Whitebone, S AmberBari, A S M HossainGavrilova, Marina LAnderson, Jason S2022-09-212022-09-212021-06Whitebone, 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.213911097-4687http://hdl.handle.net/1880/115252https://doi.org/10.11575/PRISM/46310Whereas 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.Unless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.anatomyconvolutional neural networkcross-polarized light microscopyEnthesisimage classificationscanning electron microscopysoft tissue reconstructionvertebrate paleontologyjournal article2017-04821http://dx.doi.org/10.1002/jmor.21391