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

Timothy W Hartman; Evgeni Radichev; Hafiz Munsub Ali; Mathew Olakunle Alaba; Mariah Hoffman; Gideon Kassa; Rajesh Sani; Venkata Gadhamshetty; Shankarachary Ragi; Shanta M Messerli; Pilar de la Puente; Eric S Sandhurst; Tuyen Do; Carol Lushbough; Etienne Z Gnimpieba

doi:10.1016/j.jmb.2022.167895

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

J Mol Biol. 2023 Jan 30;435(2):167895. doi: 10.1016/j.jmb.2022.167895. Epub 2022 Dec 1.

Authors

Affiliations

¹ Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States.
² Chemical and Biological Engineering Department, South Dakota School of Mines and Technology, 501 E St. Joseph Street, Rapid City, SD 57701, United States.
³ Civil and Environmental Engineering Department, South Dakota School of Mines and Technology, 501 E St. Joseph Street, Rapid City, SD 57701, United States.
⁴ Electrical Engineering Department, South Dakota School of Mines and Technology, 501 E St. Joseph Street, Rapid City, SD 57701, United States.
⁵ 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.
⁶ Cancer Biology and Immunotherapies Group, Sanford Research, 2301 E 60(th) Street North, Sioux Falls, SD 57104, United States.
⁷ 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.

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.

Keywords: FAIR; Microbe; biofilm; cancer; image comparison; machine learning; microscope imaging; segmentation; statistical analysis.

Published by Elsevier Ltd.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural

MeSH terms

Biofilms*
Biological Science Disciplines* / methods
Datasets as Topic
Image Processing, Computer-Assisted* / methods
Machine Learning*
Software*
Workflow

Abstract

Publication types

MeSH terms

Grants and funding