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

ABSTRACT: Summary 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. Graphical abstract Highlights • 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 The bigger picture 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. Machine learning is exceptionally powerful at categorizing imaging data into different classes, yet most methods do not provide insight into how classification tasks were made. Here, Edwards et al. address this limitation with TDAExplore, an image analysis pipeline combining machine learning classification with topological data analysis. In addition to being able to accurately classify fluorescent microscopy data over a broad range of examples, TDAExplore quantifies where and how much images resemble the data that were used to train it.

SUBMITTER: Edwards P 

PROVIDER: S-EPMC8600226 | biostudies-literature |

REPOSITORIES: biostudies-literature