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

Philipp Prinke, Jens Haueisen, Sascha Klee, Muhammad Qurhanul Rizqie, Eko Supriyanto, Karsten König, Hans Georg Breunig, Łukasz Piątek

Research output: Journal article (peer-reviewed)Journal article

2 Citations (Scopus)

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 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.

Original languageEnglish
Article number14534
Pages (from-to)14534
JournalScientific Reports
Volume11
Issue number1
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Algorithms
  • Humans
  • Image Processing, Computer-Assisted/methods
  • Microscopy, Fluorescence, Multiphoton/methods
  • Skin/diagnostic imaging
  • Tomography, Optical/methods

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