  Automatic prediction of fluorescently labeled organelles from label-free
transmitted light input images is an important, yet difficult task. The
traditional way to obtain fluorescence images is related to performing
biochemical labeling which is time-consuming and costly. Therefore, an
automatic algorithm to perform the task based on the label-free transmitted
light microscopy could be strongly beneficial. The importance of the task
motivated researchers from the France-BioImaging to organize the LightMyCells
challenge where the goal is to propose an algorithm that automatically predicts
the fluorescently labeled nucleus, mitochondria, tubulin, and actin, based on
the input consisting of bright field, phase contrast, or differential
interference contrast microscopic images. In this work, we present the
contribution of the AGHSSO team based on a carefully prepared and trained
encoder-decoder deep neural network that achieves a considerable score in the
challenge, being placed among the best-performing teams.
