scholarly journals Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images

Cells ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2587
Author(s):  
Andrey V. Belashov ◽  
Anna A. Zhikhoreva ◽  
Tatiana N. Belyaeva ◽  
Anna V. Salova ◽  
Elena S. Kornilova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Lines ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Optical Parameters ◽  
Photodynamic Treatment ◽  
Phase Images ◽  
3T3 Cell Lines ◽  
Different Doses

In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses.

scholarly journals Deep learning classification of lipid droplets in quantitative phase images

2020 ◽  
Author(s):  
L. Sheneman ◽  
G. Stephanopoulos ◽  
A. E. Vasdekis
Keyword(s):  
Machine Learning ◽  
Lipid Droplets ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Learning Methods ◽  
Quantitative Phase ◽  
Machine Learning Methods ◽  
Phase Images

AbstractWe report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all machine learning methods that we implemented, and their performance in computational requirements, training resource needs, and accuracy. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity and deeper insight into the thermodynamics of metabolism of single cells.Author SummaryRecently, quantitative-phase imaging (QPI) has demonstrated the ability to elucidate novel parameters of cellular physiology and metabolism without the need for fluorescent staining. Here, we apply label-free, low photo-toxicity QPI to yeast cells in order to identify lipid droplets (LDs), an important organelle with key implications in human health and biofuel development. Because QPI yields low specificity, we explore the use of modern machine learning methods to rapidly identify intracellular LDs with high discriminatory power and accuracy. In recent years, machine learning has demonstrated exceptional abilities to recognize and segment objects in biomedical imaging, remote sensing, and other areas. Trained machine learning classifiers can be combined with QPI within high-throughput analysis pipelines, allowing for efficient and accurate identification and quantification of cellular components. Non-invasive, accurate and high-throughput classification of these organelles will accelerate research and improve our understanding of cellular functions with beneficial applications in biofuels, biomedicine, and more.

scholarly journals Machine Learning-Supported Analyses Improve Quantitative Histological Assessments of Amyloid-β Deposits and Activated Microglia

2020 ◽  
pp. 1-9
Author(s):  
Pablo Bascuñana ◽  
Mirjam Brackhan ◽  
Jens Pahnke
Keyword(s):  
Machine Learning ◽  
Traditional Approach ◽  
Amyloid Β ◽  
Cell Types ◽  
Current Standard ◽  
Activated Microglia ◽  
Total Analysis ◽  
Quantitative Results ◽  
Whole Slide Images

Background: Detailed pathology analysis and morphological quantification is tedious, prone to errors. Automatic image analysis can help to increase objectivity and reduce time. Here, we present the evaluation of the DeePathology STUDIOTM for automatic analysis of histological whole-slide images using machine learning/artificial intelligence. Objective: To evaluate and validate the use of DeePathology STUDIO for the analysis of histological slides at high resolution. Methods: We compared the DeePathology STUDIO and our current standard method using macros in AxioVision for the analysis of amyloid-β (Aβ) plaques and microglia in APP-transgenic mice at different ages. We analyzed density variables and total time invested with each approach. In addition, we correlated Aβ concentration in brain tissue measured by ELISA with the results of Aβ staining analysis. Results: DeePathology STUDIO showed a significant decrease of the time for establishing new analyses and the total analysis time by up to 90% . On the other hand, both approaches showed similar quantitative results in plaque and activated microglia density in the different experimental groups. DeePathology STUDIO showed higher sensitivity and accuracy for small-sized plaques. In addition, DeePathology STUDIO allowed the classification of plaques in diffuse- and dense-packed, which was not possible with our traditional analysis. Conclusion: DeePathology STUDIO reduced substantially the effort needed for a new analysis showing comparable quantitative results to the traditional approach. In addition, it allowed including different objects (categories) or cell types in a single analysis, which is not possible with conventional methods.

scholarly journals In situ classification of cell types in human kidney tissue using 3D nuclear staining

