Automated high-content morphological analysis of muscle fiber histology

Author(s)

Miazaki, Mauro ^{[1, 2]} ; Viana, Matheus P. ^[2] ; Yang, Zhong ^{[3, 4]} ; Comin, Cesar H. ^[2] ; Wang, Yaming ^[3] ; Costa, Luciano da F. ^{[2, 5]} ; Xu, Xiaoyin ^[6]

Affiliation

^[1] Midwestern State Univ, Dept Comp Sci, Guarapuava, PR - Brazil

^[2] Univ Sao Paulo, Inst Phys Sao Carlos, Sao Carlos, SP - Brazil

^[3] Brigham & Womens Hosp, Dept Anesthesia, Boston, MA 02115 - USA

^[4] Third Mil Med Univ, Southwestern Hosp, Dept Clin Hematol, Chongqing - Peoples R China

^[5] Natl Inst Sci & Technol Complex Syst, Niteroi, RJ - Brazil

^[6] Brigham & Womens Hosp, Dept Radiol, Boston, MA 02115 - USA

• Document type: Journal article
• Source: COMPUTERS IN BIOLOGY AND MEDICINE; v. 63, p. 28-35, AUG 1 2015.
• Web of Science Citations: 5

Abstract

In the search for a cure for many muscular disorders it is often necessary to analyze muscle fibers under a microscope. For this morphological analysis, we developed an image processing approach to automatically analyze and quantify muscle fiber images so as to replace today's less accurate and time-consuming manual method. Muscular disorders, that include cardiomyopathy, muscular dystrophies, and diseases of nerves that affect muscles such as neuropathy and myasthenia gravis, affect a large percentage of the population and, therefore, are an area of active research for new treatments. In research, the morphological features of muscle fibers play an important role as they are often used as biomarkers to evaluate the progress of underlying diseases and the effects of potential treatments. Such analysis involves assessing histopathological changes of muscle fibers as indicators for disease severity and also as a criterion in evaluating whether or not potential treatments work. However, quantifying morphological features is time-consuming, as it is usually performed manually, and error-prone. To replace this standard method, we developed an image processing approach to automatically detect and measure the cross-sections of muscle fibers observed under microscopy that produces faster and more objective results. As such, it is well-suited to processing the large number of muscle fiber images acquired in typical experiments, such as those from studies with pre-clinical models that often create many images. Tests on real images showed that the approach can segment and detect muscle fiber membranes and extract morphological features from highly complex images to generate quantitative results that are readily available for statistical analysis. (C) 2015 Elsevier Ltd. All rights reserved. (AU)

• FAPESP's process: 07/50988-1 - Study of form, function and gene expression in neuroscience
• Grantee: Mauro Miazaki
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
• Grantee: Roberto Marcondes Cesar Junior
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 11/22639-8 - Unveiling the relationship between structure and dynamics on modular networks
• Grantee: Cesar Henrique Comin
• Support Opportunities: Scholarships in Brazil - Doctorate