Abstract
Fluorescence microscopy has enabled a dramatic development in modern biology by visualizing biological organisms with micrometer scale resolution. However, due to the diffraction limit, sub-micron/nanometer features are difficult to resolve. While various super-resolution techniques are developed to achieve nanometer-scale resolution, they often either require expensive optical setup or specialized fluorophores. In recent years, deep learning has shown potentials to reduce the technical barrier and obtain super-resolution from diffraction-limited images. For accurate results, conventional deep learning techniques require thousands of images as a training dataset. Obtaining large datasets from biological samples is not often feasible due to photobleaching of fluorophores, phototoxicity, and dynamic processes occurring within the organism. Therefore, achieving deep learning-based super-resolution using small datasets is challenging. We address this limitation with a new convolutional neural network based approach that is successfully trained with small datasets and achieves super-resolution images. We captured 750 images in total from 15 different field-of-views as the training dataset to demonstrate the technique. In each FOV, a single target image is generated using the super-resolution radial fluctuation method. As expected, this small dataset failed to produce a usable model using traditional super-resolution architecture. However, using the new approach, a network can be trained to achieve super-resolution images from this small dataset. This deep learning model can be applied to other biomedical imaging modalities such as MRI and X-ray imaging, where obtaining large training datasets is challenging.
Yide Zhang
NIH K99 Postdoctoral Fellow
My research is interdisciplinary and focused on developing new types of optical imaging techniques that could advance the work of other researchers and medical personnel in a wide variety of fields. Currently, I am developing next-generation photoacoustic and ultrafast imaging techniques that can observe biological and physical phenomena that are too fast to be imaged with existing methods. The observation of the ultrafast phenomena could provide a better understanding of the fundamentals of life and physical sciences. I am also developing novel quantum imaging approaches that can investigate biological organisms with an imaging performance that cannot be achieved using classical optical imaging. In my free time, I enjoy cooking, hiking, cycling, and traveling.