Edwards P, Skruber K, Milićević N, Heidings JB, Read TA, Bubenik P, Vitriol EA. TDAExplore: Quantitative analysis of fluorescence microscopy images through topology-based machine learning.
Patterns 2021;
2:100367. [PMID:
34820649 PMCID:
PMC8600226 DOI:
10.1016/j.patter.2021.100367]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/31/2021] [Accepted: 09/20/2021] [Indexed: 11/02/2022]
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.
TDAExplore combines topological data analysis with machine learning classification
As few as 20–30 high-resolution images can be used to train TDAExplore models
TDAExplore is robust to different microscopy modes, dataset size, image features
TDAExplore quantifies where and how much each image resembles the training data
Traditional intensity-based measurements of fluorescent microscopy data limit its potential to reveal new information about its sample. Here, we present an image analysis pipeline called TDAExplore, which is based on topological data analysis and machine learning classification. In addition to being highly accurate in assigning images to their correct group, TDAExplore quantifies how much images resemble the training data and identifies which parts are different, an improvement over other machine learning models that do not permit insight into how classification tasks were made. The next steps for TDAExplore will be to expand its capabilities into three-dimensional, multivariate, and time series datasets. This work represents progress into a future where machine learning identifies and describes nuanced image features in ways that allow researchers to answer important biological questions and generate new hypotheses for future studies.
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