Volumetric segmentation for integral microscopy with Fourier plane recording

Abstract

Light Field (LF) microscopy has emerged as a fast-growing field of interest in recent years due to its undoubted capacity of capturing in-vivo samples from multiple perspectives. In this work, we present a framework for Volume Reconstruction from LF images created following the setup of a Fourier Integral Microscope (FIMic). In our approach we do not use real images, instead, we use a dataset generated in Blender which mimics the capturing process of a FIMic. The resulted images have been used to create a Focal Stack (FS) of the LF, from which Epipolar Plane Images (EPIs) have been extracted. The FS and the EPIs have been used to train three different deep neural networks based on the classic U-Net architecture. The Volumetric Reconstruction is the result of the average of the probabilities produced by such networks.