A New Automated Histomorphometric MATLAB Algorithm for Immunohistochemistry Analysis Using Whole Slide Imaging.

The use of animal models along with the employment of advanced and sophisticated stereological methods for assessing bone quality combined with the use of statistical methods to evaluate the effectiveness of bone therapies has made it possible to investigate the pathways that regulate bone responses to medical devices. Image analysis of histomorphometric measurements remains a time-consuming task, as the image analysis software currently available does not allow for automated image segmentation. Such a feature is usually obtained by machine learning and with software platforms that provide image-processing tools such as MATLAB. In this study, we introduce a new MATLAB algorithm to quantify immunohistochemically stained critical-sized bone defect samples and compare the results with the commonly available Aperio Image Scope Positive Pixel Count (PPC) algorithm. Bland and Altman analysis and Pearson correlation showed that the measurements acquired with the new MATLAB algorithm were in excellent agreement with the measurements obtained with the Aperio PPC algorithm, and no significant differences were found within the histomorphometric measurements. The ability to segment whole slide images, as well as defining the size and the number of regions of interest to be quantified, makes this MATLAB algorithm a potential histomorphometric tool for obtaining more objective, precise, and reproducible quantitative assessments of entire critical-sized bone defect image data sets in an efficient and manageable workflow.

Histomorphometric Evaluation of Critical-Sized Bone Defects Using Osteomeasure and Aperio Image Analysis Systems.

Most histological evaluations of critical-sized bone defects are limited to the analysis of a few regions of interest at a time. Manual and semiautomated histomorphometric approaches often have intra- and interobserver subjectivity, as well as variability in image analysis methods. Moreover, the production of large image data sets makes histological assessment and histomorphometric analysis labor intensive and time consuming. Herein, we tested and compared two image segmentation methods: thresholding (automated) and region-based (manual) modes, for quantifying complete image sets across entire critical-sized bone defects, using the widely used Osteomeasure system and the freely downloadable Aperio Image Scope software. A comparison of bone histomorphometric data showed strong agreement between the automated segmentation mode of the Osteomeasure software with the manual segmentation mode of Aperio Image Scope analysis (bone formation R2 = 0.9615 and fibrous tissue formation R2 = 0.8734). These results indicate that Aperio is capable of handling large histological images, with excellent speed performance in producing highly consistent histomorphometric evaluations compared with the Osteomeasure image analysis system. The statistical evaluation of these two major bone parameters demonstrated that Aperio Image Scope is as capable as Osteomeasure. This study developed a protocol to improve the quality of results and reduce analysis time, while also promoting the standardization of image analysis protocols for the histomorphometric analysis of critical-sized bone defect samples. Impact Statement Despite bone tissue engineering innovations increasing over the last decade, histomorphometric analysis of large bone defects used to study such approaches continues to pose a challenge for pathological assessment. This is due to the resulting large image data set, and the lack of a gold standard image analysis protocol to quantify histological outcomes. Herein, we present a standardized protocol for the image analysis of critical-sized bone defect samples stained with Goldner's Trichrome using the Osteomeasure and Aperio Image Scope image analysis systems. The results were critically examined to determine their reproducibility and accuracy for analyzing large bone defects.