Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting
Fig 1
An overview of the inputs and targets to the network in paired cell inpainting, and of our proposed architecture.
(A) Inputs and targets to the network. We crop a source cell (green border) and a target cell (orange border) from the same image. Then, given all channels for the source cell, and the structural markers for the target cell (in this dataset, the nucleus and the microtubule channels), the network is trained to predict the appearance of the protein channel in the target cell. Images shown are of human cells, with the nucleus colored blue, microtubules colored red, and a specific protein colored green. (B) Example images from the proteome-scale datasets we use in this study. We color the protein channel for each dataset in green. The CyCLOPS yeast dataset has a cytosolic RFP, colored in red. The NOP1pr-ORF yeast dataset has a brightfield channel, colored grey. The Nup49-RFP GFP-ORF yeast dataset has a RFP fused to a nuclear pore marker, colored in red. The Human Protein Atlas images are shown as described above in A. (C) Our proposed architecture. Our architecture consists of a source cell encoder and a target marker encoder. The final layers of both encoders are concatenated and fed into a decoder that outputs the prediction of the target protein . Layers in our CNN are shown as solid-colored boxes; we label each of the convolutional layers (solid grey boxes) with the number of filters used (e.g. 3x3 Conv 96 means 96 filters are used). We show a real example of a prediction from our trained human cell model given the input image patches in this schematic.