Deep learning microscopy
Published on 2017-11-17T19:58:04Z (GMT) by
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, 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 specimen and establish new transformations among different modes of imaging.
Cite this collection
Rivenson, Yair; Göröcs, Zoltán; Günaydın, Harun; Zhang, Yibo; Wang, Hongda; Ozcan, Aydogan (2017): Deep learning microscopy. The Optical Society.