Deep Learning Enhanced Electrochemiluminescence Microscopy.

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Abstract

Limited by the efficiency of electrochemiluminescence, tens of seconds of exposure time are typically required to get a high-quality image. Image enhancement of short exposure time images to obtain a well-defined electrochemiluminescence image can meet the needs of high-throughput or dynamic imaging. Here, we propose deep enhanced ECL microscopy (DEECL), a general strategy that utilizes artificial neural networks to reconstruct electrochemiluminescence images with millisecond exposure times to have similar quality as high-quality electrochemiluminescence images with second-long exposure time. Electrochemiluminescence imaging of fixed cells demonstrates that DEECL allows improvement of the imaging efficiency by 1 to 2 orders than usual. This approach is further used for a data-intensive analysis application, cell classification, achieving an accuracy of 85% with ECL data at an exposure time of 50 ms. We anticipate that the computationally enhanced electrochemiluminescence microscopy will enable fast and information-rich imaging and prove useful for understanding dynamic chemical and biological processes.