We present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity non-destructively on unlabeled specimens. ACM is equipped with a laser scanning confocal microscopy with a quantitative phase imaging module, which provides optical path-length maps of the specimen colocalized with the fluorescence channel. Using pairs of phase and fluorescence images, a convolution neural network was trained to translate the former into the latter. The ACM images hold much stronger depth sectioning than the input (phase) images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and three-dimensional liver cancer spheroids.
We propose a multimodal imaging system, LS-GLIM, consisting of the light scanning microscope (LSM 900, Zeiss) and the gradient light interference microscope (GLIM) module [1], with photomultiplier tube (PMT) detection. GLIM upgrades a differential interference microscope with a liquid crystal variable retarder (LCVR), which introduces further controlled phase shifts between the x and y polarized light. The quantitative phase information can be retrieved from the four frames corresponding to each phase shift in the GLIM. We anticipate a broad range of applications that LS-GLIM will enable through phase imaging with computational specificity (PICS), especially in thick, highly-scattering samples.
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