ArticlePloS one2021
celldeath: A tool for detection of cell death in transmitted light microscopy images by deep learning-based visual recognition.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Deep transfer learning approach for automated cell death classification reveals novel ferroptosis-inducing agents in subsets of B-ALL.Cell death & disease · 2025Article
- Deep learning-based image classification reveals heterogeneous execution of cell death fates during viral infection.Molecular biology of the cell · 2025Article
- More than just life and death: advances in imaging and analysis for 3D-bioprinted tissues.Frontiers in bioengineering and biotechnology · 2025Article
- Semi-automated, high-content imaging of drug transporter knockout sea urchin (Lytechinus pictus) embryos.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2024Article
- Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging.eLife · 2024Article
- Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.Bioinformatics (Oxford, England) · 2023Article
- DeXtrusion: automatic recognition of epithelial cell extrusion through machine learning in vivo.Development (Cambridge, England) · 2023Article
- Machine learning-based detection of label-free cancer stem-like cell fate.Scientific reports · 2022Article
- LANCE: a Label-Free Live Apoptotic and Necrotic Cell Explorer Using Convolutional Neural Network Image Analysis.Analytical chemistry · 2022Article
- GEMA-An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices.Journal of imaging · 2022Article
- Deep Learning-Assisted Nephrotoxicity Testing with Bioprinted Renal Spheroids.International journal of bioprinting · 2022Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell death is preceded by slight morphological changes in cell shape and texture. In this paper, we trained a neural network to classify cells undergoing cell death. We found that the network was able to highly predict cell death after one hour of exposure to camptothecin. Moreover, this prediction largely outperforms human ability. Finally, we provide a simple python tool that can broadly be used to detect cell death.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.