3D identification of trabecular bone fracture zone using an automatic image registration scheme: A validation study.
Accurate identification of the local fracture zone is an important step towards the failure assessment of trabecular bone. In previous in-vitro studies, local fracture zones were visually identified in micro-CT images by experienced observers. This is a time-consuming and observer-dependent approach and it prevents any large-scale analysis of local trabecular fracture regions. The scope of this study is the application and validation of a new registration scheme for the automatic identification of trabecular bone fracture zones. Six human trabecular specimens were extracted from different anatomical sites. Five specimens were mechanically tested and scanned using micro-CT. For each specimen pre- and post-failure micro-CT datasets were obtained. The sixth specimen was scanned twice without any mechanical compression and was used to test the accuracy of the proposed scheme. The registration scheme was applied to the acquired datasets for the automatic identification of the fracture zone. The proposed scheme comprises of a three-dimensional (3D) automatic registration method to define the differences between the two datasets, and the application of a criterion for defining slices of the pre-failure dataset as "broken" or "unbroken". Identifications of the fracture zones were qualitatively validated against visual identification of observers. Furthermore, "full 3D" fracture zone identification, based on the presented scheme, was proposed. The proposed scheme proved to be more accurate and significantly faster than the currently used visual process.
Institute of Communication and Computer System, National Technical University of Athens, 9 Iroon Polytechniou Street, 157 80 Zografou, Athens, Greece. tassani.simone@gmail.com
Simone
S
Tassani
George K
GK
Matsopoulos
Fabio
F
Baruffaldi
D001185:Artificial Intelligence; D002102:Cadaver; D050723:Fractures, Bone / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*