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

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

doi:10.1016/j.patter.2021.100367

Cited by 5 publications

(4 citation statements)

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“…Newly published work on efficient and automated multicellular patterns identification 27 holds promise for broad applicability but currently relies on its application to simulated data. In 28 , the authors incorporated persistence landscapes into a microscopy image analysis pipeline (TDAExplore) for detecting changes in the architecture of actin cytoskeleton. While this tool is accessible and broadly applicable, it is limited to microscopy images with a single channel.…”

Section: Introductionmentioning

confidence: 99%

Topological data analysis of pattern formation of human induced pluripotent stem cell colonies

Hartsock,

Park,

Toppen

et al. 2024

Preprint

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Understanding the multicellular organization of stem cells is vital for determining the mechanisms that coordinate cell fate decision-making during differentiation; these mechanisms range from neighbor-to-neighbor communication to tissue-level biochemical gradients. Current methods for quantifying multicellular patterning cannot capture the spatial properties of cell colonies across all scales and typically rely on human annotation or a priori selection of parameters. We present a computational pipeline that utilizes topological data analysis to generate quantitative, multiscale descriptors which capture the shape of data extracted from multichannel microscopy images. By applying our pipeline to certain stem cell colonies, we detected subtle differences in patterning that reflect distinct biological markers and progressive stages of differentiation. These results yield insight into directed cellular movement and morphogen-mediated, neighbor-to-neighbor signaling. Because of its broad applicability to immunofluorescence microscopy images, our pipeline is well-positioned to serve as a general-purpose tool for the quantitative study of multicellular pattern formation.

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Section: Introductionmentioning

confidence: 99%

Topological data analysis of pattern formation of human induced pluripotent stem cell colonies

Hartsock,

Park,

Toppen

et al. 2024

Preprint

Self Cite

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“…For example, the persistent image (PI) feature proposed by [24] has been utilised in hepatic tumour classification with considerable accuracy [25,26]. Meanwhile, the persistent entropy (PE) and p-norm features were applied for dark soliton detection [27] and persistent landscapes (PL) in the quantitative analysis of fluorescence microscopy images [28]. Furthermore, the authors of [29] employed Betti numbers for evaluating tumour heterogeneity in image feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Persistent Homology-Based Machine Learning Method for Filtering and Classifying Mammographic Microcalcification Images in Early Cancer Detection

Malek

Alias

Razak

et al. 2023

Cancers

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Microcalcifications in mammogram images are primary indicators for detecting the early stages of breast cancer. However, dense tissues and noise in the images make it challenging to classify the microcalcifications. Currently, preprocessing procedures such as noise removal techniques are applied directly on the images, which may produce a blurry effect and loss of image details. Further, most of the features used in classification models focus on local information of the images and are often burdened with details, resulting in data complexity. This research proposed a filtering and feature extraction technique using persistent homology (PH), a powerful mathematical tool used to study the structure of complex datasets and patterns. The filtering process is not performed directly on the image matrix but through the diagrams arising from PH. These diagrams will enable us to distinguish prominent characteristics of the image from noise. The filtered diagrams are then vectorised using PH features. Supervised machine learning models are trained on the MIAS and DDSM datasets to evaluate the extracted features’ efficacy in discriminating between benign and malignant classes and to obtain the optimal filtering level. This study reveals that appropriate PH filtering levels and features can improve classification accuracy in early cancer detection.

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“… Chen et al (2019) leveraged topological information to regularize the topological complexity of kernel classifiers by incorporating a topological penalty, while Pokorny, Hawasly & Ramamoorthy (2014) used topological approaches to improve trajectory classification in robotics. Edwards et al (2021) introduced TDAExplore, a machine learning image analysis pipeline based on topological data analysis. TDAExplore can be used to classify high-resolution images and characterize which image regions contribute to classification.…”

Section: Introductionmentioning

confidence: 99%

Persistent homology classification algorithm

Lara

2023

PeerJ Computer Science

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Data classification is an important aspect of machine learning, as it is utilized to solve issues in a wide variety of contexts. There are numerous classifiers, but there is no single best-performing classifier for all types of data, as the no free lunch theorem implies. Topological data analysis is an emerging topic concerned with the shape of data. One of the key tools in this field for analyzing the shape or topological properties of a dataset is persistent homology, an algebraic topology-based method for estimating the topological features of a space of points that persists across several resolutions. This study proposes a supervised learning classification algorithm that makes use of persistent homology between training data classes in the form of persistence diagrams to predict the output category of new observations. Validation of the developed algorithm was performed on real-world and synthetic datasets. The performance of the proposed classification algorithm on these datasets was compared to that of the most widely used classifiers. Validation runs demonstrated that the proposed persistent homology classification algorithm performed at par if not better than the majority of classifiers considered.

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

Cited by 5 publications

References 39 publications

Topological data analysis of pattern formation of human induced pluripotent stem cell colonies

Topological data analysis of pattern formation of human induced pluripotent stem cell colonies

Persistent Homology-Based Machine Learning Method for Filtering and Classifying Mammographic Microcalcification Images in Early Cancer Detection

Persistent homology classification algorithm

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