Multi-channel virtual fluorescence microscopy with a learned sensing network

Fluorescence imaging is used throughout biological research to identify subcellular structures, detect neural activity, and differentiate cell types. Multi-channel fluorescence is a challenging subset of fluorescence imaging where multiple fluorescent modes are emitted simultaneously, allowing the detection of a multitude of elements within the specimen (for example, multiple types of neurons). In our work, we demonstrate a learned sensing approach to realize virtual multi-channel fluorescence, by jointly optimizing image illumination and a deep learning neural network that infers labels from brightfield images. We used our setup to demonstrate the influence of key design decisions, such as model architecture, choice of loss function, and amount of input images, on the final optical design. We expect that our work can provide a better understanding of building machine learning based imaging systems and demonstrate the scalability of our illumination optimization technique.