SimuCell: a flexible framework for creating synthetic microscopy images

Rajaram, Satwik; Pavie, Benjamin; Hac, Nicholas E F; Altschuler, Steven J.; Wu, Lani F.

doi:10.1038/nmeth.2096

Cited by 43 publications

(37 citation statements)

References 6 publications

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“…Generative models can be used to synthesize new data points, which might be useful in some specialized applications [e.g. simulation of cell morphology (Buck et al, 2012;Rajaram et al, 2012b)]. Generative models have also been successfully applied to correct misclassifications of cell cycle stages, aided by temporal information in time-lapse movies or the discovery of new biologically active peptide hormones by searching for sequence features in protein sequences (Mirabeau et al, 2007) using hidden Markov models (Rabiner, 1989).…”

Section: Supervised Machine Learning: Learning From User-defined Exammentioning

confidence: 99%

“…In light of the diversity of data types and analysis tasks in cell biology, it is often difficult to estimate the performance of published learning methods based on the specific proof-of-concept data used in the respective study. For objective benchmarking of learning methods in high-content screening, several annotated reference data sets have been published (Ljosa et al, 2012;Rajaram et al, 2012b).…”

Section: Random Forestmentioning

confidence: 99%

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Machine learning in cell biology – teaching computers to recognize phenotypes

Sommer

Gerlich

2013

Journal of Cell Science

282

237

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SummaryRecent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.

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Section: Supervised Machine Learning: Learning From User-defined Exammentioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

Machine learning in cell biology – teaching computers to recognize phenotypes

Sommer

Gerlich

2013

Journal of Cell Science

282

237

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Section: Validationmentioning

confidence: 99%

“…Thus simulations provide necessary, but not sufficient, elements of validation. Validation and benchmarking of algorithms therefore benefit from tools that simulate realistic images (Boulanger et al, 2009;Rajaram et al, 2012). Because it is hard to guarantee that simulations are sufficiently realistic, the most useful validations are those based on real data.…”

Section: Validationmentioning

confidence: 99%

From microbes to numbers: extracting meaningful quantities from images

Zimmer

2012

Cell Microbiol

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SummaryLight microscopy offers a unique window into the life and works of microbes and their interactions with hosts. Mere visualization of images, however, does not provide the quantitative information needed to reliably and accurately characterize phenotypes or test computational models of cellular processes, and is unfeasible in high-throughput screens. Algorithms that automatically extract biologically meaningful quantitative data from images are therefore an increasingly essential complement to the microscopes themselves. This paper reviews some of the computational methods developed to detect, segment and track cells, molecules or viruses, with an emphasis on their underlying assumptions, limitations, and the importance of validation.

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“…To avoid this, we present a cell simulation software package for realistic, multi-channel cell images with known ground truth in various two-dimensional microscopic imaging techniques. Our approach extends simulation capabilites compared to currently available software [1,2] which is limited to flourescence microscopy.…”

Section: Introductionmentioning

confidence: 99%

Cell Simulation for Validation of Cell Micrograph Evaluation Algorithms

Wiesmann¹,

Sauer²,

Held³

et al. 2013

Biomedical Engineering / Biomedizinische Technik

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The evaluation of biological microscopy experiments is a large subfield in image processing. Many methods have been published, which are specially designed for a certain microscopy technique or even for a single experiment. Usually they are evaluated with user annotated data which is prone to errors and, thus, shows inter-and intraobserver variance. We present a simulation software capable to generate micrographs with known ground truth of various microspcopy techniques for an unbiased and objective evaluation.

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SimuCell: a flexible framework for creating synthetic microscopy images

Cited by 43 publications

References 6 publications

Machine learning in cell biology – teaching computers to recognize phenotypes

Machine learning in cell biology – teaching computers to recognize phenotypes

From microbes to numbers: extracting meaningful quantities from images

Cell Simulation for Validation of Cell Micrograph Evaluation Algorithms

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