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