Automated segmentation of micro-CT images of bone formation in calcium phosphate scaffolds.

In this work, we develop and validate an automated micro-computed tomography (micro-CT) image segmentation algorithm that accurately and efficiently segments bone, calcium phosphate (CaP)-based bone scaffold, and soft tissue. The algorithm enables quantitative evaluation of bone growth in CaP scaffolds in our study that includes many samples (100+) and large data sets (900 images per sample). The use of micro-CT for such applications is otherwise limited because the similarity in X-ray attenuation for the two materials makes them indistinguishable. Destructive characterization using histological techniques and scanning electron microscopy (SEM) has been the standard for CaP scaffolds, but these methods are cumbersome, inaccurate, and yield only 2D information. The proposed algorithm exploits scaffold periodicity and combines signal analysis, edge detection, and knowledge of three-dimensional spatial relationships between bone, CaP scaffold, and soft tissue to achieve fast and accurate segmentation. Application of this algorithm can lead to a new understanding of the role of CaP and scaffold internal structure on patterns and rates of bone growth.