CellProfiler 3.0: Next-generation image processing for biology

McQuin, Claire; Goodman, Allen; Chernyshev, Vasiliy S.; Kamentsky, Lee; Cimini, Beth A.; Karhohs, Kyle W.; Doan, Minh; Ding, Liya; Rafelski, Susanne M.; Thirstrup, Derek; Wiegraebe, Winfried; Singh, Shantanu; Becker, Tim; Caicedo, Juan C.; Carpenter, Anne E.

doi:10.1371/journal.pbio.2005970

Cited by 1,810 publications

(1,615 citation statements)

References 33 publications

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“…The training parameters for this network were tuned using the training and validation sets, and the final model is applied to the test set to report performance. The source code of our U-Net implementation can be found in https://github.com/ carpenterlab/2019_caicedo_cytometryA, with an optional CellProfiler 3.0 plugin of this nucleus-specific model (41). Also, a U-Net plugin was independently developed for ImageJ for running generic cell segmentation and quantification tasks (42).…”

Section: U-netmentioning

confidence: 99%

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

et al. 2019

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Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

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Section: U-netmentioning

confidence: 99%

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

et al. 2019

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“…These advancements come from the application of automated microscopy capable of scanning 96-or 384-multiwell plates within hours or even minutes. Moreover, quantification of obtained data can be automatized with available open-source software (McQuin et al, 2018) or custom algorithms (Kraus & Frey, 2016).…”

Section: Cells Used In Modeling Pd Pathology In Vitromentioning

confidence: 99%

Back and to the Future: From Neurotoxin‐Induced to Human Parkinson's Disease Models

et al. 2020

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Parkinson's disease (PD) is an age‐related neurodegenerative disorder characterized by motor symptoms such as tremor, slowness of movement, rigidity, and postural instability, as well as non‐motor features like sleep disturbances, loss of ability to smell, depression, constipation, and pain. Motor symptoms are caused by depletion of dopamine in the striatum due to the progressive loss of dopamine neurons in the substantia nigra pars compacta. Approximately 10% of PD cases are familial arising from genetic mutations in α‐synuclein, LRRK2, DJ‐1, PINK1, parkin, and several other proteins. The majority of PD cases are, however, idiopathic, i.e., having no clear etiology. PD is characterized by progressive accumulation of insoluble inclusions, known as Lewy bodies, mostly composed of α‐synuclein and membrane components. The cause of PD is currently attributed to cellular proteostasis deregulation and mitochondrial dysfunction, which are likely interdependent. In addition, neuroinflammation is present in brains of PD patients, but whether it is the cause or consequence of neurodegeneration remains to be studied. Rodents do not develop PD or PD‐like motor symptoms spontaneously; however, neurotoxins, genetic mutations, viral vector‐mediated transgene expression and, recently, injections of misfolded α‐synuclein have been successfully utilized to model certain aspects of the disease. Here, we critically review the advantages and drawbacks of rodent PD models and discuss approaches to advance pre‐clinical PD research towards successful disease‐modifying therapy. © 2020 The Authors.

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“…All image datasets are imported from previously organized challenges (DIADEM [21], ISBI Cell Tracking Challenge [22], ISBI Particle Tracking Challenge [23], Kaggle Data Science Bowl 2018 [24]), created from synthetic data generators (CytoPacq [25], TREES toolbox [26], Vascusynth [27], SIMCEP [28]), or contributed by NEUBIAS members [37]. To showcase the versatility of the platform, the image analysis workflows available to process these images are running on different BIA platforms: ImageJ macros [29], Icy protocols [30], CellProfiler pipelines [31], Vaa3D plugins [32], ilastik pipelines [33], Python scripts leveraging Scikit-learn [34] for supervised learning algorithms, and Keras/PyTorch [35] [36] for deep learning. These workflows were mostly contributed by members of NEUBIAS Workgroup 5, or imported from existing challenges.…”

Section: Accessing Biaflows Online Instancementioning

confidence: 99%

BIAFLOWS: A collaborative framework to reproducibly deploy and benchmark bioimage analysis workflows

Rubens

Mormont

Baecker

et al. 2019

Preprint

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Automated image analysis has become key to extract quantitative information from biological microscopy images, however the methods involved are now often so complex that they can no longer be unambiguously described using written protocols. We introduce BIAFLOWS, a web based framework to encapsulate, deploy, and benchmark automated bioimage analysis workflows (the software implementation of an image analysis method). BIAFLOWS helps fairly comparing image analysis workflows and reproducibly disseminating them, hence safeguarding research based on their results and promoting the highest quality standards in bioimage analysis.

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CellProfiler 3.0: Next-generation image processing for biology

Cited by 1,810 publications

References 33 publications

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Back and to the Future: From Neurotoxin‐Induced to Human Parkinson's Disease Models

BIAFLOWS: A collaborative framework to reproducibly deploy and benchmark bioimage analysis workflows

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