Automated macrophage counting in DLBCL tissue samples: a ROF filter based approach.
Automated cell counting
CD14
CD163
Floating threshold
Immunohistochemical staining
Macrophage
ROF filtering
Rule-based detection
Journal
Biological procedures online
ISSN: 1480-9222
Titre abrégé: Biol Proced Online
Pays: England
ID NLM: 100963717
Informations de publication
Date de publication:
2019
2019
Historique:
received:
21
02
2019
accepted:
08
05
2019
entrez:
16
7
2019
pubmed:
16
7
2019
medline:
16
7
2019
Statut:
epublish
Résumé
For analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples, it is desirable to obtain information about counts and distribution of different macrophage subtypes. Until now, macrophage counts are mostly inferred from gene expression analysis of whole tissue sections, providing only indirect information. Direct analysis of immunohistochemically (IHC) fluorescence stained tissue samples is confronted with several difficulties, e.g. high variability of shape and size of target macrophages and strongly inhomogeneous intensity of staining. Consequently, application of commercial software is largely restricted to very rough analysis modes, and most macrophage counts are still obtained by manual counting in microarrays or high power fields, thus failing to represent the heterogeneity of tumor microenvironment adequately. We describe a Rudin-Osher-Fatemi (ROF) filter based segmentation approach for whole tissue samples, combining floating intensity thresholding and rule-based feature detection. Method is validated against manual counts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and a straightforward machine-learning approach in a set of 50 test images. Further, the novel method and both commercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expression data available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies. Among all tested methods, the novel approach is best correlated with manual count (0.9297). Automated detection of evaluation subregions proved to be fully reliable. Comparison with gene expression data obtained for the same tissue samples reveals only moderate to low correlation levels. Subsampling within tumor subregions is possible with results almost identical to full sampling. Mean macrophage size in tumor subregions is 152.5±111.3 ROF based approach is successfully applied to detection of IHC stained macrophages in DLBCL tissue samples. The method competes well with existing commercial software kits. In difference to them, it is fully automated, externally repeatable, independent on training data and completely documented. Comparison with gene expression data indicates that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution.
Sections du résumé
BACKGROUND
BACKGROUND
For analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples, it is desirable to obtain information about counts and distribution of different macrophage subtypes. Until now, macrophage counts are mostly inferred from gene expression analysis of whole tissue sections, providing only indirect information. Direct analysis of immunohistochemically (IHC) fluorescence stained tissue samples is confronted with several difficulties, e.g. high variability of shape and size of target macrophages and strongly inhomogeneous intensity of staining. Consequently, application of commercial software is largely restricted to very rough analysis modes, and most macrophage counts are still obtained by manual counting in microarrays or high power fields, thus failing to represent the heterogeneity of tumor microenvironment adequately.
METHODS
METHODS
We describe a Rudin-Osher-Fatemi (ROF) filter based segmentation approach for whole tissue samples, combining floating intensity thresholding and rule-based feature detection. Method is validated against manual counts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and a straightforward machine-learning approach in a set of 50 test images. Further, the novel method and both commercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expression data available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies.
RESULTS
RESULTS
Among all tested methods, the novel approach is best correlated with manual count (0.9297). Automated detection of evaluation subregions proved to be fully reliable. Comparison with gene expression data obtained for the same tissue samples reveals only moderate to low correlation levels. Subsampling within tumor subregions is possible with results almost identical to full sampling. Mean macrophage size in tumor subregions is 152.5±111.3
CONCLUSIONS
CONCLUSIONS
ROF based approach is successfully applied to detection of IHC stained macrophages in DLBCL tissue samples. The method competes well with existing commercial software kits. In difference to them, it is fully automated, externally repeatable, independent on training data and completely documented. Comparison with gene expression data indicates that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution.
Identifiants
pubmed: 31303867
doi: 10.1186/s12575-019-0098-9
pii: 98
pmc: PMC6600891
doi:
Types de publication
Journal Article
Langues
eng
Pagination
13Déclaration de conflit d'intérêts
Competing interestsThe authors declare that they have no competing interests.
Références
Haematologica. 2015 Feb;100(2):143-5
pubmed: 25638802
Haematologica. 2015 Mar;100(3):363-9
pubmed: 25425693
Med Oncol. 2012 Dec;29(4):2317-22
pubmed: 22198695
Blood. 2014 Feb 20;123(8):1214-7
pubmed: 24398326
N Engl J Med. 2008 Nov 27;359(22):2313-23
pubmed: 19038878
J Invest Dermatol. 2017 Nov;137(11):2450-2453
pubmed: 28684330
Haematologica. 2015 Feb;100(2):238-45
pubmed: 25381134
Sci Rep. 2016 Jul 28;6:30347
pubmed: 27464733
Blood. 2013 Sep 12;122(11):1985-6
pubmed: 24030260
J Immunol Methods. 2000 Apr 3;237(1-2):39-50
pubmed: 10725450
Nat Rev Cancer. 2014 Aug;14(8):517-34
pubmed: 25008267
Leuk Res. 2014 Nov;38(11):1374-7
pubmed: 25293515
Front Immunol. 2018 Dec 14;9:2925
pubmed: 30619287
Cell. 2010 Apr 2;141(1):39-51
pubmed: 20371344
Nature. 2000 Feb 3;403(6769):503-11
pubmed: 10676951
N Engl J Med. 2002 Jun 20;346(25):1937-47
pubmed: 12075054
Methods. 2014 Nov;70(1):59-73
pubmed: 25034370
Cold Spring Harb Protoc. 2012 Aug 01;2012(8):
pubmed: 22854572
Obesity (Silver Spring). 2017 Dec;25(12):2100-2107
pubmed: 28985040
BMC Ophthalmol. 2013 Oct 20;13:59
pubmed: 24138794
Cancer Res. 2018 Oct 1;78(19):5492-5503
pubmed: 30206177
Diagn Pathol. 2013 Jun 06;8:92
pubmed: 23829479
Haematologica. 2014 Apr;99(4):715-25
pubmed: 24510338
J Clin Oncol. 2013 Feb 20;31(6):692-700
pubmed: 23182984
Blood. 2004 Jun 1;103(11):4251-8
pubmed: 14976040