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Automated high-content morphological analysis of muscle fiber histology.
Miazaki, Mauro; Viana, Matheus P; Yang, Zhong; Comin, Cesar H; Wang, Yaming; da F Costa, Luciano; Xu, Xiaoyin.
Afiliação
  • Miazaki M; Institute of Physics at Sao Carlos, University of Sao Paulo, Sao Carlos, SP, Brazil; Department of Computer Science, Midwestern State University, Guarapuava, PR, Brazil.
  • Viana MP; Institute of Physics at Sao Carlos, University of Sao Paulo, Sao Carlos, SP, Brazil.
  • Yang Z; Department of Anesthesia, Brigham and Women's Hospital, Boston, MA, USA; Department of Clinical Hematology, Southwestern Hospital, The Third Military Medical University, Chongqing, China.
  • Comin CH; Institute of Physics at Sao Carlos, University of Sao Paulo, Sao Carlos, SP, Brazil.
  • Wang Y; Department of Anesthesia, Brigham and Women's Hospital, Boston, MA, USA.
  • da F Costa L; Institute of Physics at Sao Carlos, University of Sao Paulo, Sao Carlos, SP, Brazil; National Institute of Science and Technology for Complex Systems, Niteroi, RJ, Brazil.
  • Xu X; Department of Radiology, Brigham and Women's Hospital, 20 Shattuck Street, Boston, MA 02115, USA. Electronic address: xxu@bwh.harvard.edu.
Comput Biol Med ; 63: 28-35, 2015 Aug.
Article em En | MEDLINE | ID: mdl-26004825
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Fibras Musculares Esqueléticas / Doenças Musculares Tipo de estudo: Guideline / Prognostic_studies Limite: Animals Idioma: En Revista: Comput Biol Med Ano de publicação: 2015 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Fibras Musculares Esqueléticas / Doenças Musculares Tipo de estudo: Guideline / Prognostic_studies Limite: Animals Idioma: En Revista: Comput Biol Med Ano de publicação: 2015 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos