TDAExplore: Quantitative analysis of fluorescence microscopy images through topology-based machine learning

Parker Edwards, Kristen Skruber, Nikola Milićević, James B. Heidings, Tracy Ann Read, Peter Bubenik, Eric A. Vitriol

Research output: Contribution to journalArticlepeer-review

Abstract

Recent advances in machine learning have greatly enhanced automatic methods to extract information from fluorescence microscopy data. However, current machine-learning-based models can require hundreds to thousands of images to train, and the most readily accessible models classify images without describing which parts of an image contributed to classification. Here, we introduce TDAExplore, a machine learning image analysis pipeline based on topological data analysis. It can classify different types of cellular perturbations after training with only 20–30 high-resolution images and performs robustly on images from multiple subjects and microscopy modes. Using only images and whole-image labels for training, TDAExplore provides quantitative, spatial information, characterizing which image regions contribute to classification. Computational requirements to train TDAExplore models are modest and a standard PC can perform training with minimal user input. TDAExplore is therefore an accessible, powerful option for obtaining quantitative information about imaging data in a wide variety of applications.

Original languageEnglish (US)
Article number100367
JournalPatterns
Volume2
Issue number11
DOIs
StatePublished - Nov 12 2021

Keywords

  • DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
  • actin cytoskeleton
  • fluorescence microscopy
  • image classification
  • image segmentation
  • machine learning
  • persistence landscapes
  • persistent homology
  • topological data analysis

ASJC Scopus subject areas

  • Decision Sciences(all)

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