Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments (original) (raw)
Fig 5
Semantic segmentation of a co-culture with MCF10A and NIH-3T3 cells.
A conv-net was trained to both segment and recognize NIH-3T3 and MCF10A cells. The training data was created from separate images of NIH-3T3 and MCF10A cells with each image having a nuclear marker (Hoechst 33342) as a separate channel. (a) A ground truth image of a co-culture containing NIH-3T3 and MCF10A cells. The NIH-3T3 cells express a mCerulean nuclear marker (blue) while the MCF10A cells express an iRFP nuclear marker (red). Hoechst 33342 (image not shown) was also used to generate an image of a nuclear marker. (b) Simultaneous image segmentation and cell-type classification of the image in (a) using a trained conv-net. (c) Classification accuracy for the trained conv-net’s cellular level cell type prediction. The cellular classification score of the correct cell type for each cell is plotted as a histogram. The size of the cellular classification score is strongly associated with making a correct prediction. A classification accuracy of 86% was achieved for NIH-3T3 cells and 100% for MCF10A cells.