Abstract
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 Smad1/5CKO 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.
Highlights
Microscopy imaging of mouse growth plates is extensively used to assess development and potential pathology
Application to microscopy images of growth plates of each of the classic methods of image segmentation and processing (e.g. Canny segmentation [1], cartoon-texture decomposition [2], k-means clustering [3]) does not take into account that: 1) the color intensity of each stain used to visualize a specific biological component can vary throughout the growth plate; 2) the characteristics of cells vary greatly within healthy, normal growth plate; and 3) the shades of colors within cells are present outside cells
Longitudinal growth of bones is the result of a process involving cell division, migration, and ossification that occurs in growth plates located at both the proximal and distal ends of the long bone
Summary
Microscopy imaging of mouse growth plates is extensively used to assess development and potential pathology Such imaging confounds current automated methods for cell detection. The terminal enlarged chondrocytes are larger than that in the rest of the growth plate, either round or elongated in the longitudinal direction, and packed closely to one another. The bottom of this region is marked by ossification [15], [16]. We propose a method of automated multi-step image processing These steps prepare an image for automated measurement of the characteristics of the chondrocytes located on the plane of focus of the original growth plate specimen. Rather than manually determining cell profiles, automated cell detection, and subsequent automated morphometry would aid biological research by increasing efficiency of measurements
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