Artificial confocal microscopy for deep label-free imaging

TL;DR

This paper introduces artificial confocal microscopy (ACM), a nondestructive, label-free imaging technique that combines phase imaging and neural networks to achieve confocal-like depth resolution and specificity in thick specimens.

Contribution

The authors develop a novel method that uses phase imaging and deep learning to replicate confocal microscopy features without labels or phototoxicity.

Findings

ACM provides significantly improved depth sectioning over input images.

ACM enables segmentation and measurement of nuclei within dense spheroids.

The trained neural network can be transferred across different media.

Abstract

Widefield microscopy methods applied to optically thick specimens are faced with reduced contrast due to spatial crosstalk, in which the signal at each point is the result of a superposition from neighboring points that are simultaneously illuminated. In 1955, Marvin Minsky proposed confocal microscopy as a solution to this problem. Today, laser scanning confocal fluorescence microscopy is broadly used due to its high depth resolution and sensitivity, which come at the price of photobleaching, chemical, and photo-toxicity. Here, we present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity, on unlabeled specimens, nondestructively. We augmented a laser scanning confocal instrument with a quantitative phase imaging module, which provides optical pathlength maps of the specimen on the same field of view as the fluorescence channel. Using pairs of phase and fluorescence images, we trained a convolution neural network to translate the former into the latter. The training to infer a new tag is very practical as the input and ground truth data are intrinsically registered and the data acquisition is automated. Remarkably, the ACM images present significantly stronger depth sectioning than the input images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and 3D liver cancer spheroids. By training on nucleus-specific tags, ACM allows for segmenting individual nuclei within dense spheroids for both cell counting and volume measurements. Furthermore, taking the estimated fluorescence volumes, as annotation for the phase data, we extracted dry mass information for individual nuclei. Finally, our results indicate that the network learning can be transferred between spheroids suspended in different media.