.png){kind=icon}
.png){kind=icon}
Return
Persistent homological cell tracking technology
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-023-37760-3.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we develop a cell tracking method based on persistent homological figure detection technology. We apply our tracking method to 9 different time-series cell images and extract several kinds of cell movements. Being able to analyze various images with a single method allows researchers to systematically understand and compare different tracking data. Persistent homological cell tracking technology's 9 parameters all have clear meanings. Thus, researchers can decide the parameters not by black box trial-and-error but by the purpose of their analysis. We use model data with ground truth to see our method's performance. We compare persistent homological figure detection and cell tracking technology with Image-Pro, sure-foreground in watershed method, and cell detection methods in previous studies. We see that there are some cases where Image-Pro's tracking stops and requires manual plots, while our method does not require manual plots. We show that our technology includes sure-foreground and has more information, and can be applied to different types of data that previously needed different methods. We also show that our technology is powerful as a detection technology by applying the technology to 5 different types of cell images.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.