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Automated multiscale morphometry of muscle disease from second harmonic generation microscopy using tensor-based image processing.

Authors
  • Garbe, Christoph S1
  • Buttgereit, Andreas
  • Schürmann, Sebastian
  • Friedrich, Oliver
  • 1 Interdisciplinary Center for Scientific Computing, University of Heidelberg, 69115 Heidelberg, Germany. [email protected] , (Germany)
Type
Published Article
Journal
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Jan 01, 2012
Volume
59
Issue
1
Pages
39–44
Identifiers
DOI: 10.1109/TBME.2011.2167325
PMID: 21908249
Source
Medline
License
Unknown

Abstract

Practically, all chronic diseases are characterized by tissue remodeling that alters organ and cellular function through changes to normal organ architecture. Some morphometric alterations become irreversible and account for disease progression even on cellular levels. Early diagnostics to categorize tissue alterations, as well as monitoring progression or remission of disturbed cytoarchitecture upon treatment in the same individual, are a new emerging field. They strongly challenge spatial resolution and require advanced imaging techniques and strategies for detecting morphological changes. We use a combined second harmonic generation (SHG) microscopy and automated image processing approach to quantify morphology in an animal model of inherited Duchenne muscular dystrophy (mdx mouse) with age. Multiphoton XYZ image stacks from tissue slices reveal vast morphological deviation in muscles from old mdx mice at different scales of cytoskeleton architecture: cell calibers are irregular, myofibrils within cells are twisted, and sarcomere lattice disruptions (detected as "verniers") are larger in number compared to samples from healthy mice. In young mdx mice, such alterations are only minor. The boundary-tensor approach, adapted and optimized for SHG data, is a suitable approach to allow quick quantitative morphometry in whole tissue slices. The overall detection performance of the automated algorithm compares very well with manual "by eye" detection, the latter being time consuming and prone to subjective errors. Our algorithm outperfoms manual detection by time with similar reliability. This approach will be an important prerequisite for the implementation of a clinical image databases to diagnose and monitor specific morphological alterations in chronic (muscle) diseases.

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