenge training dataset. Then we assessed the clinical utility of the WMH volumes that were automatically computed using our method and the Alzheimer's Disease Neuroimaging Initiative database. We demonstrated that the U-Net with HF significantly improved the detection of the WMH voxels at the boundary of the WMHs or in small WMH clusters quantitatively and qualitatively. Up to date, the proposed method has achieved the best overall evaluation scores, the highest dice similarity index, and the best F1-score among 39 methods submitted on the WMH Segmentation Challenge that was initially hosted by MICCAI 2017 and is continuously accepting new challengers. The evaluation of the clinical utility showed that the WMH volume that was automatically computed using U-Net with HF was significantly associated with cognitive performance and improves the classification between cognitive normal and Alzheimer's disease subjects and between patients with mild cognitive impairment and those with Alzheimer's disease. The implementation of our proposed method is publicly available using Dockerhub (https://hub.docker.com/r/wmhchallenge/pgs). | en_US |
dc.description.sponsorship | This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF)& funded by the Korean government (MSIT) (No. 2020M3E5D9080788) and 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: HI19C0745) and a grant of the National Institutes of Health grants (P41EB015922, U54EB020406, U19AG024904), BrightFocus (A2019052S). | en_US |
dc.language | en | en_US |
dc.publisher | Academic Press Inc. | en_US |
dc.source | 80841_이종민.pdf | - |
dc.subject | Deep learning | en_US |
dc.subject | Multi-scale highlighting foregrounds | en_US |
dc.subject | Segmentation | en_US |
dc.subject | U-Net | en_US |
dc.subject | White matter hyperintensities | en_US |
dc.title | White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds | en_US |
dc.type | Article | en_US |
dc.relation.volume | 237 | - |
dc.identifier.doi | 10.1016/j.neuroimage.2021.118140 | en_US |
dc.relation.journal | NeuroImage | - |
dc.contributor.googleauthor | Park, Gilsoon | - |
dc.contributor.googleauthor | Hong, Jinwoo | - |
dc.contributor.googleauthor | Duffy, Ben A. | - |
dc.contributor.googleauthor | Lee, Jong-Min | - |
dc.contributor.googleauthor | Kim, Hosung | - |
dc.sector.campus | S | - |
dc.sector.daehak | 공과대학 | - |
dc.sector.department | 바이오메디컬공학전공 | - |
dc.identifier.pid | ljm | - |
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