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Radiation-induced Impacts on Cell Adhesion and Its Cell Cycle Dependence
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| Title: | Radiation-induced Impacts on Cell Adhesion and Its Cell Cycle Dependence |
| Other Titles: | X線被ばく後の細胞接着面積の変化と細胞周期依存性 : 深層学習に基づく画像解析の放射線生物学への応用 |
| Authors: | Seino, Ryosuke1 Browse this author | | Fukunaga, Hisanori2 Browse this author →KAKEN DB |
| Authors(alt): | 清野, 良輔1 | | 福永, 久典2 |
| Keywords: | cell adhesion | | cell cycle | | Cellpose | | deep learning | | time lapse imaging |
| Issue Date: | 15-Mar-2024 |
| Publisher: | Japan Radioisotope Association |
| Journal Title: | RADIOISOTOPES |
| Volume: | 73 |
| Issue: | 1 |
| Start Page: | 61 |
| End Page: | 67 |
| Publisher DOI: | 10.3769/radioisotopes.73.61 |
| Abstract: | In recent years, the use of artificial intelligence in imaging analysis has become increasingly popular. In particular, algorithms based on deep learning, a type of machine learning, are considered promising tools. In this study, we used Cellpose 2.0, a cell segmentation algorithm based on deep learning, to analyze changes in cell adhesion following exposure to X-rays in synchronous HeLa cells. We found that the cell adhesion area of G1-phase cells increased after irradiation, while that of G2-phase cells decreased. | | 近年，深層学習を用いた画像解析が注目されている。放射線生物学分野への応用可能性の検討を目的として，本研究では深層学習に基づく細胞セグメンテーションアルゴリズムCellpose 2.0を用いて，細胞周期を同調させたヒト子宮頸がん由来HeLa細胞において放射線被ばく後の細胞接着面積の変化を詳細に解析した。その結果，G1期細胞ではX線被ばく後に細胞接着面積が増加するのに対し，G2期細胞では減少することを新たに見出した。今後，このような人工知能利用による放射線生物学研究のさらなる進展が期待される。 |
| Rights: | https://creativecommons.org/licenses/by/4.0/ |
| Type: | article |
| URI: | http://hdl.handle.net/2115/92426 |
| Appears in Collections: | 保健科学院・保健科学研究院 (Graduate School of Health Sciences / Faculty of Health Sciences) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 福永 久典
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