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Quantitative Digital Microscopy with Deep Learning
[article]
2020
arXiv
pre-print
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We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. ...
Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. ...
ACKNOWLEDGMENTS The authors would like to thank Carlo Manzo for providing the experimental images used in the fourth case study, Jose Alvarez for designing the logo of DeepTrack 2.0, as well as the European ...
arXiv:2010.08260v1
fatcat:wo6iwmlvjfajdgvmj5nclqbph4
Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision
2016
BioMed Research International
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In this paper, inspired by the architecture of deep learning, we propose a robust feature learning method for constructing discriminative appearance models without large-scale pretraining. ...
deep learning representation architectures. ...
[56] utilize a level set-based method for cell tracking in time-lapse fluorescence microscopy. ...
doi:10.1155/2016/8182416
pmid:27689090
pmcid:PMC5015430
fatcat:j2gzxede4rhwlny245phzw6lya
Motion Analysis of Live Objects by Super-Resolution Fluorescence Microscopy
2012
Computational and Mathematical Methods in Medicine
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This paper briefly reviews the developments in this area mostly in the recent three years, especially for cellular analysis in fluorescence microscopy. ...
The review is useful to people in the related field for easy referral of the state of the art. ...
In [23] , eight tracking approaches are evaluated for testing real microscopy images of HIV-1 particles. ...
doi:10.1155/2012/859398
pmid:22162725
pmcid:PMC3227432
fatcat:pjlhy4ehbzfp5i3lhctqhwfvji
Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning
[article]
2019
bioRxiv
pre-print
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A Trackpy-based cell tracker with a graphical user interface is developed for cell tracking and data verification. The performance of Usiigaci is validated with electrotaxis of NIH/3T3 fibroblasts. ...
In this work, we introduce Usiigaci, an all-in-one, semi-automated pipeline to segment, track, and visualize cell movement and morphological changes in PCM. ...
Similar deep learning 298 methods for biomedical image analysis are used to accomplish in silico label-299 ing of cellular components instain-free images and 3D segmentation of noisy 300 medical images ...
doi:10.1101/524041
fatcat:ddseyqan3fbg7peqougubg277m
Manual and Automatic Image Analysis Segmentation Methods for Blood Flow Studies in Microchannels
2021
Micromachines
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For this purpose, the current methods used for tracking red blood cells (RBCs) flowing through a glass capillary and techniques to measure the cell-free layer thickness in different kinds of microchannels ...
In blood flow studies, image analysis plays an extremely important role to examine raw data obtained by high-speed video microscopy systems. ...
[70] developed a deep learning-based super-resolution ultrasound (DL-SRU) for particle tracking. The method is based on a convolutional neural network and deep ultrasound localization microscopy. ...
doi:10.3390/mi12030317
pmid:33803615
pmcid:PMC8002955
fatcat:anus2zawszfdlapr4f234j6he4
Cell tracking using deep neural networks with multi-task learning
2017
Image and Vision Computing
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The observation model is trained by building a CNN to learn robust cell features. The tracking procedure is started by assigning the cell position in the first frame of a microscope image sequence. ...
The proposed cell tracking method consists of a particle filter motion model, a multi-task learning observation model, and an optimized model update strategy. ...
[23] integrated the level sets method with the model evolution approach for cell tracking in time-lapse fluorescence microscopy. ...
doi:10.1016/j.imavis.2016.11.010
fatcat:uejk5falxzfqtkgzug4vx55kem
The role of convolutional neural networks in scanning probe microscopy: a review
2021
Beilstein Journal of Nanotechnology
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Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. ...
In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image ...
We further are deeply indebted to the reviewers of this manuscript for providing useful and comprehensive feedback. ...
doi:10.3762/bjnano.12.66
pmid:34476169
pmcid:PMC8372315
fatcat:dwl2mqxfjnanve363my7qr4try
TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data
2022
F1000Research
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Single particle tracking (SPT) is one of the most widely used tools in optical microscopy to evaluate particle mobility in a variety of situations, including cellular and model membrane dynamics. ...
underestimated by conventional analysis pipelines. we thus developed a Python library, under the name of TRAIT2D (Tracking Analysis Toolbox – 2D version), in order to track particle diffusion at high ...
The paper in its current form is acceptable for indexing, but it would enhance the reading experience for the reader with few well-chosen image examples of how the different components in the code improve ...
doi:10.12688/f1000research.54788.2
fatcat:ep72vtc2ezac5lfdifafd67kwe
Deep learning models for lipid-nanoparticle-based drug delivery
[article]
2020
bioRxiv
pre-print
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Large-scale time-lapse microscopy experiments are useful to understand delivery and expression in RNA-based therapeutics. ...
The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high content imaging. ...
A major bottleneck when applying these deep learning methods to cell and tissue images is the scarcity of annotated data. ...
doi:10.1101/2020.04.06.027672
fatcat:gmuaa5sx4jg4jko657rsqlz3li
A Method for Astral Microtubule Tracking in Fluorescence Images of Cells Doped with Taxol and Nocodazole
2019
Advances in Molecular Imaging
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In this paper, we describe an algorithm that performs automatic detection and tracking of astral microtubules in fluorescence confocal images. ...
The results are encouraging in terms of performance, robustness and simplicity of use, and the algorithm is now routinely employed in our Department of Molecular Biotechnology. ...
Conflicts of Interest The authors declare no conflicts of interest regarding the publication of this paper. ...
doi:10.4236/ami.2019.94009
fatcat:4pls2ycitvd6jlvuku5cajcqty
Diatrack particle tracking software: Review of applications and performance evaluation
2017
Traffic : the International Journal of Intracellular Transport
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virus particle tracking or single-molecule imaging. ...
, virus particle tracking, or single-molecule imaging. ...
Particle tracking: Once an optimal set of particles has been identified in the first image of the sequence, one may automatically process all remaining images using the currently selected parameters (" ...
doi:10.1111/tra.12530
pmid:28945316
fatcat:g5vvvenfufei3m2dnpbcss6lru
Single-pixel interior filling function approach for detecting and correcting errors in particle tracking
2016
Proceedings of the National Academy of Sciences of the United States of America
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We expect the SPIFF approach to be useful in a wide range of localization applications, including single-molecule imaging and particle tracking, in fields ranging from biology to materials science to astronomy ...
To further develop our quantitative insight into cellular transport, we analyze data sets of mRNA molecules fluorescently labeled with MS2-GFP tracked in real time in live Escherichia coli and Saccharomyces ...
It can naturally embed new images into the subspace using the learned deep architecture. ...
doi:10.1073/pnas.1619104114
pmid:28028226
pmcid:PMC5240672
fatcat:imzi4n3bmjguzjakwdalknvkmi
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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Chen, G., +, TIP
2020 5877-5888
Fluorescence
A Recurrent Neural Network for Particle Tracking in Microscopy Images
Using Future Information, Track Hypotheses, and Multiple Detections. ...
., +, TIP 2020 6561-6573 A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking
2020
IEEE transactions on intelligent transportation systems (Print)
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Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. ...
image detection to high-level psychology models, from the perspective of an AV designer. ...
In [107] , shadows are automatically removed from the images in HSV color space. ...
doi:10.1109/tits.2020.3006768
fatcat:awa5dgk4rbazteetyyqrndbgxq
BCM3D 2.0: Accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations
[article]
2021
bioRxiv
pre-print
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Leveraging the capabilities of deep convolutional neural networks (CNNs), we recently developed bacterial cell morphometry in 3D (BCM3D), an integrated image analysis pipeline that combines deep learning ...
Accurate detection and segmentation of single cells in three-dimensional (3D) fluorescence time-lapse images is essential for measuring individual cell behaviors in large bacterial communities called biofilms ...
Acknowledgements This work was supported in part by the US National Institute of General Medical Sciences Grant 1R01GM139002 (A.G.). ...
doi:10.1101/2021.11.26.470109
fatcat:wp7i4gr6rbf45oxaye6l7qmoku
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