Virtual mechanical testing with image-based digital twins enables subject-specific insights about the mechanical progression of bone fracture healing directly from imaging data. However, this technique is currently limited by the need for commercial software packages that require manual input to create finite element (FE) models from computed tomography (CT) scans. The purpose of this study was to develop automated image analysis algorithms that can create subject-specific models from CT scans without a human in the loop. Two competing techniques were developed and tested on an imaging dataset consisting of 26 intact and 44 osteotomized ovine tibiae. In both techniques, the raw image was cropped to an efficient bounding box, downsampled, segmented by an element-formation threshold, and cleaned up for efficient FE analysis using voxel-based meshes. The key difference between contour-free (CFT) and snake-reliant (SRT) techniques was threshold- and contour-based segmentation of images, respectively, before bounding box detection. The contours were detected using a snake that balanced desired aspects of the contours through energy minimization. Virtual torsion tests were performed and the results were validated by comparison to ground-truth experimental data. The CFT and SRT models produced nearly identical predictions of virtual torsional rigidity and both methods reliably replicated the physical tests. Models generated by SRT were faster to solve, but model preparation and solution combined was faster by CFT. Automatic digital twin creation by CFT is therefore recommended except where other downstream analyses require systematic spatial data sampling of the bone, which is only achieved by SRT.
Keywords: Active contour; Automatic meshing; Bone fracture healing; Finite element analysis; Image analysis; Snake.
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