深度学习
管道(软件)
人工智能
瓶颈
计算机科学
分割
计算机视觉
跟踪(教育)
斑马鱼
软件
球体
图像分割
模式识别(心理学)
生物
细胞培养
嵌入式系统
心理学
程序设计语言
教育学
基因
生物化学
遗传学
作者
Chentao Wen,Takuya Miura,Venkatakaushik Voleti,Kazushi Yamaguchi,Motosuke Tsutsumi,Kiyohiko Yamamoto,Kohei Otomo,Yujiro Fujie,Takayuki Teramoto,Takeshi Ishihara,Kazuhiro Aoki,Tomomi Nemoto,Elizabeth M. C. Hillman,Koutarou D. Kimura
出处
期刊:eLife
[eLife Sciences Publications, Ltd.]
日期:2021-03-30
卷期号:10
被引量:41
摘要
Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and ‘straightened’ freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90–100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze.
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