Inferential mining for reconstruction of 3D cell structures in Atomic Force Microscopy imaging

Atomic Force Microscopy (AFM) is a fundamental tool for the investigation of a wide range of mechanical properties on nanoscale due to the contact interaction between the AFM tip and the sample surface. The focus of this paper is on an algorithm for the reconstruction of 3D stem-differentiated cell structures extracted by typical 2D surface AFM images. The AFM images resolution is limited by the tip-sample convolution due to the combined geometry of the probe tip and the pattern configuration of the sample. This limited resolution limits the accuracy of the correspondent 3D image. To drop unwanted effects, we adopt an inferential method for pre-processing single frame AFM image (low resolution image) building its super-resolution version. Therefore the 3D reconstruction is made on animal cells using a Markov Random Field approach for augmented voxels. The 3D reconstruction should improve unambiguous identification of cells structures. The computation method is fast and can be applied both to multi- and to single-frame images.