Towards Applying Machine Learning in Volumetric 3D Microscopy
Light propagating through a non-uniform medium scatters as it interacts with particles with different refractive properties such as cells in the tissue. In this work we aim to utilize this scattering process to learn a volumetric reconstruction of scattering parameters, in particular partical densities. We target microscopy applications were coherent spackle effect are an integral part of the imaging process. We argue that the key for successful learning is modeling realistic speckles in the training process, for which we build on the development of recent physically accurate speckle simulators. We also explore how to better incorporate speckle statistics such as the memory effect in the learning framework. Overall, this paper contributes an analysis of multiple aspects of the network design including the learning architecture, the training data and the desired input features. We hope this study will pave the road for future design of learning based imaging systems in this challenging domain.
*MSc student under the supervision of Prof. Anat Levin.