Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2020
2020
Historique:
received:
21
04
2020
accepted:
16
11
2020
entrez:
28
12
2020
pubmed:
29
12
2020
medline:
21
1
2021
Statut:
epublish
Résumé
Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger's line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed segmentation. Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~92.4%. The software has been tested to work on full muscle cross-sections and works well even under non-optimal staining conditions. The MyoSAT software tool has been implemented as a macro for the freely available ImageJ software platform. This new segmentation tool allows scientists to efficiently analyze large muscle cross-sections for use in research studies and diagnostics.
Identifiants
pubmed: 33362264
doi: 10.1371/journal.pone.0243163
pii: PONE-D-20-11392
pmc: PMC7757813
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0243163Subventions
Organisme : NIDCD NIH HHS
ID : R01 DC017171
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
Lancet. 1986 Feb 8;1(8476):307-10
pubmed: 2868172
Arch Neurol. 1970 Oct;23(4):369-79
pubmed: 4248905
Neuromuscul Disord. 2004 Oct;14(10):675-82
pubmed: 15351425
J Lipid Res. 2005 Feb;46(2):230-6
pubmed: 15576838
Bioinformatics. 2016 Nov 15;32(22):3532-3534
pubmed: 27412086
J Muscle Res Cell Motil. 1989 Jun;10(3):197-205
pubmed: 2547831
BMC Med. 2013 Mar 20;11:77
pubmed: 23514382
Comput Biol Med. 2015 Aug;63:28-35
pubmed: 26004825
BMC Med Imaging. 2014 Oct 29;14:38
pubmed: 25352214
Cytometry. 1998 Aug 1;32(4):317-26
pubmed: 9701401
Monogr Clin Cytol. 1984;9:101-16
pubmed: 6238233
Sports Med. 1986 May-Jun;3(3):190-200
pubmed: 3520748
Muscle Nerve. 2016 Aug;54(2):292-9
pubmed: 26788932
J Anat. 1989 Apr;163:1-5
pubmed: 2558097
J Microsc. 2013 Dec;252(3):275-85
pubmed: 24118017
Histochem Cell Biol. 2010 Sep;134(3):307-17
pubmed: 20711601
Skelet Muscle. 2014 Nov 27;4:21
pubmed: 25937889
J Appl Physiol (1985). 2013 Jan 1;114(1):148-55
pubmed: 23139362
J Neurosci Methods. 2016 Sep 15;271:34-42
pubmed: 27268155
Development. 2011 Sep;138(17):3657-66
pubmed: 21828094
J Appl Physiol (1985). 2018 Jan 01;124(1):40-51
pubmed: 28982947