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

• Main Authors: Prinke, Philipp, Haueisen, Jens, Klee, Sascha, Rizqie, Muhammad Qurhanul, Supriyanto, Eko, Konig, Karsten, Breunig, Hans Georg, Piatek, Lukasz
• Format: Article
• Language: English
• Published: Nature Research 2021
• Subjects: QD Chemistry; QH Natural history
• Online Access: http://eprints.utm.my/id/eprint/94208/1/EkoSupriyanto2021_AutomaticSegmentationofSkinCells.pdf; http://eprints.utm.my/id/eprint/94208/; http://dx.doi.org/10.1038/s41598-021-93682-y

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.