Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada.
Departments of Computer Science, University of Calgary, Calgary, Alberta, Canada.
J Morphol. 2021 Sep;282(9):1362-1373. doi: 10.1002/jmor.21391. Epub 2021 Jul 3.
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.
虽然有大量研究研究了附着在骨头上的各种软组织的性质，但相对较少的研究关注这些组织附着部位的骨的微观特性。使用扫描电子显微镜生成 1600 张软组织附着部位图像的数据集，具有新颖卷积神经网络架构的图像分类程序可以根据微观结构形态中的观察模式，按软组织类型对附着区域的图像进行分类。使用染色组织学薄切片和液晶正交偏光显微镜，确定可以从小结构定量确定软组织类型。主要诊断特征是胶原纤维的方向和整个附着区域厚度内胶原密度的异质性。这些测定是在广泛的分类学采样和多个骨骼元素上进行的。
