Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells by Deep Learning for Cell Center Detection using Three-Dimensional Confocal Fluorescence Microscopy Images-Reference-Cited by-同舟云学术

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Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells by Deep Learning for Cell Center Detection using Three-Dimensional Confocal Fluorescence Microscopy Images

Published:2023-07-24 Issue: Volume: Page:
ISSN:
Container-title:2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Chu Slo-Li¹,Yokota Hideo²,Hsieh Hao-Lun³,Abe Kuniya⁴,Cho Dooseon⁴,Tsai Ming-Dar¹

Affiliation:

1. Chung-Yuan Christian University,Department of Information and Computer Engineering,Taoyuan,Taiwan,32023

2. RIKEN,Center for Advanced Photonics,Saitama,Japan,351-0198

3. Chung-Yuan Christian University,Master Program in Department of Information and Computer Engineering

4. RIKEN,BioResource Research Center,Ibaraki,Japan,305-0074

Funder

Ministry of Science and Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10339936/10339939/10340148.pdf?arnumber=10340148

Reference6 articles.

1. Generation of a novel germline stem cell line expressing a germline-specific reporter in the mouse

2. Automatic three‐dimensional segmentation of mouse embryonic stem cell nuclei by utilising multiple channels of confocal fluorescence images

3. U-Net: deep learning for cell counting, detection, and morphometry

4. 3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis

5. U-net structures for segmentation of single mouse embryonic stem cells using three-dimensional confocal microscopy images

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning for quantifying spatial patterning and formation process of early differentiated human‐induced pluripotent stem cells with micropattern images;Journal of Microscopy;2024-07-12

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