Artificial channel separation of monochrome multi-structure microscopy image

Observing subcellular structures labeled by distinguishable fluorescent probes in cells with the microscope is one of the key technologies commonly used in cell biology research. However, due to the spectral overlap, traditional methods of multi-channel sequential imaging of different-colored structures are difficult to overcome the problems of a limited number of labels in a single cell and imaging delay. Here we propose a double-structure network (DBSN) via multiple networks, which can extract six subcellular structures from three images with only two kinds of label markers. DBSN combines the intensity-balance models to even up the diverse densities of fluorescent labels for different structures and the structure-separation models to extract multiple different structures from a single image. The experimental results show that DBSN breaks the bottleneck of the existing technologies on the research of dynamic interaction of organelles and provide a new possibility in drawing the interaction network of organelles. ### Competing Interest Statement The authors have declared no competing interest.