Recurrent neural network-based volumetric fluorescence microscopy

Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-based volumetric image inference framework that uses 2D images are sparsely captured by standard wide-field microscope at arbitrary axial positions within the sample volume. Through recurrent convolutional neural network, which term as Recurrent-MZ, information from few planes is explicitly incorporated to digitally reconstruct volume over extended depth-of-field. Using experiments on C. Elegans nanobead samples, Recurrent-MZ demonstrated increase depth-of-field 63x/1.4NA objective lens approximately 50-fold, also providing 30-fold reduction number scans required same We further illustrated generalization this network for 3D showing its resilience varying conditions, e.g., different sequences input images, covering permutations unknown positioning errors. demonstrates first application networks microscopic reconstruction provides flexible rapid framework, overcoming limitations current scanning tools.