Robust classification of cell cycle phase and biological feature extraction by image-based deep learning.
Mol Biol Cell
; 31(13): 1346-1354, 2020 06 15.
Article
in En
| MEDLINE
| ID: mdl-32320349
ABSTRACT
Across the cell cycle, the subcellular organization undergoes major spatiotemporal changes that could in principle contain biological features that could potentially represent cell cycle phase. We applied convolutional neural network-based classifiers to extract such putative features from the fluorescence microscope images of cells stained for the nucleus, the Golgi apparatus, and the microtubule cytoskeleton. We demonstrate that cell images can be robustly classified according to G1/S and G2 cell cycle phases without the need for specific cell cycle markers. Grad-CAM analysis of the classification models enabled us to extract several pairs of quantitative parameters of specific subcellular features as good classifiers for the cell cycle phase. These results collectively demonstrate that machine learning-based image processing is useful to extract biological features underlying cellular phenomena of interest in an unbiased and data-driven manner.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Image Processing, Computer-Assisted
/
Cell Cycle
/
Deep Learning
Limits:
Animals
/
Humans
Language:
En
Journal:
Mol Biol Cell
Journal subject:
BIOLOGIA MOLECULAR
Year:
2020
Document type:
Article
Affiliation country: