Cell Type Classification and Unsupervised Morphological Phenotyping From Low-Resolution Images Using Deep Learning.

ABSTRACT: Convolutional neural networks (ConvNets) have proven to be successful in both the classification and semantic segmentation of cell images. Here we establish a method for cell type classification utilizing images taken with a benchtop microscope directly from cell culture flasks, eliminating the need for a dedicated imaging platform. Significant flask-to-flask morphological heterogeneity was discovered and overcome to support network generalization to novel data. Cell density was found to be a prominent source of heterogeneity even when cells are not in contact. For the same cell types, expert classification was poor for single-cell images and better for multi-cell images, suggesting experts rely on the identification of characteristic phenotypes within subsets of each population. We also i

SUBMITTER: Yao K 

PROVIDER: S-EPMC6749053 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature