Cochlear shape reveals that the human organ of hearing is sex-typed from birth.

Sex differences in behavioral and neural characteristics can be caused by cultural influences but also by sex-based differences in neurophysiological and sensorimotor features. Since signal-response systems influence decision-making, cooperative and collaborative behaviors, the anatomical or physiological bases for any sex-based difference in sensory mechanisms are important to explore. Here, we use uniform scaling and nonparametric representations of the human cochlea, the main organ of hearing that imprints its adult-like morphology within the petrosal bone from birth. We observe a sex-differentiated torsion along the 3D cochlear curve in samples of 94 adults and 22 juvenile skeletons from cross-cultural contexts. The cochlear sexual dimorphism measured in our study allows sex assessment from the human skeleton with a mean accuracy ranging from 0.91 to 0.93 throughout life. We conclude that the human cochlea is sex-typed from an early post-natal age. This, for the first time, allows nondestructive sex determination of juveniles' skeletal remains in which the biomolecules are too degraded for study but in which the petrosal is preserved, one of the most common bone within archaeological assemblages. Our observed sex-typed cochlear shape from birth is likely associated with complex evolutionary processes in modern humans for reasons not yet fully understood.

Automatic segmentation of breast MR images through a Markov random field statistical model.

An algorithm dedicated to automatic segmentation of breast magnetic resonance images is presented in this paper. Our approach is based on a pipeline that includes a denoising step and statistical segmentation. The noise removal preprocessing relies on an anisotropic diffusion scheme, whereas the statistical segmentation is conducted through a Markov random field model. The continuous updating of all parameters governing the diffusion process enables automatic denoising, and the partial volume effect is also addressed during the labeling step. To assess the relevance, the Jaccard similarity coefficient was computed. Experiments were conducted on synthetic data and breast magnetic resonance images extracted from a high-risk population. The relevance of the approach for the dataset is highlighted, and we demonstrate accuracy superior to that of traditional clustering algorithms. The results emphasize the benefits of both denoising guided by input data and the inclusion of spatial dependency through a Markov random field. For example, the Jaccard coefficient for the clinical data was increased by 114%, 109%, and 140% with respect to a K-means algorithm and, respectively, for the adipose, glandular and muscle and skin components. Moreover, the agreement between the manual segmentations provided by an experienced radiologist and the automatic segmentations performed with this algorithm was good, with Jaccard coefficients equal to 0.769, 0.756, and 0.694 for the above-mentioned classes.

Gap filling of 3-D microvascular networks by tensor voting.

We present a new algorithm which merges discontinuities in 3-D images of tubular structures presenting undesirable gaps. The application of the proposed method is mainly associated to large 3-D images of microvascular networks. In order to recover the real network topology, we need to fill the gaps between the closest discontinuous vessels. The algorithm presented in this paper aims at achieving this goal. This algorithm is based on the skeletonization of the segmented network followed by a tensor voting method. It permits to merge the most common kinds of discontinuities found in microvascular networks. It is robust, easy to use, and relatively fast. The microvascular network images were obtained using synchrotron tomography imaging at the European Synchrotron Radiation Facility. These images exhibit samples of intracortical networks. Representative results are illustrated.