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DC Field | Value | Language |
---|---|---|
dc.citation.conferencePlace | SP | - |
dc.citation.conferencePlace | Granada | - |
dc.citation.endPage | 343 | - |
dc.citation.startPage | 335 | - |
dc.citation.title | International Conference on Medical Image Computing and Computer Assisted Interventions | - |
dc.contributor.author | Lee, Gyuhyun | - |
dc.contributor.author | Oh, Jeong-Woo | - |
dc.contributor.author | Kang, Mi-Sun | - |
dc.contributor.author | Her, Nam-Gu | - |
dc.contributor.author | Kim, Myoung-Hee | - |
dc.contributor.author | Jeong, Won-Ki | - |
dc.date.accessioned | 2024-02-01T01:36:16Z | - |
dc.date.available | 2024-02-01T01:36:16Z | - |
dc.date.created | 2018-11-26 | - |
dc.date.issued | 2018-09-16 | - |
dc.description.abstract | In this paper, we propose a novel image processing method, DeepHCS, to transform bright-field microscopy images into synthetic fluorescence images of cell nuclei biomarkers commonly used in high-content drug screening. The main motivation of the proposed work is to automatically generate virtual biomarker images from conventional bright-field images, which can greatly reduce time-consuming and laborious tissue preparation efforts and improve the throughput of the screening process. DeepHCS uses bright-field images and their corresponding cell nuclei staining (DAPI) fluorescence images as a set of image pairs to train a series of end-to-end deep convolutional neural networks. By leveraging a state-of-the-art deep learning method, the proposed method can produce synthetic fluorescence images comparable to real DAPI images with high accuracy. We demonstrate the efficacy of this method using a real glioblastoma drug screening dataset with various quality metrics, including PSNR, SSIM, cell viability correlation (CVC), the area under the curve (AUC), and the IC50. | - |
dc.identifier.bibliographicCitation | International Conference on Medical Image Computing and Computer Assisted Interventions, pp.335 - 343 | - |
dc.identifier.doi | 10.1007/978-3-030-00934-2_38 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.scopusid | 2-s2.0-85054101483 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/80920 | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007%2F978-3-030-00934-2_38 | - |
dc.language | 영어 | - |
dc.publisher | MICCAI 2018 | - |
dc.title | DeepHCS: Bright-field to fluorescence microscopy image conversion using deep learning for label-free high-content screening | - |
dc.type | Conference Paper | - |
dc.date.conferenceDate | 2018-09-16 | - |
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