CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository, an example of accessing containerized tools, improved documentation, and added citation/reference tools to facilitate the use and contribution of the community.
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J Microsc
December 2024
Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy.
View Article and Find Full Text PDFCellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy.
View Article and Find Full Text PDFRedox Biol
June 2023
Center for Systems and Therapeutics, Gladstone Institutes, 1650 Owens Street, San Francisco, CA, 94158, USA; Departments of Neurology and Physiology, University of California, San Francisco, San Francisco, CA, 94158, USA. Electronic address:
DNA damage is a common cellular feature seen in cancer and neurodegenerative disease, but fast and accurate methods for quantifying DNA damage are lacking. Comet assays are a biochemical tool to measure DNA damage based on the migration of broken DNA strands towards a positive electrode, which creates a quantifiable 'tail' behind the cell. However, a major limitation of this approach is the time needed for analysis of comets in the images with available open-source algorithms.
View Article and Find Full Text PDFSci Rep
March 2022
Biomarker Imaging Research Laboratory, Sunnybrook Research Institute, Toronto, ON, Canada.
Cellular profiling with multiplexed immunofluorescence (MxIF) images can contribute to a more accurate patient stratification for immunotherapy. Accurate cell segmentation of the MxIF images is an essential step. We propose a deep learning pipeline to train a Mask R-CNN model (deep network) for cell segmentation using nuclear (DAPI) and membrane (NaKATPase) stained images.
View Article and Find Full Text PDFStem Cell Reports
April 2019
Department of Anatomy and Embryology, Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands. Electronic address:
Vascular smooth muscle cells (vSMCs) are highly heterogeneous across different vascular beds. This is partly dictated by their developmental origin but also their position in the vascular tree, reflected in their differential responses to vasoactive agonists depending on which arteriolar or venular segment they are located. Functional assays are necessary to capture this heterogeneity in vitro since there are no markers that distinguish subtypes.
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