Related Experiment Video
Updated: Aug 19, 2025
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Published on: August 13, 2014
BASIN: A Semi-automatic Workflow, with Machine Learning Segmentation, for Objective Statistical Analysis of
Timothy W Hartman1, Evgeni Radichev1, Hafiz Munsub Ali1
1Biomedical Engineering Department, University of South Dakota Sioux Falls, 4800 N Career Avenue, Sioux Falls, SD 57107, United States.
None:
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.
