Phase Contrast and Differential Interference Contrast (DIC) microscopy are two popular noninvasive techniques for monitoring live cells. Each of these two imaging modalities has its own advantages and disadvantages to visualize specimens, so biologists need these two complementary modalities together to analyze specimens. In this paper, we propose a novel data-driven learning method capable of transferring microscopy images from one imaging modality to the other imaging modality, reflecting the characteristics of specimens from different perspectives. For example, given a Phase Contrast microscope, we can transfer its images to the corresponding DIC images without using DIC microscope, vice versa. The preliminary experiments demonstrate that the image transfer approach can provide biologists a computational way to switch between microscopy imaging modalities, so biologists can combine the advantages of different imaging modalities to better visualize and analyze specimens over time, without purchasing all types of microscopy imaging modalities or switching between imaging systems back-and-forth during time-lapse experiments.
Machine Vision and Applications – Springer Journals
Published: Jun 6, 2018
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