Literature DB >> 34166446

celldeath: A tool for detection of cell death in transmitted light microscopy images by deep learning-based visual recognition.

Alejandro Damián La Greca1, Nelba Pérez1, Sheila Castañeda1, Paula Melania Milone1, María Agustina Scarafía1, Alan Miqueas Möbbs1, Ariel Waisman1,2, Lucía Natalia Moro1,2, Gustavo Emilio Sevlever1, Carlos Daniel Luzzani1,2, Santiago Gabriel Miriuka1,2.   

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.

Entities:  

Year:  2021        PMID: 34166446     DOI: 10.1371/journal.pone.0253666

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  1 in total

1.  Deep Learning-Assisted Nephrotoxicity Testing with Bioprinted Renal Spheroids.

Authors:  Kevin Tröndle; Guilherme Miotto; Ludovica Rizzo; Roman Pichler; Fritz Koch; Peter Koltay; Roland Zengerle; Soeren S Lienkamp; Sabrina Kartmann; Stefan Zimmermann
Journal:  Int J Bioprint       Date:  2022-01-19
  1 in total

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