Automatic tracking of multiple cell regions in 4D live-cell imaging data
| Project/Area Number |
15K16021
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Perceptual information processing
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| Research Institution | Kanazawa University |
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Principal Investigator |
Hirose Osamu 金沢大学, 電子情報学系, 助教 (30549671)
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| Research Collaborator |
Kawaguchi Shotaro 金沢大学
Tokunaga Terumasa 九州工業大学
Yoshida Ryo 統計数理研究所
Toyoshima Yu 東京大学
Teramoto Takayuki 九州大学
Kuge Sayuri 九州大学
Ishihara Takeshi 九州大学
Iino Yuichi 東京大学
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2016: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2015: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 物体追跡 / 粒子フィルタ / マルコフ確率場 / ベイズ推定 / 人工知能 / 機械学習 / 4Dライブセルイメージングデータ / ライブセルイメージングデータ / 時系列解析 |
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| Outline of Final Research Achievements |
In this research, we aimed at developing the methods for automating the detection and tracking of cell regions in 4D live-cell imaging data. Cells in 4D live-cell imaging data are often imaged as ellipsoidal shapes and are densely distributed. For this data, standard methods usually fail to automatic tracking because of cell-switching and coalescence of tracked positions. To address this issue, we utilized typical characteristics in 4D live-cell imaging data; movements of nearly-located cells are strongly correlated. By using the characteristics used as the information for predicting cells' positions, we succeeded to improve tracking performance drastically. The software developed in this research is being distributed on the project website.
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Report
(4 results)
Research Products
(8 results)
