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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 of the model, while the stability index indicates segment quality in the multi-stage merging process. These two new features, combined with the local gradient magnitude and compactness, are used for the model-based fuzzy evaluation of the cell segments. We exemplify our approach on an image stack with 200 × 200 × 100 μm3, including the skin layers of the stratum spinosum and the stratum basale of a healthy volunteer. Our image processing pipeline contributes to the fully automated classification of human skin cells in multiphoton data and provides a basis for the detection of skin cancer using non-invasive optical biopsy. [ABSTRACT FROM AUTHOR]

Five dimensional microscopy with a 12-fs laser scanning microscope based on spectrally resolved two-photon autofluorescence lifetime and second-harmonic generation (SHG) imaging was used to characterize all layers of the porcine cornea. This setup allowed the simultaneous excitation of both metabolic cofactors, NAD (P)H and flavins, and their discrimination based on their spectral emission properties and fluorescence decay characteristics. Furthermore, the architecture of the stromal collagen fibrils was assessed by SHG imaging in both forward and backward directions. Information on the metabolic state and the tissue architecture of the porcine cornea were obtained with subcellular resolution, and high temporal and spectral resolutions. [ABSTRACT FROM AUTHOR]