Deep Learning for Tracking and Detection of Euglena Gracilis in Microfluidic Devices

カテゴリ: 研究会(論文単位)

論文No: BMS24014

グループ名: 【E】センサ・マイクロマシン部門 バイオ・マイクロシステム研究会

発行日: 2024/07/01

タイトル(英語): Deep Learning for Tracking and Detection of Euglena Gracilis in Microfluidic Devices

著者名: Iqbal Muhammad Ahtsham(Toyohashi University of Technology),Ono Ryoga(Toyohashi University of Technology),OKAMOTO Shunya (Toyohashi University of Technology),SHIBATA Takayuki (Toyohashi University of Technology ),NAGAI Moeto(Toyohashi University of Techno

著者名(英語): Muhammad Ahtsham Iqbal(Toyohashi University of Technology),Ryoga Ono(Toyohashi University of Technology),Shunya OKAMOTO(Toyohashi University of Technology), Takayuki SHIBATA(Toyohashi University of Technology ),Moeto NAGAI(Toyohashi University of Technology)

キーワード: Microrobots |Euglena Gracilis|Phototaxis|Microrobots |Euglena Gracilis|Phototaxis

要約(日本語): Microscopic robots made from biocompatible materials are gaining significant traction in various scientific disciplines. These microrobots hold promise for minimally invasive therapeutic interventions and targeted drug delivery. One such promising candidate is the microorganism Euglena gracilis. Euglena's inherent phototaxis, its ability to respond to specific wavelengths of light, makes it a natural microrobot amenable to external manipulation. When navigating Euglena inside microfluidic devices, precise control of density becomes critical. To automate the detection, tracking, and analysis of these microrobots, we implemented an AI model trained on data collected from various experiments. This approach facilitates the automated computation of their key numerical properties.

要約(英語): Microscopic robots made from biocompatible materials are gaining significant traction in various scientific disciplines. These microrobots hold promise for minimally invasive therapeutic interventions and targeted drug delivery. One such promising candidate is the microorganism Euglena gracilis. Euglena's inherent phototaxis, its ability to respond to specific wavelengths of light, makes it a natural microrobot amenable to external manipulation. When navigating Euglena inside microfluidic devices, precise control of density becomes critical. To automate the detection, tracking, and analysis of these microrobots, we implemented an AI model trained on data collected from various experiments. This approach facilitates the automated computation of their key numerical properties.

本誌: 2024年7月4日-2024年7月5日バイオ・マイクロシステム研究会

本誌掲載ページ: 19-22 p

原稿種別: 英語

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