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Software Tools

The Biosystems and Biomaterials Division (BBD) develops measurement technologies to bolster the bioeconomy and foster innovation. To support these efforts, BBD staff have developed open-source software tools to address critical vacancies in the available analysis landscape across a broad portfolio of activities. Note that the software products listed below are not considered Standard Reference products and provided as is. Please see the NIST Software Disclamer statement for additional information.

IMAGE ANALYSIS

Image Name Description Contact
DiameterJ thumbnail: SEM of polymer nanofibers
DiameterJ DiameterJ is a validated nanofiber diameter characterization tool for use in ImageJ [1/2] and Fiji. DiameterJ is able to analyze an image and find the diameter of nanofibers or microfibers at every pixel along a fibers axis and produces a histogram of these diameters. Included with this histogram are summary statistics such as mean fiber diameter and most occurring fiber diameter. Simon
Optical absorption pseudocolor image of RPE cells
SQuIRE, CARPE  CARPE is an ImageJ plugin to convert bright-field microscope images into absorbance images. SQuIRE is a micromanager plugin for automated acquisition of the images. Simon
BCARS Pseudocolor image of murine vaginal tissue
CRIKit2 CRIKit2 is a Python library for processing hyperspectral imagery, primarily from coherent anti-Stokes Raman scattering (CARS) microscopy systems. This library contains command line tools, interactive widgets, and a user interface. Camp
Benchmarking2

Benchmarking script

(uManager)

NIST developed a procedure for characterizing the performance of a fluorescence microscope by benchmarking the detection threshold, saturation, and linear dynamic range to a physical artifact, such as a fluorescent material. This method has already been implemented by several universities and companies, and is proven to improve the reproducibility of wide-field fluorescence imaging. Our strategy adds value to image data repositories by benchmarking the conditions under which the images were acquired. Halter
WIPP
WIPP Several advanced microscopes have the capability to acquire overlapping image tiles automatically. Such automated acquisitions provide a way to image a large spatial coverage of a specimen, to take measurements at multiple physical length scales, and to learn the underlying models that describe the specimen. There is a need to assist imaging scientists with computational solutions that convert raw image tiles to calibrated, stitched, and viewable Giga-pixel images with interactive and traceable measurements and modeling tools. In other words, a need for a solution to go From Image Tiles to Web-Based Traceable Measurements and Interactive Modeling in One Stop (denoted as web image pipeline for interactive discoveries). Halter
MIST
MIST Microscopy Image Stitching Tool (MIST), is a stitching tool for 2D grids of images. MIST estimates the stage mechanical model (actuator backlash, stage repeatability ‘r’, etc.) from computed pairwise translations and then minimizes stitching errors by optimizing the translations within a (4r)^2 square area. This minimizes the maximum uncertainty related to the translation computation for any pair of images. Halter
EGT
EGT EGT is a novel and empirically derived image gradient threshold selection method for separating foreground and background pixels in an image. The method works across multiple optical microscopy imaging modalities and cell lines. Halter
Fogbank
Fogbank FogBank separates cells when they are confluent and touching each other. The separation of touching cells in microscopy images is critical for the counting, identification and measurement of individual cells. Halter

Genomic analysis

Image Name Description Contact
GIAB3
GIAB Integration Pipeline Genome In A Bottle small and structural variant integration pipeline for generating GIAB benchmark sets. Olson
  metagenomeFeatures R packages for working with 16S rRNA databases. Olson

MACHINE LEARNING

Image Name Description Contact
pyMCR Contration Retrieval Thumbnail
pyMCR pyMCR is a small Python 3 package for performing multivariate curve resolution. Currently, it implements a simple alternating regression scheme (MCR-AR). The most common implementation is with ordinary least-squares regression, MCR-ALS. Camp

DISCLAIMER

For more information concerning software licenses and use, please see the NIST Software Disclamer statement.

Created February 6, 2020, Updated March 23, 2020