Further processing options
Automatic segmentation of skin cells in multiphoton data using multi-stage merging
Saved in:
| Published in: | Scientific reports day:15; 11(2021) vom: 15. Juli, Artikel-ID 14534, Seite 1-19; volume:11; year:2021; elocationid:14534; pages:1-19; month:07 |
|---|---|
| Authors and Corporations: | , , , , , , , |
| Other Authors: | Haueisen, Jens 1966- [Author] • Klee, Sascha 1978- [Author] • Rizqie, Muhammad Qurhanul [Author] • Supriyanto, Eko [Author] • König, Karsten 1960- [Author] • Breunig, Hans Georg [Author] • Piatek, Lukasz [Author] |
| Type of Resource: | E-Book Component Part |
| Language: | English |
| published: |
2021
|
| Series: |
Scientific reports, 11(2021) vom: 15. Juli, Artikel-ID 14534, Seite 1-19
|
| Source: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
| ISSN: | 2045-2322 |
| License: |
| Summary: | 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 [my]m^3, 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. |
|---|---|
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-021-93682-y |
| Access: | Open Access |