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Counting Cells in Time-Lapse Microscopy using Deep Neural Networks [article]

Alexander Gomez Villa, Augusto Salazar, Igor Stefanini
2018 arXiv   pre-print
In this paper, a method for microscopy cell counting using multiple frames (hence temporal information) is proposed.  ...  A spatiotemporal model using ConvNets and long short term memory (LSTM) recurrent neural networks is proposed to overcome temporal variations.  ...  Fig. 4 shows the proposed framework for cell counting in time-lapse microscopy.  ... 
arXiv:1801.10443v1 fatcat:xksshmglvngjfhgzzimkhd4zd4

Deep Convolutional Neural Networks for Human Embryonic Cell Counting [chapter]

Aisha Khan, Stephen Gould, Mathieu Salzmann
2016 Lecture Notes in Computer Science  
We address the problem of counting cells in time-lapse microscopy images of developing human embryos.  ...  The framework employs a deep convolutional neural network model trained to count cells from raw microscopy images. We demonstrate the effectiveness of our approach on a data set of 265 human embryos.  ...  In this paper we focus on the problem of determining the number of cells in time-lapse microscopy images of developing human embryos.  ... 
doi:10.1007/978-3-319-46604-0_25 fatcat:y753nqwulnajti3gwssntx3boq

DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks

Luca Rappez, Alexander Rakhlin, Angelos Rigopoulos, Sergey Nikolenko, Theodore Alexandrov
2020 Molecular Systems Biology  
Here, we present DeepCycle, a deep learning method for estimating a cell cycle trajectory from unsegmented single-cell microscopy images, relying exclusively on the brightfield and nuclei-specific fluorescent  ...  The advent of single-cell methods is paving the way for an in-depth understanding of the cell cycle with unprecedented detail.  ...  , in particular to investigate dynamics of the cell cycle via, e.g., live or time-lapse microscopy.  ... 
doi:10.15252/msb.20209474 pmid:33022142 fatcat:xnra5pz2xzhy3ov4rksc6ikpwu

OrganoID: a versatile deep learning platform for organoid image analysis [article]

Jonathan M Matthews, Brooke Schuster, Sara Saheb Kashaf, Ping Liu, Mustafa Bilgic, Andrey Rzhetsky, Savas Tay
2022 bioRxiv   pre-print
The platform identifies organoid morphology pixel by pixel without the need for fluorescence or transgenic labeling and accurately analyzes a wide range of organoid types in time-lapse microscopy experiments  ...  OrganoID uses a modified u-net neural network with minimal feature depth to encourage model generalization and allow fast execution.  ...  (d) Identified organoids in time-lapse microscopy images are matched across frames to generate single-organoid tracks and follow responses over time.  ... 
doi:10.1101/2022.01.13.476248 fatcat:ng6b7v5u5fck7g7pyt7kai2wg4

CellSium – versatile cell simulator for microcolony ground truth generation

Christian Carsten Sachs, Karina Ruzaeva, Johannes Seiffarth, Wolfgang Wiechert, Benjamin Berkels, Katharina Nöh, Alex Bateman
2022 Bioinformatics Advances  
Synthetic time-lapse videos with and without fluorescence, using programmable cell growth models, and simulation-ready 3D colony geometries for computational fluid dynamics (CFD) are also supported.  ...  We illustrate that the simulated images are suitable for training neural networks.  ...  Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in  ... 
doi:10.1093/bioadv/vbac053 fatcat:t6suyoap7naizperjadfbumalu

Multiscale assay of unlabeled neurite dynamics using phase imaging with computational specificity (PICS) [article]

Mikhail E. Kandel, Eunjae Kim, Young Jae Lee, Gregory Tracy, Hee Jung Chung, Gabriel Popescu
2020 arXiv   pre-print
as cell count.  ...  neural networks.  ...  To recover time-lapse data with specificity to antibodies, deep convolutional neural networks trained on the fixed cells were used to infer the fluorescent signals on live cells. c, PICS (inferred fluorescence  ... 
arXiv:2008.00626v1 fatcat:sn5egveh5na3tefulgzscxjo3u

Deep Learning in Image Cytometry: A Review

Anindya Gupta, Philip J. Harrison, Håkan Wieslander, Nicolas Pielawski, Kimmo Kartasalo, Gabriele Partel, Leslie Solorzano, Amit Suveer, Anna H. Klemm, Ola Spjuth, Ida‐Maria Sintorn, Carolina Wählby
2018 Cytometry Part A  
In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples.  ...  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.  ...  They used this approach to automatically control focus during time-lapse microscopy.  ... 
doi:10.1002/cyto.a.23701 pmid:30565841 pmcid:PMC6590257 fatcat:dszbcsfncrhxnazsxopjkbe3ju

Cell segmentation from telecentric bright-field transmitted light microscopy images using a Residual Attention U-Net: a case study on HeLa line [article]

