204 citations · 672 across the 14 of their papers we have counts for
6 papers · 1 filter
Evaluating Integrative Strategies for Incorporating Phenotypic Features in Spatial Transcriptomics
Levin M Moser, Ahmad Kamal Hamid, Esteban Miglietta +2
Spatial transcriptomics (ST) technologies not only offer an unprecedented opportunity to interrogate intact biological samples in a spatially informed manner, but also set the stag…
Cell Painting Gallery: an open resource for image-based profiling
Erin Weisbart, Ankur Kumar, John Arevalo +3
Image-based or morphological profiling is a rapidly expanding field wherein cells are "profiled" by extracting hundreds to thousands of unbiased, quantitative features from images…
Defining the boundaries: challenges and advances in identifying cells in microscopy images
Nodar Gogoberidze, Beth A. Cimini
Segmentation, or the outlining of objects within images, is a critical step in the measurement and analysis of cells within microscopy images. While improvements continue to be mad…
Reproducible image-based profiling with Pycytominer
Erik Serrano, Srinivas Niranj Chandrasekaran, Dave Bunten +25
Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Whether by deep learning or classical algorithms, imag…
CellProfiler plugins -- an easy image analysis platform integration for containers and Python tools
Erin Weisbart, Callum Tromans-Coia, Barbara Diaz-Rohrer +7
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 Ce…
CellProfiler Analyst 3.0: Accessible data exploration and machine learning for image analysis
David R. Stirling, Anne E. Carpenter, Beth A. Cimini
Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free,…