Image ExFluorer

Image ExFluorer-AI

High-contents Live Cell Imaging System

Cell biologists are increasingly seeking techniques to perform live-cell imaging experiments. However, it is difficult to work on each cell image. Image ExFluorer proposes AI technology that facilitates data analysis for researchers at the forefront of the bio industry. Image processing, which was previously difficult or impossible, will provide researchers with customized solutions and convenience with more advanced technologies through AI system. Comprehensive cell recognition will reduce your time by applying various tasks collectively.


Cells Analysis with AI

Many researchers spend a lot of time analyzing cells. As separate standard is established for each image, it takes a lot of time, and the data varies depending on the operator’s proficiency. However, AI system enables integrated processing, allowing more sophisticated and faster processing of images.

– Image Clarification : It is a technology that finds the origin of fluorescence through a pre-trained algorithm and makes to clear the phenomenon of blurring in addition to Cell Signal, one of the disadvantages of existing wild-field microscopes. This greatly helps to effectively control the blurry image of fluorescent images to implement the shape of real cells.

– Image Restoration : Many researchers are worried about cell damage caused by strong light. Some cells are extremely sensitive to phototoxicity and need to be photographed as quickly as possible. However, it effectively restores images that have been compromised due to a small amount of light. This can be applied effectively in sensitive cells.

– Image Prediction : By recognizing certain forms appearing on two different images (e.g., fluorescent, BF), we can train to predict (fluorescent) different images (BF) through one image (fluorescent). Through this, researchers can get away from concerns about cell damage caused by phototoxicity such as UV, and allows researchers to obtain various data.

– Image Segmentation : It is very important that computers recognize the cell areas. This is because various data can be obtained by designating regions. If there is not much difference between the cell signal and background value, it is not easy to obtain the desired data if the criterion is given as a traditional threshold value. However, using this feature will enable researchers to learn the areas they want to use on the computer, and will accurately set up the cell region through various criteria. Researchers can do a batch of integrated processing without having to work on multiple images of the same cells one by one.

– General Analysis 3(GA3) : GA3 creates a playground for integrated process from imaging to analysis and data management. Users can build algorithms that fit their user objectivity by linking desired functions to the image. Through these tasks, a number of images can be performed in an integrated manner according to the specified algorithm.