CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging

By Michael Held, Michael H. A. Schmitz, Bernd Fischer, Thomas Walter, Beate Neumann, Michael H. Olma, Matthias Peter, Jan Ellenberg, Daniel Gerlich. Nature Methods, 2010

381 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/50cc8dd825103c97204ff58a7c5da09c70bb128c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Oren Z. Kraus
2. Brendan J. Frey
3. Charles Boone
4. Brenda J. Andrews
5. Ben T Grys
6. Nil Sahin
7. Quaid D. Morris
8. Dara S Lo

In Molecular Biology

1. Oren Z. Kraus
2. Brendan J. Frey
3. Quaid D. Morris
4. Charles Boone
5. Brenda J. Andrews
6. Ben T Grys
7. Nil Sahin
8. Dara S Lo

Citations in the corpus

• High-Content Screening for Quantitative Cell Biology. (2016, methodology): “CellCognition Combines object detection and supervised machine learning for classification of morphologies with time-resolved analysis by single cell tracking [74] http://www.”
• Machine learning and computer vision approaches for phenotypic profiling (2017, background): “Numerous other groups have also taken advantage of SVM classifiers to classify morphological phenotypes in human cells, including mutant morphology (Schmitz et al., 2010), cell cycle arrest phenotypes (Neumann et al., 2010), and subcellular localization classes (Conrad et al., 2004).”
• Large‐scale image‐based screening and profiling of cellular phenotypes (2017, background): “…may represent the prevalence (in the form of histograms or distributions) of different cellular subpopulations (110,111) or local patches/key-point regions (89), temporal relationships between different classes of phenotypes (87,91), or cell shape or protein subcellular localization patterns (112).”
• Machine learning in cell biology – teaching computers to recognize phenotypes (2013, methodology): “Supervised machine learning has been an important backbone for analysis pipelines in many high-content screening projects (Kittler et al., 2007; Fuchs et al., 2010; Neumann et al., 2010; Schmitz et al., 2010; Mercer et al., 2012).”
• Classifying and segmenting microscopy images with deep multiple instance learning (2015, background): “Although comprehensive tools have become available [2] they are often optimized for mammalian cells and not directly applicable to model organisms such as yeast and C. elegans .”
• Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “In the second stage, a Gaussian kernel-based SVM model with global cell features [136] is used”
• In silico labeling: Predicting fluorescent labels in unlabeled images (2018, background): “, 2012) or the imposition of special and somewhat artificial sample preparation procedures, such as the requirement for low-plating density (Held et al., 2010; Van Valen et al., 2016; Zhong et al., 2012).”
• Computer vision for high content screening (2016, methodology): “…algorithms that have been used to classify cellular phenotypes include support vector machines (SVM) (Chong et al ., 2015; Fuchs et al ., 2010; Held et al ., 2010; Loo et al ., 2007), adaptive boosting (Jones et al ., 2008), random forests (Kaynig et al ., 2010), and neural networks (Boland &…”