Image-based Cell Phenotyping Using Deep Learning
Berryman, S. G.; Matthews, K.; Lee, J. H.; Duffy, S. P.; Ma, H.
Show abstract
The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used for image cytometry but has been limited by cell agglomeration and it is unclear if this approach can reliably phenotype cells indistinguishable to the human eye. Here, we show disaggregated single cells can be phenotyped with a high degree of accuracy using low-resolution bright-field and non-specific fluorescence images of the nucleus, cytoplasm, and cytoskeleton. Specifically, we trained a convolutional neural network using automatically segmented images of cells from eight standard cancer cell-lines. These cells could be identified with an average classification accuracy of 94.6%, tested using separately acquired images. Our results demonstrate the potential to develop an \"electronic eye\" to phenotype cells directly from microscopy images indistinguishable to the human eye.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A deep learning approach for staging embryonic tissue isolates with small data 96%
- High-volume, label-free imaging for quantifying single-cell dynamics in induced pluripotent stem cell colonies 95%
- Caveolae and scaffold detection from single molecule localization microscopy data using deep learning 95%
Similar papers in this journal
- ImmuNet: A Segmentation-Free Machine Learning Pipeline for Immune Landscape Phenotyping in Tumors by Muliplex Imaging 94%
- tUbe net: a generalisable deep learning tool for 3D vessel segmentation 92%
- Live Cell Fluorescence Microscopy - An End-to-End Workflow for High-Throughput Image and Data Analysis 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.