Various papers described mesh morphing techniques for computational biomechanics, but none of them provided a quantitative assessment of generality, robustness, automation, and accuracy in predicting strains. This study aims to quantitatively evaluate the performance of a novel mesh-morphing algorithm. A mesh-morphing algorithm based on radial-basis functions and on manual selection of corresponding landmarks on template and target was developed. The periosteal geometries of 100 femurs were derived from a computed tomography scan database and used to test the algorithm generality in producing finite element (FE) morphed meshes. A published benchmark, consisting of eight femurs for which in vitro strain measurements and standard FE model strain prediction accuracy were available, was used to assess the accuracy of morphed FE models in predicting strains. Relevant parameters were identified to test the algorithm robustness to operative conditions. Time and effort needed were evaluated to define the algorithm degree of automation. Morphing was successful for 95% of the specimens, with mesh quality indicators comparable to those of standard FE meshes. Accuracy of the morphed meshes in predicting strains was good (R(2)>0.9, RMSE%<10%) and not statistically different from the standard meshes (p-value=0.1083). The algorithm was robust to inter- and intra-operator variability, target geometry refinement (p-value>0.05) and partially to the number of landmark used. Producing a morphed mesh starting from the triangularized geometry of the specimen requires on average 10 min. The proposed method is general, robust, automated, and accurate enough to be used in bone FE modelling from diagnostic data, and prospectively in applications such as statistical shape modelling.

In this paper we present a fast and efficient method for generating a patient-specific femur mesh by morphing a template femur mesh of ten-node tetrahedral elements on a geometry represented in STL format (STL: Surface Tessellation Language). The morphing is constrained through a set of user defined anatomical landmark points. Our method splits the input geometries into open surfaces, and maps each surface individually on a planar parameterization. We morph the parameterizations individually using a regression model based on Radial Basis Functions (RBFs). In experiments our method shows precise results in generating new patient-specific meshes from existing models and in re-meshing.

Within a specific medical application, the primary liver cancer curative treatment by a percutaneous high intensity ultrasound surgery, our study was designed to propose a fast 3D semi-automatic segmentation method of the liver, the tumor and the hepatic vascular networks. This method is characterized by a graph description of contrast medium injected CT volume where the links between the nodes describe either the degrees of similarity between voxels of the same class (region based approach) or the class changes between two neighboring voxels (boundary based approaches). The various weights describing these two properties are defined after a first interactive training phase. A Max-Flow/Min-Cut graph cut algorithm allowed partitioning the volume in two representative subsets of the segmented classes.

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