Due to the small size of training sets, statistical shape models often over-constrain the deformation in medical image segmentation. Hence, artificial enlargement of the training set has been proposed as a solution for the problem to increase the flexibility of the models. In this paper, different methods were evaluated to artificially enlarge a training set. Furthermore, the objectives were to study the effects of the size of the training set, to estimate the optimal number of deformation modes, to study the effects of different error sources, and to compare different deformation methods. The study was performed for a cardiac shape model consisting of ventricles, atria, and epicardium, and built from magnetic resonance (MR) volume images of 25 subjects. Both shape modeling and image segmentation accuracies were studied. The objectives were reached by utilizing different training sets and datasets, and two deformation methods. The evaluation proved that artificial enlargement of the training set improves both the modeling and segmentation accuracy. All but one enlargement techniques gave statistically significantly (p < 0.05) better segmentation results than the standard method without enlargement. The two best enlargement techniques were the nonrigid movement technique and the technique that combines principal component analysis (PCA) and finite element model (FEM). The optimal number of deformation modes was found to be near 100 modes in our application. The active shape model segmentation gave better segmentation accuracy than the one based on the simulated annealing optimization of the model weights.

In this paper, a novel segmentation method for liver vasculature is presented, intended for numerical simulation of radio frequency ablation (RFA). The developed method is a semiautomatic hybrid based on multi-scale vessel enhancement combined with ridge-oriented region growing and skeleton-based postprocessing. In addition, an interactive tool for segmentation refinement was developed. Four instances of threephase contrast enhanced computed tomography (CT) images of porcine liver were used in the evaluation. The results showed improved accuracy over common approaches and illustrated the method's suitability for simulation purposes.

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Objectives: The motivation behind this work was to design an automatic algorithm capable of segmenting the exterior of the dental and facial bones including the mandible, teeth, maxilla and zygomatic bone with an open surface (a surface with a boundary) from CBCT images for the anatomy-based reconstruction of radiographs. Such an algorithm would provide speed, consistency and improved image quality for clinical workflows, for example, in planning of implants. Methods: We used CBCT images from two studies: first to develop (n 5 19) and then to test (n 5 30) a segmentation pipeline. The pipeline operates by parameterizing the topology and shape of the target, searching for potential points on the facial bone-soft tissue edge, reconstructing a triangular mesh by growing patches on from the edge points with good contrast and regularizing the result with a surface polynomial. This process is repeated for convergence. Results: The output of the algorithm was benchmarked against a hand-drawn reference and reached a 0.50 ± 1.0-mm average and 1.1-mm root mean squares error in Euclidean distance from the reference to our automatically segmented surface. These results were achieved with images affected by inhomogeneity, noise and metal artefacts that are typical for dental CBCT. Conclusions: Previously, this level of accuracy and precision in dental CBCT has been reported in segmenting only the mandible, a much easier target. The segmentation results were consistent throughout the data set and the pipeline was found fast enough (,1-min average computation time) to be considered for clinical use.

Deriving an accurate cost function for tensor valued data has been one of the main difficulties in diffusion tensor image (DTI) registration. In this work, we evaluate and compare five voxel similarity measures: Euclidean distance (ED), LogEuclidean distance (LOG), distance based on diffusion profiles (DP), diffusion mode based similarity (MBS), and multichannel version of sum of squared differences (SSD). In evaluation we used an optimization-independent evaluation protocol to assess the capture range, the number of local minima, and cyclic registrations to evaluate consistency. Statistically significant differences were observed: DP and MBS were found to be the most consistent similarity measures, ED had the least number of local minima, and SSD was inferior to other similarity measures in all evaluations.