Seeing the invisible in differential interference contrast microscopy images.

Abstract

Automated microscopy image restoration, especially in Differential Interference Contrast (DIC) imaging modality, has attracted increasing attentions since it greatly facilitates long-term living cell analysis without staining. Although the previous work on DIC image restoration is able to restore the nuclei regions of living cells, it is still challenging to reconstruct the unnoticeable cytoplasm details in DIC images. In this paper, we propose to extract the tiny movement information of living cells in DIC images and reveal the hidden details in DIC images by magnifying the cells' motion as well as attenuating the intensity variation from the background. From our restored images, we can clearly observe the previously-invisible details in DIC images. Experiments on two DIC image datasets show that the motion-based restoration method can reveal the hidden details of living cells. In addition, we demonstrate our restoration method can also be applied to other imaging modalities such as the phase contrast microscopy to enhance cells' details. Furthermore, based on the pixel-level restoration results, we can obtain the object-level segmentation by leveraging a label propagation approach, providing promising results on facilitating the cell shape and behavior analysis. The proposed algorithm can be a software module to enhance the visualization capability of microscopes.