Intelligent software for the automatic detection and classification of CTCs in liquid-biopsy samples. The solution uses advanced deep-learning models based on multi-channel convolutional neural networks, capable of analysing microscopy images previously processed by RUBYchip®, a device specialised in isolating CTCs.
By jointly analysing different antigens using fluorescent staining, the software can automatically identify and classify these cells into three main phenotypes: E-CTC, M-CTC and EMT-CTC.
Automatic classification of CTCs in liquid biopsy
The software’s main application is the automatic detection and classification of circulating tumour cells in liquid-biopsy samples. To do this, it analyses microscopy images previously processed by RUBYchip® and identifies different antigens using fluorescent staining. This analysis makes it possible to classify CTCs according to the E-CTC, M-CTC and EMT-CTC phenotypes.
AI-based automation of cellular analysis
We automate the detection and classification of CTCs from microscopy images. Using deep-learning models and multi-channel convolutional neural networks, the software can analyse different fluorescent stains and classify cells according to the three main phenotypes defined.
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