Deep Learning in Cell Image Analysis

Xu, Junde; Zhou, Donghua; Deng, Danruo; Li, Jingpeng; Chen, Cheng; Liao, Xiangyun; Chen, Guangyong; Heng, Pheng‐Ann

doi:10.34133/2022/9861263

Cited by 11 publications

(5 citation statements)

References 144 publications

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“…The combination of image and deep learning models has made significant advancements in the field of biomedical research. , In this study, ViT is used as the default model for conducting classification tasks utilizing multiomics images. We demonstrate that ViT achieves better performance compared to other image classifiers.…”

Section: Methodsmentioning

confidence: 99%

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery

Zhang,

Mou,

et al. 2024

J. Chem. Inf. Model.

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In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.

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Section: Methodsmentioning

confidence: 99%

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery

Zhang,

Mou,

et al. 2024

J. Chem. Inf. Model.

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“…While these previous techniques are still applicable and useful in conjunction with newer methods, the incorporation of deep neural networks has been able to overcome common issues such as heterogenous distribution of the fluorescent markers for watershed and uniform shapes making masking difficult. Deep learning-based methods are able to utilize large hand annotated data sets to accurately segment cells without relying on predefined shapes, distinct cell boundaries, or an evenly distributed cell culture with minimal clustering [32][33][34][35][36]. 3D segmentation of sub-organelles within islets has allowed for more definitive quantification of intracellular interactions [37,38].…”

Section: Introductionmentioning

confidence: 99%

BetaBuddy: An automated end-to-end computer vision pipeline for analysis of calcium fluorescence dynamics in β-cells

Alsup,

Fowlds,

Cho

et al. 2024

PLoS ONE

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Insulin secretion from pancreatic β-cells is integral in maintaining the delicate equilibrium of blood glucose levels. Calcium is known to be a key regulator and triggers the release of insulin. This sub-cellular process can be monitored and tracked through live-cell imaging and subsequent cell segmentation, registration, tracking, and analysis of the calcium level in each cell. Current methods of analysis typically require the manual outlining of β-cells, involve multiple software packages, and necessitate multiple researchers—all of which tend to introduce biases. Utilizing deep learning algorithms, we have therefore created a pipeline to automatically segment and track thousands of cells, which greatly reduces the time required to gather and analyze a large number of sub-cellular images and improve accuracy. Tracking cells over a time-series image stack also allows researchers to isolate specific calcium spiking patterns and spatially identify those of interest, creating an efficient and user-friendly analysis tool. Using our automated pipeline, a previous dataset used to evaluate changes in calcium spiking activity in β-cells post-electric field stimulation was reanalyzed. Changes in spiking activity were found to be underestimated previously with manual segmentation. Moreover, the machine learning pipeline provides a powerful and rapid computational approach to examine, for example, how calcium signaling is regulated by intracellular interactions.

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“…While these previous techniques are still applicable and useful in conjunction with newer methods, the incorporation of deep neural networks has been able to overcome common issues such as heterogenous distribution of the fluorescent markers for watershed and uniform shapes making masking difficult. Deep learning-based methods are able to utilize large hand annotated data sets to accurately segment cells without relying on predefined shapes, distinct cell boundaries, or an evenly distributed cell culture with minimal clustering [34][35][36][37][38]. More novel deep learning networks, such as Cellpose, aim to create generalist models that increase flexibility of image analysis by training with a variety of cell types.…”

Section: Introductionmentioning

confidence: 99%

BetaBuddy: An end-to-end computer vision pipeline for the automated analysis of insulin secreting β-cells

Fowlds

Cho

et al. 2023

Preprint

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Insulin secretion from pancreatic β-cells is integral in maintaining the delicate equilibrium of blood glucose levels. Calcium is known to be a key regulator and triggers the release of insulin. This sub-cellular process can be monitored and tracked through live-cell imaging and subsequent cell segmentation, registration, tracking, and analysis of the calcium level in each cell. Current methods of analysis typically require the manual outlining of β-cells, involve multiple software packages, and necessitate multiple researchers - all of which tend to introduce biases. Utilizing deep learning algorithms, we have therefore created a pipeline to automatically segment and track thousands of cells, which greatly reduces the time required to gather and analyze a large number of sub-cellular images and improve accuracy. Tracking cells over a time-series image stack also allows researchers to isolate specific calcium spiking patterns and spatially identify those of interest, creating an efficient and user-friendly analysis tool. Using our automated pipeline, a previous dataset used to evaluate changes in calcium spiking activity in β-cells post-electric field stimulation was reanalyzed. Changes in spiking activity were found to be underestimated previously with manual segmentation. Moreover, the machine learning pipeline provides a powerful and rapid computational approach to examine, for example, how calcium signaling is regulated by intracellular interactions in a cluster of β-cells.

show abstract

Deep Learning in Cell Image Analysis

Cited by 11 publications

References 144 publications

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery

BetaBuddy: An automated end-to-end computer vision pipeline for analysis of calcium fluorescence dynamics in β-cells

BetaBuddy: An end-to-end computer vision pipeline for the automated analysis of insulin secreting β-cells

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