CNN-based approach for 3D artifact correction of intensity diffraction tomography images

3D reconstructions after tomographic imaging often suffer from elongation artifacts due to the limited-angle acquisitions. Retrieving the original 3D shape is not an easy task, mainly due to the intrinsic morphological changes that biological objects undergo during their development. Here we present to the best of our knowledge a novel approach for correcting 3D artifacts after 3D reconstructions of intensity-only tomographic acquisitions. The method relies on a network architecture that combines a volumetric and a 3D finite object approach. The framework was applied to time-lapse images of a mouse preimplantation embryo developing from fertilization to the blastocyst stage, proving the correction of the axial elongation and the recovery of the spherical objects. This work paves the way for novel directions on a generalized non-supervised pipeline suited for different biological samples and imaging conditions.