Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping.

ABSTRACT: The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance

SUBMITTER: Joshi S 

PROVIDER: S-EPMC10942457 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature