Estimation of Cell Cycle States of Human Melanoma Cells with Quantitative Phase Imaging and Deep Learning

Tristan Henser-brownhill, Robert J. Ju, Nikolas K. Haass, Samantha J. Stehbens, Christoph Ballestrem, Timothy F. Cootes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

159 Downloads (Pure)

Abstract

Visualization and classification of cell cycle stages in live cells requires the introduction of transient or stably expressing fluorescent markers. This is not feasible for all cell types, and can be time consuming to implement. Labelling of living cells also has the potential to perturb normal cellular function. Here we describe a computational strategy to estimate core cell cycle stages without markers by taking advantage of features extracted from information-rich ptychographic time-lapse movies. We show that a deep-learning approach can estimate the cell cycle trajectories of individual human melanoma cells from short 3-frame (~23 minute) snapshots, and can identify cell cycle arrest induced by chemotherapeutic agents targeting melanoma driver mutations.
Original languageEnglish
Title of host publication 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
PublisherIEEE
Pages1617-1621
ISBN (Electronic)978-1-5386-9330-8
ISBN (Print)978-1-5386-9331-5
DOIs
Publication statusPublished - 22 May 2020
Event2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) - Iowa City, IA, USA
Duration: 3 Apr 20207 Apr 2020

Conference

Conference2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
Period3/04/207/04/20

Fingerprint

Dive into the research topics of 'Estimation of Cell Cycle States of Human Melanoma Cells with Quantitative Phase Imaging and Deep Learning'. Together they form a unique fingerprint.

Cite this