Ascenzi Maria-Grazia, Du Xia, Harding James I, Beylerian Emily N, de Silva Brian M, Gross Ben J, Kastein Hannah K, Wang Weiguang, Lyons Karen M, Schaeffer Hayden
Department of Orthopaedic Surgery, University of California, Los Angeles, California 90095, USA.
Department of Mathematics, University of California, Los Angeles, California 90095, USA.
Appl Math (Irvine). 2014 Oct;5(18):2866-2880. doi: 10.4236/am.2014.518273.
Microscopy imaging of mouse growth plates is extensively used in biology to understand the effect of specific molecules on various stages of normal bone development and on bone disease. Until now, such image analysis has been conducted by manual detection. In fact, when existing automated detection techniques were applied, morphological variations across the growth plate and heterogeneity of image background color, including the faint presence of cells (chondrocytes) located deeper in tissue away from the image's plane of focus, and lack of cell-specific features, interfered with identification of cell. We propose the first method of automated detection and morphometry applicable to images of cells in the growth plate of long bone. Through ad hoc sequential application of the Retinex method, anisotropic diffusion and thresholding, our new cell detection algorithm (CDA) addresses these challenges on bright-field microscopy images of mouse growth plates. Five parameters, chosen by the user in respect of image characteristics, regulate our CDA. Our results demonstrate effectiveness of the proposed numerical method relative to manual methods. Our CDA confirms previously established results regarding chondrocytes' number, area, orientation, height and shape of normal growth plates. Our CDA also confirms differences previously found between the genetic mutated mouse and its control mouse on fluorescence images. The CDA aims to aid biomedical research by increasing efficiency and consistency of data collection regarding arrangement and characteristics of chondrocytes. Our results suggest that automated extraction of data from microscopy imaging of growth plates can assist in unlocking information on normal and pathological development, key to the underlying biological mechanisms of bone growth.
小鼠生长板的显微镜成像在生物学中被广泛用于了解特定分子对正常骨骼发育各个阶段以及对骨疾病的影响。到目前为止，此类图像分析一直通过手动检测进行。事实上，当应用现有的自动检测技术时，生长板上的形态变化以及图像背景颜色的异质性，包括位于远离图像焦平面的组织深处的细胞（软骨细胞）的微弱存在，以及缺乏细胞特异性特征，都会干扰细胞的识别。我们提出了第一种适用于长骨生长板细胞图像的自动检测和形态测量方法。通过对Retinex方法、各向异性扩散和阈值化的特殊顺序应用，我们的新细胞检测算法（CDA）解决了小鼠生长板明场显微镜图像上的这些挑战。用户根据图像特征选择的五个参数调节我们的CDA。我们的结果证明了所提出的数值方法相对于手动方法的有效性。我们的CDA证实了先前关于正常生长板软骨细胞数量、面积、方向、高度和形状的既定结果。我们的CDA还证实了先前在荧光图像上发现的基因变异小鼠与其对照小鼠之间的差异。CDA旨在通过提高关于软骨细胞排列和特征的数据收集的效率和一致性来辅助生物医学研究。我们的结果表明，从生长板显微镜成像中自动提取数据有助于揭示正常和病理发育的信息，这是骨骼生长潜在生物学机制的关键。
