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Image Cytometry

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An AI-based imaging flow cytometry approach to study erythrophagocytosis.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Erythrophagocytosis is a process consisting of recognition, engulfment and digestion by phagocytes of antibody-coated or damaged erythrocytes. Understanding the dynamics that are behind erythrophagocytosis is fundamental to comprehend this cellular p...

Single-detector multiplex imaging flow cytometry for cancer cell classification with deep learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry...

Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX.

Nature communications
The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (...

Artificial intelligence-based classification of peripheral blood nucleated cells using label-free imaging flow cytometry.

Lab on a chip
Label-free image identification of circulating rare cells, such as circulating tumor cells within peripheral blood nucleated cells (PBNCs), the vast majority of which are white blood cells (WBCs), remains challenging. We previously described developi...

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry.

Nature protocols
Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly ...

Graph of graphs analysis for multiplexed data with application to imaging mass cytometry.

PLoS computational biology
Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous prote...

Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers.

IEEE/ACM transactions on computational biology and bioinformatics
Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for a...

Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.

Lab on a chip
The association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols t...

Single-cell dispensing and 'real-time' cell classification using convolutional neural networks for higher efficiency in single-cell cloning.

Scientific reports
Single-cell dispensing for automated cell isolation of individual cells has gained increased attention in the biopharmaceutical industry, mainly for production of clonal cell lines. Here, machine learning for classification of cell images is applied ...

Deep Learning in Image Cytometry: A Review.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is ap...