BASIN: A Semi-automatic Workflow, with Machine Learning Segmentation, for Objective Statistical Analysis of Biomedical and Biofilm Image Datasets.

Journal: Journal of molecular biology
Published Date: Dec 1, 2022

Abstract

Micrograph comparison remains useful in bioscience. This technology provides researchers with a quick snapshot of experimental conditions. But sometimes a two- condition comparison relies on researchers' eyes to draw conclusions. Our Bioimage Analysis, Statistic, and Comparison (BASIN) software provides an objective and reproducible comparison leveraging inferential statistics to bridge image data with other modalities. Users have access to machine learning-based object segmentation. BASIN provides several data points such as images' object counts, intensities, and areas. Hypothesis testing may also be performed. To improve BASIN's accessibility, we implemented it using R Shiny and provided both an online and offline version. We used BASIN to process 498 image pairs involving five bioscience topics. Our framework supported either direct claims or extrapolations 57% of the time. Analysis results were manually curated to determine BASIN's accuracy which was shown to be 78%. Additionally, each BASIN version's initial release shows an average 82% FAIR compliance score.

Authors

  • Timothy W Hartman
    Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States.
  • Evgeni Radichev
    Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States.
  • Hafiz Munsub Ali
    Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States.
  • Mathew Olakunle Alaba
    Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States.
  • Mariah Hoffman
    Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States.
  • Gideon Kassa
    Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States.
  • Rajesh Sani
    Chemical and Biological Engineering Department, South Dakota School of Mines and Technology, 501 E St. Joseph Street, Rapid City, SD 57701, United States.
  • Venkata Gadhamshetty
    Civil and Environmental Engineering Department, South Dakota School of Mines and Technology, 501 E St. Joseph Street, Rapid City, SD 57701, United States.
  • Shankarachary Ragi
    Electrical Engineering Department, South Dakota School of Mines and Technology, 501 E St. Joseph Street, Rapid City, SD 57701, United States.
  • Shanta M Messerli
    Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States; Cancer Biology and Immunotherapies Group, Sanford Research, 2301 E 60(th) Street North, Sioux Falls, SD 57104, United States; Department of Biology and Microbiology, South Dakota State University, Brookings, SD 57006, United States.
  • Pilar de la Puente
    Cancer Biology and Immunotherapies Group, Sanford Research, 2301 E 60(th) Street North, Sioux Falls, SD 57104, United States.
  • Eric S Sandhurst
    Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States.
  • Tuyen Do
    Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States.
  • Carol Lushbough
    Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States.
  • Etienne Z Gnimpieba
    Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States. Electronic address: Etienne.Gnimpieba@usd.edu.

Keywords

External Resources

Popular Topics
Recent Journals