Abstract
Automated quantitative image analysis is essential for all fields of life science research. Although several software programs and algorithms have been developed for bioimage processing, an advanced knowledge of image processing techniques and high-performance computing resources are required to use them. Hence, we developed a cloud-based image analysis platform called IMACEL, which comprises morphological analysis and machine learning-based image classification. The unique click-based user interface of IMACEL’s morphological analysis platform enables researchers with limited resources to evaluate particles rapidly and quantitatively without prior knowledge of image processing. Because all the image processing and machine learning algorithms are performed on high-performance virtual machines, users can access the same analytical environment from anywhere. A validation study of the morphological analysis and image classification of IMACEL was performed. The results indicate that this platform is an accessible and potentially powerful tool for the quantitative evaluation of bioimages that will lower the barriers to life science research.
Highlights
Recent developments in microscopic and image processing technologies have led to new findings in the life sciences
We validated the morphological analysis of the IMACEL particle analyser by determining how similar its extracted features were to those of a manual evaluation
Manual evaluation by tracing each stress granule took approximately 16 h (Fig 2d, S2 Movie). These results indicate that the IMACEL particle analyser can evaluate the morphology of particles quantitatively and rapidly, with high accuracy
Summary
Recent developments in microscopic and image processing technologies have led to new findings in the life sciences. With the evolution of imaging devices, such as microscopes, MRI, and CT, image data in the life sciences are increasingly detailed. The development of visualisation techniques, such as the use of fluorescence microscopy and fluorescent probes, facilitate the analysis of biological structures and diversify molecular imaging. It is becoming critical to analyse these bioimage data efficiently and quickly in quantitative studies [1,2]. The analysis of large and detailed images is very laborious and time-consuming, and is a burden for researchers. In addition to advances in imaging devices, a variety of open source and commercial image analysis software
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