Deep learning microscopy
This week a new paper by the group leaded by A. Ozcan appeared in Optica.
Deep learning microscopy,
Y. Ribenson et al, at Optica
(featured image exctracted from Fig. 6 of the supplement)
We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field of view and depth of field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with better resolution, matching the performance of higher numerical aperture lenses and also significantly surpassing their limited field of view and depth of field. These results are significant for various fields that use microscopy tools, including, e.g., life sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond such applications, the presented approach might be applicable to other imaging modalities, also spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better as they continue to image specimens and establish new transformations among different modes of imaging.
By using different images obtained with high/low numerical aperture microscope objectives, they have trained a deep neural network to create high spatial resolution images from low spatial resolution ones. Moreover, the final result matches the field of view of the input image, thus obtaining one of the major goals of optical microscopy: high resolution and high field of view at the same time (and using a low numerical aperture objective).
I really liked the supplement, where they give information about the neural network (which is really useful for a newbie like me).