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 superp...

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Autores principales:	Philipp Prinke, Jens Haueisen, Sascha Klee, Muhammad Qurhanul Rizqie, Eko Supriyanto, Karsten König, Hans Georg Breunig, Łukasz Piątek
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ec01b91a3ba442d99811f64455d57403
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spelling	oai:doaj.org-article:ec01b91a3ba442d99811f64455d574032021-12-02T15:33:12ZAutomatic segmentation of skin cells in multiphoton data using multi-stage merging10.1038/s41598-021-93682-y2045-2322https://doaj.org/article/ec01b91a3ba442d99811f64455d574032021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93682-yhttps://doaj.org/toc/2045-2322Abstract 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.Philipp PrinkeJens HaueisenSascha KleeMuhammad Qurhanul RizqieEko SupriyantoKarsten KönigHans Georg BreunigŁukasz PiątekNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Philipp Prinke Jens Haueisen Sascha Klee Muhammad Qurhanul Rizqie Eko Supriyanto Karsten König Hans Georg Breunig Łukasz Piątek Automatic segmentation of skin cells in multiphoton data using multi-stage merging
description	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.
format	article
author	Philipp Prinke Jens Haueisen Sascha Klee Muhammad Qurhanul Rizqie Eko Supriyanto Karsten König Hans Georg Breunig Łukasz Piątek
author_facet	Philipp Prinke Jens Haueisen Sascha Klee Muhammad Qurhanul Rizqie Eko Supriyanto Karsten König Hans Georg Breunig Łukasz Piątek
author_sort	Philipp Prinke
title	Automatic segmentation of skin cells in multiphoton data using multi-stage merging
title_short	Automatic segmentation of skin cells in multiphoton data using multi-stage merging
title_full	Automatic segmentation of skin cells in multiphoton data using multi-stage merging
title_fullStr	Automatic segmentation of skin cells in multiphoton data using multi-stage merging
title_full_unstemmed	Automatic segmentation of skin cells in multiphoton data using multi-stage merging
title_sort	automatic segmentation of skin cells in multiphoton data using multi-stage merging
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ec01b91a3ba442d99811f64455d57403
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Automatic segmentation of skin cells in multiphoton data using multi-stage merging

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