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.
Ecole Normale Supérieure de Cachan, France. yu@cmla.ens-cachan.fr
Yong
Y
Yu
Alain
A
Trouvé
Bernard
B
Chalemond
D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D002478:Cells, Cultured / Q000166:cytology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018613:Microscopy, Confocal / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012399:Rotation; D012680:Sensitivity and Specificity