Automatic segmentation of skin cells in multiphoton data using multi-stage merging.

ABSTRACT: We propose a novel automatic segmentation algorithm that separates the components of human skin cells from the rest of the tissue in fluorescence data of three-dimensional scans using non-invasive multiphoton tomography. The algorithm encompasses a multi-stage merging on preprocessed superpixel images to ensure independence from a single empirical global threshold. This leads to a high robustness of the segmentation considering the depth-dependent data characteristics, which include variable contrasts and cell sizes. The subsequent classification of cell cytoplasm and nuclei are based on a cell model described by a set of four features. Two novel features, a relationship between outer cell and inner nucleus (OCIN) and a stability index, were derived. The OCIN feature describes the topology

SUBMITTER: Prinke P 

PROVIDER: S-EPMC8282875 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature