Elastic image registration using hierarchical spatially based mean shift

An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical simulation of facial soft tissue change.

Lung motion estimation by robust point matching and spatiotemporal tracking for 4D CT

We propose a deformable registration approach to estimate patient-specific lung motion during free breathing for four-dimensional (4D) computed tomography (CT) based on point matching and tracking between images in different phases. First, a robust point matching (RPM) algorithm coarsely aligns the source phase image onto all other target phase images of 4D CT. Scale-invariant feature transform (SIFT) is introduced into the cost function in order to accelerate and stabilize the convergence of the point matching. Next, the temporal consistency of the estimated lung motion model is preserved by fitting the trajectories of the points in the respiratory phase using L₁ norm regularization. Then, the fitted positions of a point along the trajectory are used as the initial positions for the point tracking. Spatial mean-shift iteration is employed to track points in all phase images. The tracked positions in all phases are used to perform RPM again. These steps are repeated until the number of updated points is smaller than a given threshold σ. With this method, the correspondence between the source phase image and other target phase image is established more accurately. Trajectory fitting ensures the estimated trajectory does not fluctuate violently. We evaluated our method by using the public DIR-lab, POPI-model, CREATIS and COPDgene lung datasets. In the experimental results, the proposed method achieved satisfied accuracy for image registration. Our method also preserved the topology of the deformation fields well for image registration with large deformation.

A fast algorithm to estimate inverse consistent image transformation based on corresponding landmarks

Inverse consistency is an important feature for non-rigid image transformation in medical imaging analysis. In this paper, a simple and efficient inverse consistent image transformation estimation algorithm is proposed to preserve correspondence of landmarks and accelerate convergence. The proposed algorithm estimates both the forward and backward transformations simultaneously in the way that they are inverse to each other based on the correspondence of landmarks. Instead of computing the inverse functions and the inverse consistent transformations, respectively, we combine them together, which can improve computation efficiency significantly. Moreover, radial basis functions (RBFs) based transformation is adopted in our algorithm, which can handle deformation with local or global support. Our algorithm maps one landmark to its corresponding position exactly using the forward and backward transformations. Moreover, our algorithm is employed to estimate the forward and backward transformations in robust point matching, as well to demonstrate the application of our algorithm in image registration. The experiment results of uniform grids and test images indicate the improvement of the proposed algorithm in the aspect of inverse consistency of transformations and the reduction of the computation time of the forward and the backward transformations. The performance of our algorithm applying to robust point matching is evaluated using both brain slices and lung slices. Our experiments show that by combing robust point matching with our algorithm, the registration accuracy can be improved and the smoothness of transformations can be preserved.

A novel region-based level set method initialized with mean shift clustering for automated medical image segmentation

Appropriate initialization and stable evolution are desirable criteria to satisfy in level set methods. In this study, a novel region-based level set method utilizing both global and local image information complementarily is proposed. The global image information is extracted from mean shift clustering without any prior knowledge. Appropriate initial contours are obtained by regulating the clustering results. The local image information, as extracted by a data fitting energy, is employed to maintain a stable evolution of the zero level set curves. The advantages of the proposed method are as follows. First, the controlling parameters of the evolution can be easily estimated by the clustering results. Second, the automaticity of the model increases because of a reduction in computational cost and manual intervention. Experimental results confirm the efficiency and accuracy of the proposed method for medical image segmentation.