Images are used in many scientific domains as raw measurements. The raw image files are growing larger in size, number and complexity. The challenges of extracting measurements from the raw images are associated with the inability (a) to view content at multiple length scales, (b) to automate and characterize measurements, (c) to perform computations over big images, and (d) to reproduce all software-based operations. Computational science applied to image-based measurements of objects becomes critical in order to extract information about complex content from big images.
Biosciences and materials sciences are two out of many domain sciences that depend on microscopy image-based measurements. Research in stem cell therapies, regenerative medicine, bio-manufacturing, and materials genome depend on quantitative measurements from microscopy images. Existing image analysis methods have limited applicability, are not well-characterized, do not scale, and are hard to trace to replicate computations. Biological and materials science researchers and practitioners strive to conduct reproducible experiments to obtain statistically significant population-level statistics. However, their efforts are hindered by the lack of measurement methods and tools that operate on large and complex microscopy imagery.
NIST has formed an interdisciplinary team to enable scientific measurements from microscopy imagery. The focus of the interdisciplinary team is on scientific measurement workflows in the domains of cell biology and materials science through the research, development, and application of tools, techniques, and standards for:
NIST efforts have led to the development of an interactive measurement system for large scientific image data over the Web. Several biological and materials science image sets are publicly available at https://isg.nist.gov. The URL also serves as the first NIST prototype for disseminating big 2D or 3D reference images to the general public. In biology, the researched capability of visualizing, quantifying, and evaluating stem cell colonies from Gigapixel images and terabyte-sized microscopy videos enabled the discovery of rare cell colonies in terms of their time-dependent pluripotency states. In computational science, the work advanced metrology over big images in terms of automation, optimization, accuracy evaluation, web-based collaboration, and distributed computing.