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Automated analysis of skeletal muscle fiber cross‐sectional area using CyteSeer image analysis program
Author(s) -
Kostrominova Tatiana Y,
Ingermanson Randall,
Laris Casey A,
Loy Patricia,
McDonough Patrick M
Publication year - 2010
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.24.1_supplement.455.3
Subject(s) - skeletal muscle , muscle fibre , skeletal muscle fibers , cross validation , fiber , cross sectional study , computer science , statistical analysis , biomedical engineering , medicine , anatomy , artificial intelligence , mathematics , materials science , pathology , statistics , composite material
The accurate quantification of skeletal muscle fiber cross‐sectional areas is desired in diverse areas of biomedical research. Majority of current studies employ time consuming analysis techniques with manual outlining of muscle fibers using Image J software. The goal of our study was to adapt CyteSeer, a recently developed automated image analysis program, for the quantification of skeletal muscle fiber cross‐sectional areas. Initial CyteSeer algorithm created masks located in the middle of the endomysium between adjacent muscle fibers. This caused the values of the CyteSeer analyzed muscle fiber cross‐sectional areas to be much larger (~25%) when compared with manual analysis of the same fibers. Subsequent modification of the CyteSeer algorithm allowed accurate quantification of skeletal muscle fiber cross‐sectional area. Automated analysis required only 5 seconds/image versus several hours/image needed for the manual analysis. Manual and automated analysis of muscle fiber cross‐sectional areas was performed side‐by‐side for samples from rats, mice, pigs, and monkeys. It showed very high degree of correlation with an average error of 3.2%. The results demonstrate that CyteSeer can be used for accurate automated quantification of skeletal muscle fiber cross‐sectional areas while greatly reducing time required for the analysis. Support: NIH R41AR55604 (PM).