The demand for interdisciplinary research is on the rise.
Researchers are slowly turning into routine workers from having to handle the laborious tasks derived from analyzing a high volume of images.
There are limited opportunities for researchers to acquire the skills to properly analyze images. The lack of this skill is lowering the overall efficiency in the research space.
Extensive knowledge on image analysis is not a requirement for using IMACEL. The processed images generated by IMACEL minimizes the complexities involved in extracting data from your images.
IMACEL learns all of the processing procedures. The same procedures can be subsequently applied to several images, making it possible to conduct a batch analysis for the same type of image.
Analysis accuracy is improved with repeated usage as the AI accumulates the data acquired from the analysis procedure, and learns the steps required to analyze the images.
Researchers can learn the basics of image processing as the detailed processes are displayed below the images. Advice on imaging techniques and analysis is also provided by our team of expert image analysts.
The cloud-based platform allows users to use the newest classifier from anywhere, regardless of the OS.
IMACEL is built on Microsoft Azure, meaning that users can be ensured that all of their important image data will be protected.
* All data is encrypted used 256-bit AES encryption.
The combination of unique algorithms, conventional image processing techniques and AI allows for instant calculation of the total cell count.
By simply preparing the image dataset to train IMACEL, the AI will automatically classify the images based on the features of the images.
Gain access to a wide variety of quantitative information from your images, including the area, perimeter, centroid (x and y coordinates), major and minor axis, aspect ratio, rotation angle, circularity, degree of circularity, solidity and intensity.
All of the procedures applied during analysis are summarized in a PDF document, enhancing the traceability of the information acquired from your images. Users also have access to the document that compares the statistical data of the analysis results.
Towards a Productive Environment for Researchers
Dr. Shimahara completed his graduate studies and Ph.D. at the Graduate School of Frontier Sciences at the University of Tokyo. During the course of his studies, Yuki’s research topics focused on artificial photosynthesis and cell organelle image analysis and simulation. Yuki continued to expand his professional profile by taking roles in business strategy and global business development at two leading IT firms.