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Non invasive live cell cycle monitoring using quantitative phase imaging and proximal machine learning methods
- Source :
- CBMS, 32nd IEEE CBMS International Symposium on Computer-Based Medical Systems, 32nd IEEE CBMS International Symposium on Computer-Based Medical Systems, Jun 2019, Cordoue, Spain
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- International audience; This interdisciplinary work focuses on the interest of a new proximal algorithm (ADRS) for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (QP I) camera. This type of camera produces interferograms that have to be processed in order to extract features derived from quantitative linear retardance and birefringence measurements. We monitor cell cycling in different populations with both a classical fluorescent DNA intercalating agent imaging on the one hand and QP I without any cellular manipulation nor treatment on the other hand. We show that the accuracy of the classification of these cells in different phases of the cell cycle is equivalent, if not better, when using QP I features as compared to fluorescence imaging features. This is a very important finding since we demonstrate that it is now possible to very precisely follow cell growth under regular culture conditions without any bias. No dye or any kind of markers are necessary for this live monitoring, thus the cells normal physiology is not at all affected by this non invasive procedure. Any studies requiring analysis of cell growth or cellular response to any kind of treatment could benefit from this new approach..
- Subjects :
- Fluorescence-lifetime imaging microscopy
business.industry
[SDV]Life Sciences [q-bio]
Feature extraction
Non invasive
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Cell cycle
[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]
[SDV] Life Sciences [q-bio]
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Phase imaging
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- CBMS, 32nd IEEE CBMS International Symposium on Computer-Based Medical Systems, 32nd IEEE CBMS International Symposium on Computer-Based Medical Systems, Jun 2019, Cordoue, Spain
- Accession number :
- edsair.doi.dedup.....19600c682f2996735a5be5702617f558