Imaging at high spatio-temporal resolution requires a trade-off with image quality leading to low signal-to-noise ratio in acquired data. This renders traditional image analysis methods to perform unreliably. In this thesis I propose methods for image reconstruction, denoising and segmentation using deep learning methods that are robust to noise.