A multimethod approach to the differentiation of enthesis bone microstructure based on soft tissue type
Date
2021-06
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley
Abstract
Whereas there is a wealth of research studying the nature of various soft tissues that attach to bone, comparatively little research focuses on the bone's microscopic properties in the area where these tissues attach. Using scanning electron microscopy to generate a dataset of 1600 images of soft tissue attachment sites, an image classification program with novel convolutional neural network architecture can categorize images of attachment areas by soft tissue type based on observed patterns in microstructure morphology. Using stained histological thin section and liquid crystal cross-polarized microscopy, it is determined that soft tissue type can be quantitatively determined from the microstructure. The primary diagnostic characters are the orientation of collagen fibers and heterogeneity of collagen density throughout the attachment area thickness. These determinations are made across broad taxonomic sampling and multiple skeletal elements.
Description
Keywords
anatomy, convolutional neural network, cross-polarized light microscopy, Enthesis, image classification, scanning electron microscopy, soft tissue reconstruction, vertebrate paleontology
Citation
Whitebone, S. A., Bari, A. S. M. H., Gavrilova, M. L., & Anderson, J. S. (2021). A multimethod approach to the differentiation of enthesis bone microstructure based on soft tissue type. Journal of Morphology, 282(9), 1362–1373. https://doi.org/10.1002/jmor.21391