Ali Ghaznavi, Renata Rychtarikova, Mohammadmehdi Saberioon, Dalibor Stys
2022 arXiv   pre-print
The main objective of this paper is to develop a deep learning, U-Net-based method to segment the living cells of the HeLa line in bright-field transmitted light microscopy.  ...  Living cell segmentation from bright-field light microscopy images is challenging due to the image complexity and temporal changes in the living cells.  ...  The authors declare no conflict of interest, or known competing financial interests, or personal relationships that could have appeared to influence the work reported in this paper.  ... 
arXiv:2203.12290v2 fatcat:jowf57462raznf3vztaxb4dgmm

Automatic Improvement of Deep Learning Based Cell Segmentation in Time-Lapse Microscopy by Neural Architecture Search

Yanming Zhu, Erik Meijering, Jinbo Xu
2021 Bioinformatics  
Results We propose a novel NAS based solution for deep-learning based cell segmentation in time-lapse microscopy images.  ...  Motivation Live cell segmentation is a crucial step in biological image analysis and is also a challenging task because time-lapse microscopy cell sequences usually exhibit complex spatial structures and  ...  Recently, with the huge success of deep learning in various image processing problems, deep neural networks have been proposed for microscopy cell segmentation (Al-Kofahi et al., 2018; Araú jo et al.,  ... 
doi:10.1093/bioinformatics/btab556 pmid:34329376 pmcid:PMC8665766 fatcat:ex52qe6ymnhmlmvfc4diuqecma

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics [article]

Owen M. O'Connor, Razan N. Alnahhas, Jean-Baptiste Lugagne, Mary Dunlop
2021 bioRxiv   pre-print
The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training.  ...  Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data.  ...  In previous work, we developed the Deep Learning for Time-lapse Analysis (DeLTA) pipeline to analyze single-cell growth and gene expression in microscopy images (7) .  ... 
doi:10.1101/2021.08.10.455795 fatcat:dxbjra4wqzdkhehu7dhhbjlrbm

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments

Mikhail E. Kandel, Yuchen R. He, Young Jae Lee, Taylor Hsuan-Yu Chen, Kathryn Michele Sullivan, Onur Aydin, M. Taher A. Saif, Hyunjoon Kong, Nahil Sobh, Gabriel Popescu
2020 Nature Communications  
AbstractDue to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology.  ...  Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids.  ...  Catalin Chiritescu & Taha Anwar at Phi Optics for ongoing maintenance and software development of the Cell Vista microscopes used in this work.  ... 
doi:10.1038/s41467-020-20062-x pmid:33288761 fatcat:4jglsyoaofhb5k36iadey3tl4y

Mitosis Detection in Phase Contrast Microscopy Image Sequences of Stem Cell Populations: A Critical Review

Rakshitha K. S, Radhika K. R
2018 International Journal of Trend in Scientific Research and Development  
Modern technology has enabled monitoring of large populations of live cells over extended time periods in experimental settings.  ...  Live cells are often of low contrast with little natural pigmentation, therefore they usually need to be stained or fixed in order to be visible under bright field microscopy or florescence microscopy.  ...  We use a supervised Deep Neural Network (DNN) as a powerful pixel classifier. The DNN is a max-pooling (MP) convolutional neural network (CNN).  ... 
doi:10.31142/ijtsrd9472 fatcat:g4fsggwperfrtbimzdq5cnzezi

YeastNet: Deep-Learning-Enabled Accurate Segmentation of Budding Yeast Cells in Bright-Field Microscopy

Danny Salem, Yifeng Li, Pengcheng Xi, Hilary Phenix, Miroslava Cuperlovic-Culf, Mads Kærn
2021 Applied Sciences  
Despite recent development of deep-learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to  ...  We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labeling and tracking  ...  Abbreviations The following abbreviations are used in this manuscript: IoU Intersection over Union CNN Convolutional Neural Net  ... 
doi:10.3390/app11062692 fatcat:b2jpwqkfqff7vlceccrwdnag4i

Brain on the stage – Spotlight on nervous system development in zebrafish: EMBO practical course, KIT, Sept. 2013

Steffen Scholpp, Lucia Poggi, Mihaela Žigman
2013 Neural Development  
During the EMBO course 'Imaging of Neural Development in Zebrafish', held on September 9-15 th 2013, researchers from different backgrounds shared their latest results, ideas and practical expertise on  ...  Support for keynote sponsorship was provided by Leica and the Journal of Neural Development. Volocity software was provided by PerkinElmer and Imaris by Bitplane AG. We thank Anja H.  ...  The lecture was accompanied with a time-lapse experiment showing Wnt positive filopodia in the neural plate.  ... 
doi:10.1186/1749-8104-8-23 pmid:24350623 pmcid:PMC3878791 fatcat:6noaheh2cfeodgcbgvo47v56je

YeastNet: Deep Learning Enabled Accurate Segmentation of Budding Yeast Cells in Bright-field Microscopy [article]

Danny Salem, Yifeng Li, Pengcheng Xi, Miroslava Cuperlovic-Culf, Hilary Phenix, Mads Kaern
2020 bioRxiv   pre-print
Despite recent development of deep learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to  ...  We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labelling and tracking  ...  Microfluidics-enabled time-lapse fluorescence microscopy allows the study of dynamic cellular processes in a single-cell manner.  ... 
doi:10.1101/2020.11.30.402917 fatcat:57a2l2ahybf2jp6rogy7anrrme
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