Quantitative method to characterize cell morphology
December 2017
The Catholic University of America, Washington, USA
In this study, human breast cancer cells were cultured in different substrates to classify them depending on their morphology. Digital holographic microscopy coupled with epifluorescence microscopy were used to relate cell phase parameters to actin features. A machine learning method was used to classify the morphologies of cancer cells. The results showed that this method has high accuracy in classifying cell morphologies, which makes it a useful method to monitor cancer cell morphology features.
Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning
Christopher B Raub
Added on: 08-01-2021
[1] https://onlinelibrary.wiley.com/doi/full/10.1002/cyto.a.23316[2] https://data.jrc.ec.europa.eu/dataset/ffebe454-ed9a-47cf-8a33-8cf70c1b7d38
