Automatic segmentation of skin cells in multiphoton data using multi-stage merging.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
15 07 2021
15 07 2021
Historique:
received:
14
02
2021
accepted:
27
06
2021
entrez:
16
7
2021
pubmed:
17
7
2021
medline:
16
11
2021
Statut:
epublish
Résumé
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 μm
Identifiants
pubmed: 34267247
doi: 10.1038/s41598-021-93682-y
pii: 10.1038/s41598-021-93682-y
pmc: PMC8282875
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
14534Informations de copyright
© 2021. The Author(s).
Références
PLoS One. 2015 Jul 20;10(7):e0131640
pubmed: 26192624
Sci Rep. 2013;3:2266
pubmed: 23881180
Adv Drug Deliv Rev. 2011 Apr 30;63(4-5):388-404
pubmed: 21514335
Sci Rep. 2020 May 14;10(1):7968
pubmed: 32409755
Bioinformatics. 2014 Sep 15;30(18):2644-51
pubmed: 24849580
IEEE Trans Med Imaging. 2013 Jun;32(6):1121-31
pubmed: 23549886
Opt Lett. 1986 Feb 1;11(2):94
pubmed: 19730544
J Biophotonics. 2017 Sep;10(9):1180-1188
pubmed: 27896951
Dermatol Res Pract. 2012;2012:810749
pubmed: 22203841
Clin Gastroenterol Hepatol. 2004 Sep;2(9):744-53
pubmed: 15354274
J Biomed Opt. 2020 Jan;25(1):1-12
pubmed: 32003191
Biophys J. 1997 Jun;72(6):2405-12
pubmed: 9168018
Cytometry A. 2011 Jul;79(7):545-59
pubmed: 21674772
Methods Appl Fluoresc. 2020 Apr 22;8(3):034002
pubmed: 32320386
J Invest Dermatol. 2012 Apr;132(4):1215-21
pubmed: 22217744
IEEE J Biomed Health Inform. 2013 Jul;17(4):862-9
pubmed: 25055315
Med Image Comput Comput Assist Interv. 2013;16(Pt 1):452-60
pubmed: 24505698
J Biomed Opt. 2003 Jul;8(3):432-9
pubmed: 12880349
Acta Derm Venereol. 2007;87(1):4-8
pubmed: 17225007
IEEE Trans Med Imaging. 2013 Jun;32(6):995-1006
pubmed: 23372077
J Biophotonics. 2017 Apr;10(4):532-541
pubmed: 27090206
Skin Res Technol. 2013 May;19(2):115-24
pubmed: 23441573
Sci Rep. 2012;2:503
pubmed: 22787560
IEEE J Biomed Health Inform. 2014 Jan;18(1):94-108
pubmed: 24403407
Science. 1990 Apr 6;248(4951):73-6
pubmed: 2321027
IEEE Trans Image Process. 2001;10(2):266-77
pubmed: 18249617
J Microsc. 2014 Jan;253(1):54-64
pubmed: 24251410
Quant Imaging Med Surg. 2015 Feb;5(1):17-22
pubmed: 25694949
IEEE Trans Med Imaging. 2019 Jan;38(1):1-10
pubmed: 28796613
Cytometry A. 2013 May;83(5):495-507
pubmed: 23568787