A Bayesian 3D Volume Reconstruction for Confocal Micro-rotation Cell Imaging

Recently, micro-rotation confocal microscopy has enabled the acquisition of a sequence of slices for a non-adherent living cells where the slices' positions are roughly controlled by a dielectric-field biological cage. The high resolution volume reconstruction requires then the integration of precise alignment of slice positions. We propose in the Bayesian context, a new method combining both slice positioning and 3D volume reconstruction simultaneously, which leads naturally to an energy minimization procedure of a variational problem. An automatic calibration paradigm via Maximum Likelihood estimation (MLE) principle is used for the relative hyper-parameter determination. We provide finally experimental comparison results on both conventional z-stack confocal images and 3D volume reconstruction from micro-rotation slices of the same non-adherent living cell to show its potential biomedical application.