Training Strategy For Limited Labeled Data By Learning From Confusion

Tilt-series cryo-electron tomography (cryoET) is an established imaging technique used in several scientific fields to determine samples' three-dimensional (3D) structures at near-atomic resolutions. However, the motion and misalignment that occur during the acquisition stage are major limiting factors to reaching smaller resolutions. indeed, they introduce blur and artifacts, which deteriorate the reconstruction quality. in this paper, we propose a joint motion-correction and reconstruction framework to improve the quality of the output volume and, consequently, its resolution. Using embedded fiducial markers, our framework first estimates the motion field in the sample in order to correct the captured data. Then an iterative Plug-and-Play approach is used to reconstruct the sample. The validation of our approach on real captured datasets shows a good quality reconstruction translated in a resolution improvement.