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DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 83460 | - |
dc.citation.startPage | 83449 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 7 | - |
dc.contributor.author | Lee, Jimin | - |
dc.contributor.author | Kim, Hyejin | - |
dc.contributor.author | Cho, Hyungjoo | - |
dc.contributor.author | Jo, Youngju | - |
dc.contributor.author | Song, Yujin | - |
dc.contributor.author | Ahn, Daewoong | - |
dc.contributor.author | Lee, Kangwon | - |
dc.contributor.author | Park, Yongkeun | - |
dc.contributor.author | Ye, Sung-Joon | - |
dc.date.accessioned | 2023-12-21T19:06:48Z | - |
dc.date.available | 2023-12-21T19:06:48Z | - |
dc.date.created | 2021-03-04 | - |
dc.date.issued | 2019-06 | - |
dc.description.abstract | We proposed a method of label-free segmentation of cell nuclei by exploiting a deep learning (DL) framework. Over the years, fluorescent proteins and staining agents have been widely used to identify cell nuclei. However, the use of exogenous agents inevitably prevents from long-term imaging of live cells and rapid analysis and even interferes with intrinsic physiological conditions. Without any agents, the proposed method was applied to label-free optical diffraction tomography (ODT) of human breast cancer cells. A novel architecture with optimized training strategies was validated through cross-modality and cross-laboratory experiments. The nucleus volumes from the DL-based label-free ODT segmentation accurately agreed with those from fluorescent-based. Furthermore, the 4D cell nucleus segmentation was successfully performed for the time-lapse ODT images. The proposed method would bring out broad and immediate biomedical applications with our framework publicly available. | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.7, pp.83449 - 83460 | - |
dc.identifier.doi | 10.1109/ACCESS.2019.2924255 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.scopusid | 2-s2.0-85068664425 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/50107 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/8743437 | - |
dc.identifier.wosid | 000475476400001 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Deep-Learning-Based Label-Free Segmentation of Cell Nuclei in Time-Lapse Refractive Index Tomograms | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications | - |
dc.relation.journalResearchArea | Computer Science; Engineering; Telecommunications | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Cell nucleus segmentation | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | label-free segmentation | - |
dc.subject.keywordAuthor | optical diffraction tomography | - |
dc.subject.keywordAuthor | refractive index tomogram | - |
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