@article{edwards_tdaexplore_2021, 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.}, author = {Edwards, Parker and Skruber, Kristen and Milićević, Nikola and Heidings, James B. and Read, Tracy-Ann and Bubenik, Peter and Vitriol, Eric A.}, date = {2021-11-12}, doi = {10.1016/j.patter.2021.100367}, issn = {2666-3899}, journaltitle = {Patterns}, keywords = {1 - Actin cytoskeleton, 1 - Biology, 1 - Image classification, 1 - Image segmentation, 2 - Machine learning, 2 - Persistence landscape, 2 - Persistent homology, 3 - Grayscale images}, langid = {english}, number = {11}, pages = {100367}, shortjournal = {Patterns}, shorttitle = {{TDAExplore}}, title = {{TDAExplore}: Quantitative analysis of fluorescence microscopy images through topology-based machine learning}, url = {https://www.sciencedirect.com/science/article/pii/S2666389921002294}, urldate = {2022-06-28}, volume = {2} }