Analysis of new bone, cartilage, and fibrosis tissue in healing murine allografts using whole slide imaging and a new automated histomorphometric algorithm

Abstract

Histomorphometric analysis of histologic sections of normal and diseased bone samples, such as healing allografts and fractures, is widely used in bone research. However, the utility of traditional semi-automated methods is limited because they are labor-intensive and can have high interobserver variability depending upon the parameters being assessed, and primary data cannot be re-analyzed automatically. Automated histomorphometry has long been recognized as a solution for these issues, and recently has become more feasible with the development of digital whole slide imaging and computerized image analysis systems that can interact with digital slides. Here, we describe the development and validation of an automated application (algorithm) using Visiopharm’s image analysis system to quantify newly formed bone, cartilage, and fibrous tissue in healing murine femoral allografts in high-quality digital images of H&E/alcian blue-stained decalcified histologic sections. To validate this algorithm, we compared the results obtained independently using OsteoMeasure^TM and Visiopharm image analysis systems. The intraclass correlation coefficient between Visiopharm and OsteoMeasure was very close to one for all tissue elements tested, indicating nearly perfect reproducibility across methods. This new algorithm represents an accurate and labor-efficient method to quantify bone, cartilage, and fibrous tissue in healing mouse allografts.

Bone repair: tissue analysis automated

The analysis of bone architecture has been made faster and more reliable by the development of a fully automated system. The process has been semi-automated for over 30 years, but identifying and quantifying different tissue types has remained time-consuming and susceptible to errors. Brendan Boyce and colleagues at the University of Rochester Medical Center, USA, have developed a system that automates much more of the process. They combined the digitization of microscope images with image analysis software that could identify different tissue types according to their colours when stained. The system reduced the time required for analysis, and reliably distinguished between new bone, cartilage and fibrous tissue using two different image analysis systems. The automated system could help with monitoring bone healing after bone grafts and fractures.

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