Disparity of turbinal bones in placental mammals.

Turbinals are key bony elements of the mammalian nasal cavity, involved in heat and moisture conservation as well as olfaction. While turbinals are well known in some groups, their diversity is poorly understood at the scale of placental mammals, which span 21 orders. Here, we investigated the turbinal bones and associated lamellae for one representative of each extant order of placental mammals. We segmented and isolated each independent turbinal and lamella and found an important diversity of variation in the number of turbinals, as well as their size, and shape. We found that the turbinal count varies widely, from zero in the La Plata dolphin, (Pontoporia blainvillei) to about 110 in the African bush elephant (Loxodonta africana). Multiple turbinal losses and additional gains took place along the phylogeny of placental mammals. Some changes are clearly attributed to ecological adaptation, while others are probably related to phylogenetic inertia. In addition, this work highlights the problem of turbinal nomenclature in some placental orders with numerous and highly complex turbinals, for which homologies are extremely difficult to resolve. Therefore, this work underscores the importance of developmental studies to better clarify turbinal homology and nomenclature and provides a standardized comparative framework for further research.

Synergistic role of nucleotides and lipids for the self-assembly of Shs1 septin oligomers.

Budding yeast septins are essential for cell division and polarity. Septins assemble as palindromic linear octameric complexes. The function and ultra-structural organization of septins are finely governed by their molecular polymorphism. In particular, in budding yeast, the end subunit can stand either as Shs1 or Cdc11. We have dissected, here, for the first time, the behavior of the Shs1 protomer bound to membranes at nanometer resolution, in complex with the other septins. Using electron microscopy, we have shown that on membranes, Shs1 protomers self-assemble into rings, bundles, filaments or two-dimensional gauzes. Using a set of specific mutants we have demonstrated a synergistic role of both nucleotides and lipids for the organization and oligomerization of budding yeast septins. Besides, cryo-electron tomography assays show that vesicles are deformed by the interaction between Shs1 oligomers and lipids. The Shs1-Shs1 interface is stabilized by the presence of phosphoinositides, allowing the visualization of micrometric long filaments formed by Shs1 protomers. In addition, molecular modeling experiments have revealed a potential molecular mechanism regarding the selectivity of septin subunits for phosphoinositide lipids.

Statistical model based shape prediction from a combination of direct observations and various surrogates: application to orthopaedic research.

In computer-assisted orthopaedic surgery, recovering three-dimensional patient-specific anatomy from incomplete information has been focus of interest due to several factors such as less invasive surgical procedures, reduced radiation doses, and rapid intra-operative updates of the anatomy. The aim of this paper is to report results obtained combining statistical shape modeling and multivariate regression techniques for predicting bone shape from clinically and surgically relevant predictors, including sparse observations of the bone surface but also morphometric and anthropometric information. Different state of the art methods such as partial least square regression, principal component regression, canonical correlation analysis, and non-parametric kernel-based regression are compared. Clinically relevant surrogate variables and combinations are investigated on a database of 142 femur and 154 tibia shapes obtained from CT images. The results are evaluated using cross validation to quantify the prediction error. The proposed approach enables to characterize the added value of different predictors in a quantitative and localized fashion. Results indicate that complementary sources of information can be efficiently exploited to improve the accuracy of shape prediction.

Mapping changes of in vivo connectivity patterns in the human mediodorsal thalamus: correlations with higher cognitive and executive functions.

The mediodorsal thalamic nucleus is recognized as an association hub mediating interconnections with mainly the prefrontal cortex. Tracer studies in primates and in vivo diffusion tensor tractography findings in both humans and monkeys confirm its role in relaying networks that connect to the dorsolateral prefrontal, orbitofrontal, frontal medial and cingulate cortex. Our study was designed to use in vivo probabilistic tractography to describe the pathways emerging from or projecting to the mediodorsal nucleus; moreover, to use such information to automatically define subdivisions based on the divergence of remote structural connections. Diffusion tensor MR imaging data of 156 subjects were utilized to perform connectivity-based segmentation of the mediodorsal nucleus by employing a k-means clustering algorithm. Two domains were revealed (medial and lateral) that are separated from each other by a sagittally oriented plane. For each subject, general assessment of cognitive performance by means of the Wechsler Abbreviated Scale of Intelligence and measures of Delis-Kaplan Executive Function System (D-KEFS) test was utilized. Inter-subject variability in terms of connectivity-based cluster sizes was discovered and the relative sizes of the lateral mediodorsal domain correlated with the individuals' performance in the D-KEFS Sorting test (r = 0.232, p = 0.004). Our results show that the connectivity-based parcellation technique applied to the mediodorsal thalamic nucleus delivers a single subject level descriptor of connectional topography; furthermore, we revealed a possible weak interaction between executive performance and the size of the thalamic area from which pathways converge to the lateral prefrontal cortex.

Confidence regions for statistical model based shape prediction from sparse observations.

Shape prediction from sparse observation is of increasing interest in minimally invasive surgery, in particular when the target is not directly visible on images. This can be caused by a limited field-of-view of the imaging device, missing contrast or an insufficient signal-to-noise ratio. In such situations, a statistical shape model can be employed to estimate the location of unseen parts of the organ of interest from the observation and identification of the visible parts. However, the quantification of the reliability of such a prediction can be crucial for patient safety. We present here a framework for the estimation of complete shapes and of the associated uncertainties. This paper formalizes and extends previous work in the area by taking into account and incorporating the major sources of uncertainties, in particular the estimation of pose together with shape parameters, as well as the identification of correspondences between the sparse observation and the model. We evaluate our methodology on a large database of 171 human femurs and synthetic experiments based on a liver model. The experiments show that informative and reliable confidence regions can be estimated by the proposed approach.

Confidence bounds for the estimation of the volume phase fraction from a single image in a nickel base superalloy.

We propose an image-based framework to evaluate the uncertainty in the estimation of the volume fraction of specific microstructures based on the observation of a single section. These microstructures consist of cubes organized on a cubic mesh, such as monocrystalline nickel base superalloys. The framework is twofold: a model-based stereological analysis allows relating two-dimensional image observations to three-dimensional microstructure features, and a spatial statistical analysis allows computing approximate confidence bounds while assessing the representativeness of the image. The reliability of the method is assessed on synthetic models. Volume fraction estimation variances and approximate confidence intervals are computed on real superalloy images in the context of material characterization.

A mean three-dimensional atlas of the human thalamus: generation from multiple histological data.

Functional neurosurgery relies on robust localization of the subcortical target structures, which cannot be visualized directly with current clinically available in-vivo imaging techniques. Therefore, one has still to rely on an indirect approach, by transferring detailed histological maps onto the patient's individual brain images. In contrast to macroscopic MRI atlases, which often represent the average of a population, each stack of sections, which a stereotactic atlas provides, is based on a single specimen. In addition to this bias, the anatomy is displayed with a highly anisotropic resolution, leading to topological ambiguities and limiting the accuracy of geometric reconstruction. In this work we construct an unbiased, high-resolution three-dimensional atlas of the thalamic structures, representing the average of several stereotactically oriented histological maps. We resolve the topological ambiguity by combining the information provided by histological data from different stereotactic directions. Since the stacks differ not only in geometrical detail provided, but also due to inter-individual variability, we adopt an iterative approach for reconstructing the mean model. Starting with a reconstruction from a single stack of sections, we iteratively register the current reference model onto the available data and reconstruct a refined mean three-dimensional model. The results show that integration of multiple stereotactic anatomical data to produce an unbiased, mean model of the thalamic nuclei and their subdivisions is feasible and that the integration reduces problems of atlas reconstruction inherent to histological stacks to a large extent.

Estimating the confidence of statistical model based shape prediction.

We propose a method for estimating confidence regions around shapes predicted from partial observations, given a statistical shape model. Our method relies on the estimation of the distribution of the prediction error, obtained non-parametrically through a bootstrap resampling of a training set. It can thus be easily adapted to different shape prediction algorithms. Individual confidence regions for each landmark are then derived, assuming a Gaussian distribution. Merging those individual confidence regions, we establish th probability that, on average, a given proportion of the predicted landmarks actually lie in their estimated regions. We also propose a method for validating the accuracy of these regions using a test set.

Conditional variability of statistical shape models based on surrogate variables.

We propose to increment a statistical shape model with surrogate variables such as anatomical measurements and patient-related information, allowing conditioning the shape distribution to follow prescribed anatomical constraints. The method is applied to a shape model of the human femur, modeling the joint density of shape and anatomical parameters as a kernel density. Results show that it allows for a fast, intuitive and anatomically meaningful control on the shape deformations and an effective conditioning of the shape distribution, allowing the analysis of the remaining shape variability and relations between shape and anat omy. The approach can be further employed for initializing elastic registration methods such as Active Shape Models, improving their regularization term and reducing the search space for the optimization.