On Simulating 3D Fluorescent Microscope Images

Svoboda, David; Kašík, Marek; Maška, Martin; Hubený, Jan; Stejskal, Stanislav; Zimmermann, Michal

doi:10.1007/978-3-540-74272-2_39

Cited by 17 publications

(18 citation statements)

References 15 publications

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“…The last module, '3D-acquigen' is the digital CCD camera simulator of the phenomenon's that occur during image capture (noise, sampling, digitization) by changing the camera selection, the acquisition time, the dynamic range usage and the stage z-step. For example, this tool is capable of recreating a 3D rendition of a HL-60 Nucleus, generated from an ellipsoid in black and white, deformed using partial differential equation-based methods and then texturized by creating and defining the internal structure and adding the nucleus [37], [38]. 'CytoPacq' is able to generate an artificial time-lapsed microscopy images, marking an important step for the validation of automatic tools used in live cell imaging, as time series extends the observation from a unique time-point (just 1 frame) to the observation various frames containing cellular dynamics, such as measuring protein or RNA levels or even observing cell migration, cell division and cell growth [3], [4].…”

Section: Cmentioning

confidence: 99%

‘miSimBa’ — A simulator of synthetic time-lapsed microscopy images of bacterial cells

Martins

Fonseca

Ribeiro

2015

2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG)

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Escherichia coli is a model organism for the study of multiple biological processes, including gene expression and cellular aging. Recently, these studies started to rely on temporal single cell imaging. To support these efforts, available automated image analysis methods should be improved. One important step is their validation. Ideally, the "ground truth" of the images should be known, which is possible only in synthetic images. To simulate artificial images of E. coli cells, we are developing the 'miSimBa' tool (Microscopy Image Simulator of Bacterial Cells). 'miSimBa' simulates images that reproduce the spatial and temporal bacterial organization by modelling realistically cell morphology (shape, size and spatial arrangement), cell growth and division, cell motility and some internal functions and intracellular structures, namely, the nucleoid. This tool also incorporates image acquisition parameters that simulate illumination and the primary sources of noise.

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Section: Cmentioning

confidence: 99%

‘miSimBa’ — A simulator of synthetic time-lapsed microscopy images of bacterial cells

Martins

Fonseca

Ribeiro

2015

2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG)

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“…A number of different approaches have been described for constructing models of either or both of these. While work has been done on constructing generative models of nuclei or cells by hand [24, 25], We will focus here on learning generative models directly from images. Manually constructed models may be oversimplified and may not capture subtle differences between cell populations, and the approach does not scale to the large collections of images available for many cells types.…”

Section: Constructing Modelsmentioning

confidence: 99%

Building cell models and simulations from microscope images

Murphy

2016

Methods

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The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge.

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“…In the case of the diffeomorphic model, the parameters are very extensive. Other types of generative models of nuclear shape can be used (16,17), although our overall philosophy is to prefer models whose parameters are automatically learned from images.…”

Section: Models Of Subcellular Organizationmentioning

confidence: 99%

Communicating subcellular distributions

Murphy

2010

Cytometry Pt A

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To build more accurate models of cells and tissues, the ability to incorporate information on the distributions of proteins (and other macromolecules) will become increasingly important. This review describes current progress towards determining and representing protein subcellular patterns so that the information can be used as part of systems biology efforts. Approaches to decomposing an image of the subcellular pattern of a protein give critical information about the fraction of that protein in each of a number of fundamental patterns (e.g., organelles). Methods for learning generative models from images provide a means of capturing the essential properties and variation in those properties of cell shape and organelle patterns. The combination of models of fundamental patterns and vectors specifying the fraction of a protein in each of them provide a much better means of communicating subcellular patterns than the descriptive terms that are currently used. Communicating information about subcellular patterns is important not only for systems biology simulations but also for representing results from microscopy experiments, including high content screening and imaging flow cytometry, in a transportable and generalizable manner. ' 2010 International Society for Advancement of Cytometry Key terms location proteomics; subcellular location; pattern recognition; pattern unmixing; generative models; systems biology STRUCTURED information on subcellular location in protein databases, such as Uniprot (1), currently consists only of terms from a restricted vocabulary. These are usually drawn from the Cellular Component portion of the Gene Ontology (GO) (2). The guiding principle behind this approach is that each protein can be assigned to one, or at most a few distinct subcellular structures whose names are given in the ontology. Assignments may be based on experimental determinations, unsupported assertions in articles, or predictions based on sequence analysis. Some databases, such as LOCATE (3), provide links to articles describing experimental evidence for a given assignment. Most protein databases have at least some GO terms associated with most proteins.There are three major considerations that currently limit incorporation of information on protein subcellular distributions into spatially realistic cell simulations. The first is the need for accurate models of the structure (and variation in structure) of each subcellular component. The second is the need for information on the distribution of proteins across different components. The third is the need for information on how protein locations change between different conditions or cell types. This review summarizes progress towards addressing these goals through direct analysis of images. The various approaches are depicted for reference in Figure 1.A word on the terminology used here. The terms subcellular component or structure are used below rather than organelle since the latter term usually implies the presence of a limiting membrane and many distin...

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On Simulating 3D Fluorescent Microscope Images

Cited by 17 publications

References 15 publications

‘miSimBa’ — A simulator of synthetic time-lapsed microscopy images of bacterial cells

‘miSimBa’ — A simulator of synthetic time-lapsed microscopy images of bacterial cells

Building cell models and simulations from microscope images

Communicating subcellular distributions

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On Simulating 3D Fluorescent Microscope Images

Cited by 17 publications

References 15 publications

&#x2018;miSimBa&#x2019; &#x2014; A simulator of synthetic time-lapsed microscopy images of bacterial cells

&#x2018;miSimBa&#x2019; &#x2014; A simulator of synthetic time-lapsed microscopy images of bacterial cells

Building cell models and simulations from microscope images

Communicating subcellular distributions

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Product

Resources

About

‘miSimBa’ — A simulator of synthetic time-lapsed microscopy images of bacterial cells

‘miSimBa’ — A simulator of synthetic time-lapsed microscopy images of bacterial cells