Statistical shape analysis via principal factor analysis

Statistical shape analysis techniques commonly employed in the medical imaging community, such as active shape models or active appearance models, rely on principal component analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction (as opposed to independent component analysis, ICA). The key difference between PFA and PCA is that PFA models covariance between variables, rather than the total variance in the data. The added value of PFA is illustrated on 2D landmark data of corpora callosa outlines. Then, a study of the 3D shape variability of the human left femur is performed. Finally, we report results on vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI of the brain.

Age, microbiota, and T cells shape diverse individual IgA repertoires in the intestine

Intestinal immunoglobulin A (IgA) ensures host defense and symbiosis with our commensal microbiota. Yet previous studies hint at a surprisingly low diversity of intestinal IgA, and it is unknown to what extent the diverse Ig arsenal generated by somatic recombination and diversification is actually used. In this study, we analyze more than one million mouse IgA sequences to describe the shaping of the intestinal IgA repertoire, its determinants, and stability over time. We show that expanded and infrequent clones combine to form highly diverse polyclonal IgA repertoires with very little overlap between individual mice. Selective homing allows expanded clones to evenly seed the small but not large intestine. Repertoire diversity increases during aging in a dual process. On the one hand, microbiota-, T cell-, and transcription factor RORγt-dependent but Peyer's patch-independent somatic mutations drive the diversification of expanded clones, and on the other hand, new clones are introduced into the repertoire of aged mice. An individual's IgA repertoire is stable and recalled after plasma cell depletion, which is indicative of functional memory. These data provide a conceptual framework to understand the dynamic changes in the IgA repertoires to match environmental and intrinsic stimuli.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.