Purpose Statistical Shape and Appearance Models play an important role in reducing the segmentation processing time of a vertebra and in improving results for 3D model development. Here we describe the different steps in generating a Statistical Shape Model of the second cervical vertebra (C2) and provide the shape model for general use by the scientific community. The main difficulties in its construction are the morphological complexity of the C2 and its variability in the population. Methods The input dataset is composed of manually segmented anonymized patient computerized tomography (CT) scans. The alignment of the different datasets is done with the Procrustes Alignment on surface models and then the registration is cast as a model-fitting problem using a Gaussian process. A Principal Component Analysis (PCA) based model is generated which includes the variability of the C2. Results The Statistical Shape Model (SSM) was generated using 92 CT scans. The resulting SSM was evaluated for specificity, compactness and generalization ability. The SSM of the C2 is freely available to the scientific community in Slicer (an open source software for image analysis and scientific visualization) with a module created to visualize the SSM using Statismo, a framework for statistical shape modeling. Conclusion The SSM of the vertebra allows the shape variability of the C2 to be represented. Moreover, the SSM will enable semi-automatic segmentation and 3D model generation of the vertebra, which would greatly benefit surgery planning.

Objectives: Estimating the sex of decomposed corpses and skeletal remains of unknown individuals is one of the first steps in the identification process in forensic contexts.Although various studies have considered the femur for sex estimation, the focus has primarily been on a specific single or a handful of measurements rather than the entire shape of the bone. In this article, we use statistical shape modeling (SSM) for sex estimation. We hypothesize that the accuracy of sex estimation will be improved by using the entire shape. Materials and Methods:For this study, we acquired a total of 61 femora from routine postmortem CT scans at the Institute for Forensic Medicine of the University of Zurich. The femora were extracted using segmentation technique. After building a SSM, we used the linear regression and nonlinear support vector machine technique for classification.Results: Using linear logistic regression and only the first principal component of the SSM, 76% of the femora were correctly classified by sex. Using the first five principal components, this value could be increased to 80%. Using nonlinear support vector machines and the first 20 principal components increased the rate of correctly classified femora to 87%.Discussion: Despite some limitations, the results obtained by using SSM for sex estimation in femur were promising and confirm the findings of other studies. Sex estimation accuracy, however, is not significantly improved over single or multiple linear measurements. Further research might improve the sex determination process in forensic anthropology by using SSM. K E Y W O R D SCT, forensic anthropology, forensic imaging, sex estimation, statistical shape modeling

Being able to quickly compute the inverse of a deformation field is often useful in the context of medical image analysis. While ITK supports this functionality, the current algorithms are slow and do not always yield accurate results. In this paper we describe an ITK implementation of a fixed point algorithm for the approximate inversion of deformation fields that was recently proposed by M. Chen and co-workers. The algorithm has been shown to be both faster and more accurate than those currently implemented in ITK.