Tracking of cells in a sequence of images using a low-dimension image representation

Primet, Maël; Démarez, Alice; Taddéi, François; Lindner, Ariel B.; Moisan, Lionel

doi:10.1109/isbi.2008.4541166

Cited by 11 publications

(17 citation statements)

References 7 publications

(6 reference statements)

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“…Phase contrast images were analyzed by customized software "Cellst" [58] for cell segmentation and single cell lineage reconstruction. Phase contrast images were denoised using the flatten background filter of Metamorph software for long movies or a mixed denoising algorithm [58] for fast movies. The mixed denoising algorithm combines two famous image denoising methods: NL-means denoising [59], which is patch-based and Total…”

Section: Image Analysis and Aggregate Trackingmentioning

confidence: 99%

Localization of Protein Aggregation in Escherichia coli Is Governed by Diffusion and Nucleoid Macromolecular Crowding Effect

et al. 2013

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Aggregates of misfolded proteins are a hallmark of many age-related diseases. Recently, they have been linked to aging of Escherichia coli (E. coli) where protein aggregates accumulate at the old pole region of the aging bacterium. Because of the potential of E. coli as a model organism, elucidating aging and protein aggregation in this bacterium may pave the way to significant advances in our global understanding of aging. A first obstacle along this path is to decipher the mechanisms by which protein aggregates are targeted to specific intercellular locations. Here, using an integrated approach based on individual-based modeling, time-lapse fluorescence microscopy and automated image analysis, we show that the movement of aging-related protein aggregates in E. coli is purely diffusive (Brownian). Using single-particle tracking of protein aggregates in live E. coli cells, we estimated the average size and diffusion constant of the aggregates. Our results provide evidence that the aggregates passively diffuse within the cell, with diffusion constants that depend on their size in agreement with the Stokes-Einstein law. However, the aggregate displacements along the cell long axis are confined to a region that roughly corresponds to the nucleoid-free space in the cell pole, thus confirming the importance of increased macromolecular crowding in the nucleoids. We thus used 3D individual-based modeling to show that these three ingredients (diffusion, aggregation and diffusion hindrance in the nucleoids) are sufficient and necessary to reproduce the available experimental data on aggregate localization in the cells. Taken together, our results strongly support the hypothesis that the localization of aging-related protein aggregates in the poles of E. coli results from the coupling of passive diffusion-aggregation with spatially non-homogeneous macromolecular crowding. They further support the importance of “soft” intracellular structuring (based on macromolecular crowding) in diffusion-based protein localization in E. coli.

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Section: Image Analysis and Aggregate Trackingmentioning

confidence: 99%

Localization of Protein Aggregation in Escherichia coli Is Governed by Diffusion and Nucleoid Macromolecular Crowding Effect

et al. 2013

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“…Furthermore, Since the scenes should be invariant to image scaling, translation, and rotation, we employ the Scale Invariant Feature Transform (SIFT) algorithm [1] to extract the feature point of the stitched scenes. It is verified that the approach to match can robustly identify objects among a series of images while achieving near real-time performance [2]. Finally, the scenes mosaic are formed from the linked list data model.…”

Section: Introductionmentioning

confidence: 85%

Study on the automatic Unmanned Aerial Vehicle Image mosaic algorithm

Yin

2011

2011 2nd International Conference on Intelligent Control and Information Processing

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In this paper, a novel mosaic algorithm for Unmanned Aerial Vehicle Images (UAVI) was put forward. We implemented the automatic mosaic method as follows: Firstly, the UAVI were zoomed and adjusted to the required size using the wavelet frequency-divided encoder. Secondly, the correspondences between the neighboring frames were established and the feature information in the linked list data model was recorded. Finally, the mosaic panorama was fulfilled according as the linked list data model. The experimental result showed that the algorithm was effective and can adapt to the real-time character of UAVI stitching.

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“…Nevertheless, many automated analysis programs have been developed for specific experimental setups, mostly for analysis of eukaryotic cells (for review see [ 5 ]). For prokaryotic cells, several software solutions exist [ 6 – 11 ], among which only Schnitzcells [ 6 ] and MicrobeTracker [ 7 ] are widely used.…”

Section: Introductionmentioning

confidence: 99%

BactImAS: a platform for processing and analysis of bacterial time-lapse microscopy movies

Mekterović

Maglica

2014

BMC Bioinformatics

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BackgroundThe software available to date for analyzing image sequences from time-lapse microscopy works only for certain bacteria and under limited conditions. These programs, mostly MATLAB-based, fail for microbes with irregular shape, indistinct cell division sites, or that grow in closely packed microcolonies. Unfortunately, many organisms of interest have these characteristics, and analyzing their image sequences has been limited to time consuming manual processing.ResultsHere we describe BactImAS – a modular, multi-platform, open-source, Java-based software delivered both as a standalone program and as a plugin for Icy. The software is designed for extracting and visualizing quantitative data from bacterial time-lapse movies. BactImAS uses a semi-automated approach where the user defines initial cells, identifies cell division events, and, if necessary, manually corrects cell segmentation with the help of user-friendly GUI and incorporated ImageJ application. The program segments and tracks cells using a newly-developed algorithm designed for movies with difficult-to-segment cells that exhibit small frame-to-frame differences. Measurements are extracted from images in a configurable, automated fashion and an SQLite database is used to store, retrieve, and exchange all acquired data. Finally, the BactImAS can generate configurable lineage tree visualizations and export data as CSV files. We tested BactImAS on time-lapse movies of Mycobacterium smegmatis and achieved at least 10-fold reduction of processing time compared to manual analysis. We illustrate the power of the visualization tool by showing heterogeneity of both icl expression and cell growth atop of a lineage tree.ConclusionsThe presented software simplifies quantitative analysis of time-lapse movies overall and is currently the only available software for the analysis of mycobacteria-like cells. It will be of interest to the community of both end-users and developers of time-lapse microscopy software.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-251) contains supplementary material, which is available to authorized users.

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Tracking of cells in a sequence of images using a low-dimension image representation

Cited by 11 publications

References 7 publications

Localization of Protein Aggregation in Escherichia coli Is Governed by Diffusion and Nucleoid Macromolecular Crowding Effect

Localization of Protein Aggregation in Escherichia coli Is Governed by Diffusion and Nucleoid Macromolecular Crowding Effect

Study on the automatic Unmanned Aerial Vehicle Image mosaic algorithm

BactImAS: a platform for processing and analysis of bacterial time-lapse microscopy movies

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