Here we explore use of a CNN for a completely distinct kind of application in cell biology, NOT a classification task. Instead we develop a conditional autoencoder framework that has two applications:
1) Statistically accurate image generation based on learning correlated features within a large image data set, to predict distributions of fluorescent labels that are not directly observed. It is important to emphasize that this approach is COMPLETELY DIFFERENT from the pixel-by-pixel classification approaches described above, as we can use this to learn and measure population distributions of organelles within cells, explore their relationships to one another, etc., not just predict a distribution in a given transmitted light image. The VAE architecture also makes the generative function much more flexible than other alternatives, in that we can generate expected organelle distributions for an artificial chosen reference shape (e.g. a cubical cell with a spherical nucleus).
2) Practical, nonlinear dimensionality reduction for extremely high dimensional image data (number of voxels * number of channels). This enables us to, for example, construct a statistically meaningful “average” cell from a population, determine whether a particular cell represents a common or unusual phenotype, and quantitatively measure changes in cell organization as a function of cell state (mitotic state, drug treatment, etc.).
Issue summary
Make our two key contributions stand out
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