2020 ◽  
Author(s):  
Andre Woloshuk ◽  
Suraj Khochare ◽  
Aljohara Fahad Almulhim ◽  
Andrew McNutt ◽  
Dawson Dean ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Cell Types ◽  
Learning Approach ◽  
Cell Type ◽  
Cell Classification

AbstractTo understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational structure is tightly linked to key physiological functions. Advances in imaging-based cell classification may be limited by the need to incorporate specific markers that can link classification to function. Multiplex imaging can mitigate these limitations, but requires cumulative incorporation of markers, which may lead to tissue exhaustion. Furthermore, the application of such strategies in large scale 3-dimensional (3D) imaging is challenging. Here, we propose that 3D nuclear signatures from a DNA stain, DAPI, which could be incorporated in most experimental imaging, can be used for classifying cells in intact human kidney tissue. We developed an unsupervised approach that uses 3D tissue cytometry to generate a large training dataset of nuclei images (NephNuc), where each nucleus is associated with a cell type label. We then devised various supervised machine learning approaches for kidney cell classification and demonstrated that a deep learning approach outperforms classical machine learning or shape-based classifiers. Specifically, a custom 3D convolutional neural network (NephNet3D) trained on nuclei image volumes achieved a balanced accuracy of 80.26%. Importantly, integrating NephNet3D classification with tissue cytometry allowed in situ visualization of cell type classifications in kidney tissue. In conclusion, we present a tissue cytometry and deep learning approach for in situ classification of cell types in human kidney tissue using only a DNA stain. This methodology is generalizable to other tissues and has potential advantages on tissue economy and non-exhaustive classification of different cell types.

scholarly journals Review of Machine Learning Applications and Datasets in Classification of Acute Leukemia

2021 ◽  
Vol 11 (6) ◽  
pp. 1-9
Author(s):  
Jaishree Ranganathan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Acute Leukemia ◽  
Blood Cells ◽  
White Blood Cells ◽  
Cell Types ◽  
Research Community ◽  
Machine Learning Applications ◽  
Different Sources

Cancer is an extremely heterogenous disease. Leukemia is a cancer of the white blood cells and some other cell types. Diagnosing leukemia is laborious in a multitude of areas including heamatology. Machine Learning (ML) is the branch of Artificial Intelligence. There is an emerging trend in ML models for data classification. This review aimed to describe the literature of ML in the classification of datasets for acute leukemia. In addition to describing the existing literature, this work aims to identify different sources of publicly available data that could be utilised for research and development of intelligent machine learning applications for classification. To best of the knowledge there is no such work that contributes such information to the research community.

scholarly journals Review of Machine Learning Applications and Datasets in Classification of Acute Leukemia

2021 ◽  
Vol 11 (6) ◽  
pp. 1-9
Author(s):  
Jaishree Ranganathan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Acute Leukemia ◽  
Blood Cells ◽  
White Blood Cells ◽  
Cell Types ◽  
Research Community ◽  
Machine Learning Applications ◽  
Different Sources

Cancer is an extremely heterogenous disease. Leukemia is a cancer of the white blood cells and some other cell types. Diagnosing leukemia is laborious in a multitude of areas including heamatology. Machine Learning (ML) is the branch of Artificial Intelligence. There is an emerging trend in ML models for data classification. This review aimed to describe the literature of ML in the classification of datasets for acute leukemia. In addition to describing the existing literature, this work aims to identify different sources of publicly available data that could be utilised for research and development of intelligent machine learning applications for classification. To best of the knowledge there is no such work that contributes such information to the research community.

scholarly journals Deep learning classification of lipid droplets in quantitative phase images

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249196
Author(s):  
Luke Sheneman ◽  
Gregory Stephanopoulos ◽  
Andreas E. Vasdekis
Keyword(s):  
Machine Learning ◽  
Lipid Droplets ◽  
Supervised Machine Learning ◽  
Label Free ◽  
Learning Methods ◽  
Method Performance ◽  
Quantitative Phase ◽  
Machine Learning Methods ◽  
Phase Images

We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells.

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