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

Abstract 3D reconstructions after tomographic imaging often suffer from elongation artifacts due to the limited-angle acquisitions. The retrieval of 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 a novel approach for the correction of 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 on time-lapse images of a mouse preimplantation embryo developing from fertilization to the blastocyst stage, proving the correction of the axial elongation, the recovery of the spherical objects and improved quantitative refractive index values. This work paves the way for novel directions on a generalized non-supervised pipeline suited for different biological samples and imaging conditions.