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Analysis of Multidimensional Microscopy Data Using Cell-ACDC
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DOI：10.3791/68954.png)
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Abstract
En 中文
Recent advances in quantitative microscopy for the life sciences have enabled experimental biologists to probe cells with unprecedented resolution and speed. At the same time, the AI revolution has dramatically increased the amount of information that can be extracted from multidimensional microscopy data. However, the large amount of data generated and the complexity of state-of-the-art AI models pose a severe bottleneck at the image analysis stage. Cell-ACDC is an open-source, user-friendly software that provides a powerful end-to-end solution for segmentation, tracking, and quantitative analysis of single cells in multidimensional microscopy data. It is tailored for experimental biologists who may lack the advanced technical expertise required to implement such models. This article shows how to utilize the framework to easily leverage the most recent models, along with many tools for smart and semi-automated data correction, to maximize the amount of biological information obtainable. CellACDC supports multi-channel, time-lapse, and z-stack microscopy data, and provides a dedicated toolset tailored to each type of data dimensionality. Because of its modular design, which allows new models to be seamlessly integrated and directly accessed by biologists, Cell-ACDC has the potential to serve as a reference tool for microscopy data analysis.
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