Abstract. The Active Shape Model (ASM) is a segmentation algorithm which uses a Statistical Shape Model (SSM) to constrain segmentations to 'plausible' shapes. This makes it possible to robustly segment organs with low contrast to adjacent structures. The standard SSM assumes that shapes are Gaussian distributed, which implies that unseen shapes can be expressed by linear combinations of the training shapes. Although this assumption does not always hold true, and several nonlinear SSMs have been proposed in the literature, virtually all applications in medical imaging use the linear SSM. In this work, we investigate 3D ASM segmentation with a nonlinear SSM based on Kernel PCA. We show that a recently published energy minimization approach for constraining shapes with a linear shape model extends to the nonlinear case, and overcomes shortcomings of previously published approaches. Our approach for nonlinear ASM segmentation is applied to vertebra segmentation and evaluated against the linear model.
DECT based BMD assessment is a method to extend the usage of increasingly acquired DECT image data. The developed DECT based analysis methods in conjunction with the visualization provide more detailed information for both, the radiologist and the orthopedist, compared to standard DXA based analysis.
Objective. Minimally invasive procedures minimize iatrogenic tissue damage and lead to a lower complication rate and high patient satisfaction. To date only experimental minimally invasive single-port approaches to the lateral skull base have been attempted. The aim of this study was to verify the feasibility of a minimally invasive multiport approach for advanced manipulation capability and visual control and develop a software tool for preoperative planning. Methods. Anatomical 3D models were extracted from twenty regular temporal bone CT scans. Collision-free trajectories, targeting the internal auditory canal, round window, and petrous apex, were simulated with a specially designed planning software tool. A set of three collision-free trajectories was selected by skull base surgeons concerning the maximization of the distance to critical structures and the angles between the trajectories. Results. A set of three collision-free trajectories could be successfully simulated to the three targets in each temporal bone model without violating critical anatomical structures. Conclusion. A minimally invasive multiport approach to the lateral skull base is feasible. The developed software is the first step for preoperative planning. Further studies will focus on cadaveric and clinical translation.
The quality of automatic 3D medical segmentation algorithms needs to be assessed on test datasets comprising several 3D images (i.e., instances of an organ). The experts need to compare the segmentation quality across the dataset in order to detect systematic segmentation problems. However, such comparative evaluation is not supported well by current methods. We present a novel system for assessing and comparing segmentation quality in a dataset with multiple 3D images. The data is analyzed and visualized in several views. We detect and show regions with systematic segmentation quality characteristics. For this purpose, we extended a hierarchical clustering algorithm with a connectivity criterion. We combine quality values across the dataset for determining regions with characteristic segmentation quality across instances. Using our system, the experts can also identify 3D segmentations with extraordinary quality characteristics. While we focus on algorithms based on statistical shape models, our approach can also be applied to cases, where landmark correspondences among instances can be established. We applied our approach to three real datasets: liver, cochlea and facial nerve. The segmentation experts were able to identify organ regions with systematic segmentation characteristics as well as to detect outlier instances.
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