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9 March 2020 Deep-learning based three-dimensional virtual refocusing of fluorescence microscopy images (Conference Presentation)
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Abstract
We report a digital image refocusing framework in fluorescence microscopy (termed “Deep-Z”), where a deep neural network is trained to virtually-refocus a 2D fluorescence image onto user-defined 3D surfaces. Using Deep-Z, we demonstrated 3D reconstruction of C. elegans neuronal activity from a 2D movie, digitally increasing the depth-of-field by 20-fold. We also demonstrated digital correction of sample drift, tilt and other image aberrations, all performed after the acquisition of a single image. Deep-Z also permits cross-modality virtual refocusing, where a single 2D wide-field image can be digitally refocused to match a confocal microscopy image captured at a different sample plane.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yichen Wu, Yair Rivenson, Hongda Wang, Yilin Luo, Eyal Ben-David, Laurent A. Bentolila, Christian Pritz, and Aydogan Ozcan "Deep-learning based three-dimensional virtual refocusing of fluorescence microscopy images (Conference Presentation)", Proc. SPIE 11245, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII, 112450P (9 March 2020); https://doi.org/10.1117/12.2545249
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