Deep learning techniques and mathematical modeling allow 3D analysis of mitotic spindle dynamics.

ABSTRACT: Time-lapse microscopy movies have transformed the study of subcellular dynamics. However, manual analysis of movies can introduce bias and variability, obscuring important insights. While automation can overcome such limitations, spatial and temporal discontinuities in time-lapse movies render methods such as 3D object segmentation and tracking difficult. Here, we present SpinX, a framework for reconstructing gaps between successive image frames by combining deep learning and mathematical object modeling. By incorporating expert feedback through selective annotations, SpinX identifies subcellular structures, despite confounding neighbor-cell information, non-uniform illumination, and variable fluorophore marker intensities. The automation and continuity introduced here allows the precise 3

SUBMITTER: Dang D 

PROVIDER: S-EPMC9998659 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature