Sex estimation from coxal bones using deep learning in a population balanced by sex and age.

In the field of forensic anthropology, researchers aim to identify anonymous human remains and determine the cause and circumstances of death from skeletonized human remains. Sex determination is a fundamental step of this procedure because it influences the estimation of other traits, such as age and stature. Pelvic bones are especially dimorphic, and are thus the most useful bones for sex identification.

Spatially Constrained Online Dictionary Learning for Source Separation.

Whether in medical imaging, astronomy or remote sensing, the data are increasingly complex. In addition to the spatial dimension, the data may contain temporal or spectral information that characterises the different sources present in the image. The compromise between spatial resolution and temporal/spectral resolution is often at the expense of spatial resolution, resulting in a potentially large mixing of sources in the same pixel/voxel.

Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.

Purpose: We address the automatic segmentation of healthy and cancerous liver tissues (parenchyma, active and necrotic parts of hepatocellular carcinoma (HCC) tumor) on multiphase CT images using a deep learning approach.

Methods: We devise a cascaded convolutional neural network based on the U-Net architecture. Two strategies for dealing with multiphase information are compared: Single-phase images are concatenated in a multi-dimensional features map on the input layer, or output maps are computed independently for each phase before being merged to produce the final segmentation.

Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans.

Purpose: Toward an efficient clinical management of hepatocellular carcinoma (HCC), we propose a classification framework dedicated to tumor necrosis rate estimation from dynamic contrast-enhanced CT scans. Based on machine learning, it requires weak interaction efforts to segment healthy, active and necrotic liver tissues.

Methods: Our contributions are two-fold.

The memory gene KIBRA is a bidirectional regulator of synaptic and structural plasticity in the adult brain.

Memory formation is associated with activity-dependent changes in synaptic plasticity. The mechanisms underlying these processes are complex and involve multiple components. Recent work has implicated the protein KIBRA in human memory, but its molecular functions in memory processes remain not fully understood.

Which is the most appropriate strategy for conducting multivariate voxel-based group studies on diffusion tensors?

There is a real need in the neuroscience community for efficient tools to compare Diffusion Tensor Magnetic Resonance Imaging across cohorts of subjects. Most studies focus on the comparison of scalar images such as fractional anisotropy or mean diffusivity. Although different statistical frameworks have been proposed to compare the whole diffusion tensor information, they are still seldom used in neuroimaging studies.

Heritable and inducible gene knockdown in astrocytes or neurons in vivo by a combined lentiviral and RNAi approach.

Gene knockout by homologous recombination is a popular method to study gene functions in the mouse in vivo. However, its lack of temporal control has limited the interpretation of knockout studies because the complete elimination of a gene product often alters developmental processes, and can induce severe malformations or lethality. Conditional gene knockdown has emerged as a compelling alternative to gene knockout, an approach well-established in vitro but that remains challenging in vivo, especially in the adult brain.

On the estimation and correction of bias in local atrophy estimations using example atrophy simulations.

Brain atrophy is considered an important marker of disease progression in many chronic neuro-degenerative diseases such as multiple sclerosis (MS). A great deal of attention is being paid toward developing tools that manipulate magnetic resonance (MR) images for obtaining an accurate estimate of atrophy. Nevertheless, artifacts in MR images, inaccuracies of intermediate steps and inadequacies of the mathematical model representing the physical brain volume change, make it rather difficult to obtain a precise and unbiased estimate.

Longitudinal change detection: inference on the diffusion tensor along white matter pathways.

Diffusion weighted magnetic resonance imaging (DW-MRI) makes it possible to probe brain connections in vivo. This paper presents a change detection framework that relies on white matter pathways with application to neuromyelitis optica (NMO). The objective is to detect local or global fiber diffusion property modifications between two longitudinal DW-MRI acquisitions of a patient.

Idebenone protects against retinal damage and loss of vision in a mouse model of Leber's hereditary optic neuropathy.

Leber's hereditary optic neuropathy (LHON) is an inherited disease caused by mutations in complex I of the mitochondrial respiratory chain. The disease is characterized by loss of central vision due to retinal ganglion cell (RGC) dysfunction and optic nerve atrophy. Despite progress towards a better understanding of the disease, no therapeutic treatment is currently approved for this devastating disease.

Selective regulation of NR2B by protein phosphatase-1 for the control of the NMDA receptor in neuroprotection.

An imbalance between pro-survival and pro-death pathways in brain cells can lead to neuronal cell death and neurodegeneration. While such imbalance is known to be associated with alterations in glutamatergic and Ca(2+) signaling, the underlying mechanisms remain undefined. We identified the protein Ser/Thr phosphatase protein phosphatase-1 (PP1), an enzyme associated with glutamate receptors, as a key trigger of survival pathways that can prevent neuronal death and neurodegeneration in the adult hippocampus.

Longitudinal change detection in diffusion MRI using multivariate statistical testing on tensors.

This paper presents a longitudinal change detection framework for detecting relevant modifications in diffusion MRI, with application to neuromyelitis optica (NMO) and multiple sclerosis (MS). The core problem is to identify image regions that are significantly different between two scans. The proposed method is based on multivariate statistical testing which was initially introduced for tensor population comparison.

Longitudinal change detection: inference on the diffusion tensor along white-matter pathways.

Diffusion tensor magnetic resonance imaging (DT-MRI) tractography allows to probe brain connections in vivo. This paper presents a change detection framework that relies on white-matter pathways with application to neuromyelitis optica (NMO). The objective is to detect global or local fiber diffusion property modifications between two longitudinal DT-MRI acquisitions of a patient.

Generalized likelihood ratio tests for change detection in diffusion tensor images: application to multiple sclerosis.

The automatic analysis of subtle changes between MRI scans is an important tool for monitoring disease evolution. Several methods have been proposed to detect changes in serial conventional MRI but few works have considered Diffusion Tensor Imaging (DTI), which is a promising modality for monitoring neurodegenerative disease and particularly Multiple Sclerosis (MS). In this paper, we introduce a comprehensive framework for detecting changes between two DTI acquisitions by considering different levels of representation of diffusion imaging, namely the Apparent Diffusion Coefficient (ADC) images, the diffusion tensor fields, and scalar images characterizing diffusion properties such as the fractional anisotropy and the mean diffusivity.

Change detection in diffusion MRI using multivariate statistical testing on tensors.

This paper presents a longitudinal change detection framework for detecting relevant modifications in diffusion MRI, with application to Multiple Sclerosis (MS). The proposed method is based on multivariate statistical testings which were initially introduced for tensor population comparison. We use these methods in the context of longitudinal change detection by considering several strategies to build sets of tensors characterizing the variability of each voxel.

Protein phosphatase 1 regulates the histone code for long-term memory.

Chromatin remodeling through histone posttranslational modifications (PTMs) and DNA methylation has recently been implicated in cognitive functions, but the mechanisms involved in such epigenetic regulation remain poorly understood. Here, we show that protein phosphatase 1 (PP1) is a critical regulator of chromatin remodeling in the mammalian brain that controls histone PTMs and gene transcription associated with long-term memory. Our data show that PP1 is present at the chromatin in brain cells and interacts with enzymes of the epigenetic machinery including HDAC1 (histone deacetylase 1) and histone demethylase JMJD2A (jumonji domain-containing protein 2A).

Use of simulated atrophy for performance analysis of brain atrophy estimation approaches.

In this paper, we study the performance of popular brain atrophy estimation algorithms using a simulated gold standard. The availability of a gold standard facilitates a sound evaluation of the measures of atrophy estimation, which is otherwise complicated. Firstly, we propose an approach for the construction of a gold standard.

Statistical detection of longitudinal changes between apparent diffusion coefficient images: application to multiple sclerosis.

The automatic analysis of longitudinal changes between Diffusion Tensor Imaging (DTI) acquisitions is a promising tool for monitoring disease evolution. However, few works address this issue and existing methods are generally limited to the detection of changes between scalar images characterizing diffusion properties, such as Fractional Anisotropy or Mean Diffusivity, while richer information can be exploited from the whole set of Apparent Diffusion Coefficient (ADC) images that can be derived from a DTI acquisition. In this paper, we present a general framework for detecting changes between two sets of ADC images and we investigate the performance of four statistical tests.

Symmetric nonrigid image registration: application to average brain templates construction.

Image registration aims at estimating a consistent mapping between two images. Common techniques consist in choosing arbitrarily one image as a reference image and the other one as a floating image, thus leading to the estimation of inconsistent mappings. We present a symmetric formulation of the registration problem that maps the two images in a common coordinate system halfway between them.

Accurate inversion of 3-D transformation fields.

This correspondence addresses the inversion of 3-D transformation fields, which is a problem that typically arises in image warping problems. A topology preserving parametric B-spline-based representation of the deformation field is considered. Topology preservation ensures that the transformation is a one-to-one mapping and consequently that it is invertible.

Robust pose estimation and recognition using non-gaussian modeling of appearance subspaces.

We present an original appearance model that generalizes the usual Gaussian visual subspace model to non-Gaussian and nonparametric distributions. It can be useful for the modeling and recognition of images under difficult conditions such as large occlusions and cluttered backgrounds. Inference under the model is efficiently solved using the mean shift algorithm.

Affine-invariant geometric shape priors for region-based active contours.

We present a new way of constraining the evolution of a region-based active contour with respect to a reference shape. Minimizing a shape prior, defined as a distance between shape descriptors based on the Legendre moments of the characteristic function, leads to a geometric flow that can be used with benefits in a two-class segmentation application. The shape model includes intrinsic invariance with regard to pose and affine deformations.

3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization.

This paper deals with topology preservation in three-dimensional (3-D) deformable image registration. This work is a nontrivial extension of, which addresses the case of two-dimensional (2-D) topology preserving mappings. In both cases, the deformation map is modeled as a hierarchical displacement field, decomposed on a multiresolution B-spline basis.

Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models.

In this paper, a novel functional magnetic resonance imaging (fMRI) brain mapping method is presented within the statistical modeling framework of hidden semi-Markov event sequence models (HSMESMs). Neural activation detection is formulated at the voxel level in terms of time coupling between the sequence of hemodynamic response onsets (HROs) observed in the fMRI signal, and an HSMESM of the hidden sequence of task-induced neural activations. The sequence of HRO events is derived from a continuous wavelet transform (CWT) of the fMRI signal.