Abstract
A patient-specific anatomical structure model has been widely used in many medical applications. However, in practical applications, to effectively construct a patient-specific anatomical structure model is a challenge, the reasons are: 1) the manual marking process for landmark points is time-consuming and is prone to have false points; 2) the correspondence establishment is difficult; and 3) the performance of the model is limited. Therefore, the purpose of this study is to automatically construct a patient-specific anatomical structure model to solve these difficulties. Firstly, the input data are preprocessed to enhance the region of interest in CT scan images. Then, the region of interest is regarded as a training specimen, and the triangle is used to mesh the training specimen. Meanwhile, vertices contraction strategy is introduced to iteratively contract triangle meshes, and the correspondences are established through improved B-spline free-form deformation. Finally, principal component analysis is used to generate the final patient-specific anatomical structure model. Experimental results on 30 pelvic CT scan images verify that the proposed method outperforms the compared methods.
Highlights
INTRODUCTIONDavies [13] proposed a statistical shape model (SSM) for the patient anatomical structure, where landmark points were manually marked and used to establish the correspondence
We can clearly observe that the effect of vertices contraction is significantly better than that of Quadric Edge Collapse Decimation (QECD)
We propose a novel method to construct a patient-specific anatomical structure model based on statistical shape model
Summary
Davies [13] proposed a statistical shape model (SSM) for the patient anatomical structure, where landmark points were manually marked and used to establish the correspondence. A novel method was developed to construct a statistical shape model of the femur and pelvis through 3D ultrasound imaging They represented the template bone surface with triangular mesh and registered the template to the UltraSound (US) surface point cloud through the iterative nearest neighbor method. An innovation point is to propose a vertices contraction strategy to conduct mesh simplification This strategy can effectively reduce the number of mesh vertices and the complexity of computational in the phase of model construction. Another innovation point is to propose an improved B-spline free form deformation to successful establish the correspondence. Through the improved b-spline free form deformation, we get the mapping of each control point and concatenate all the control points to get the mapping between the template specimen and the target specimens
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