Reliable acetabular cup orientation with a new gravity-assisted guidance system.

Acetabular cup orientation is a key factor determining hip stability, and standard mechanical guides have shown little help in improving alignment. An in vitro study was carried out to compare the accuracy and precision of a new gravity-assisted guidance system with a standard mechanical guide. Three hundred ten cups were impacted by 5 surgeons, and the final cup orientation was measured. With the new guide, the average error in anteversion was 0.4 degrees , compared with 10.4 degrees with the standard guide and 0.3 degrees and -4.7 degrees , respectively, for abduction angles. The average time required for orienting the cups was similar for both guides. The accuracy and reproducibility obtained with the new guide were better (P < .0001). These good results would require a clinical validation.

A 2D/3D image analysis system to track fluorescently labeled structures in rod-shaped cells: application to measure spindle pole asymmetry during mitosis.

BACKGROUND: The yeast Schizosaccharomyces pombe is frequently used as a model for studying the cell cycle. The cells are rod-shaped and divide by medial fission. The process of cell division, or cytokinesis, is controlled by a network of signaling proteins called the Septation Initiation Network (SIN); SIN proteins associate with the SPBs during nuclear division (mitosis). Some SIN proteins associate with both SPBs early in mitosis, and then display strongly asymmetric signal intensity at the SPBs in late mitosis, just before cytokinesis. This asymmetry is thought to be important for correct regulation of SIN signaling, and coordination of cytokinesis and mitosis. In order to study the dynamics of organelles or large protein complexes such as the spindle pole body (SPB), which have been labeled with a fluorescent protein tag in living cells, a number of the image analysis problems must be solved; the cell outline must be detected automatically, and the position and signal intensity associated with the structures of interest within the cell must be determined. RESULTS: We present a new 2D and 3D image analysis system that permits versatile and robust analysis of motile, fluorescently labeled structures in rod-shaped cells. We have designed an image analysis system that we have implemented as a user-friendly software package allowing the fast and robust image-analysis of large numbers of rod-shaped cells. We have developed new robust algorithms, which we combined with existing methodologies to facilitate fast and accurate analysis. Our software permits the detection and segmentation of rod-shaped cells in either static or dynamic (i.e. time lapse) multi-channel images. It enables tracking of two structures (for example SPBs) in two different image channels. For 2D or 3D static images, the locations of the structures are identified, and then intensity values are extracted together with several quantitative parameters, such as length, width, cell orientation, background fluorescence and the distance between the structures of interest. Furthermore, two kinds of kymographs of the tracked structures can be established, one representing the migration with respect to their relative position, the other representing their individual trajectories inside the cell. This software package, called "RodCellJ", allowed us to analyze a large number of S. pombe cells to understand the rules that govern SIN protein asymmetry. CONCLUSIONS: "RodCell" is freely available to the community as a package of several ImageJ plugins to simultaneously analyze the behavior of a large number of rod-shaped cells in an extensive manner. The integration of different image-processing techniques in a single package, as well as the development of novel algorithms does not only allow to speed up the analysis with respect to the usage of existing tools, but also accounts for higher accuracy. Its utility was demonstrated on both 2D and 3D static and dynamic images to study the septation initiation network of the yeast Schizosaccharomyces pombe. More generally, it can be used in any kind of biological context where fluorescent-protein labeled structures need to be analyzed in rod-shaped cells. AVAILABILITY: RodCellJ is freely available under http://bigwww.epfl.ch/algorithms.html, (after acceptance of the publication).

Is spinal stenosis assessment dependent on slice orientation? A magnetic resonance imaging study.

INTRODUCTION: Lumbar spinal stenosis (LSS) treatment is based primarily on the clinical criteria providing that imaging confirms radiological stenosis. The radiological measurement more commonly used is the dural sac cross-sectional area (DSCA). It has been recently shown that grading stenosis based on the morphology of the dural sac as seen on axial T2 MRI images, better reflects severity of stenosis than DSCA and is of prognostic value. This radiological prospective study investigates the variability of surface measurements and morphological grading of stenosis for varying degrees of angulation of the T2 axial images relative to the disc space as observed in clinical practice. MATERIALS AND METHODS: Lumbar spine TSE T2 three-dimensional (3D) MRI sequences were obtained from 32 consecutive patients presenting with either suspected spinal stenosis or low back pain. Axial reconstructions using the OsiriX software at 0°, 10°, 20° and 30° relative to the disc space orientation were obtained for a total of 97 levels. For each level, DSCA was digitally measured and stenosis was graded according to the 4-point (A-D) morphological grading by two observers. RESULTS: A good interobserver agreement was found in grade evaluation of stenosis (k = 0.71). DSCA varied significantly as the slice orientation increased from 0° to +10°, +20° and +30° at each level examined (P < 0.0001) (-15 to +32% at 10°, -24 to +143% at 20° and -29 to +231% at 30° of slice orientation). Stenosis definition based on the surface measurements changed in 39 out of the 97 levels studied, whereas the morphology grade was modified only in two levels (P < 0.01). DISCUSSION: The need to obtain continuous slices using the classical 2D MRI acquisition technique entails often at least a 10° slice inclination relative to one of the studied discs. Even at this low angulation, we found a significantly statistical difference between surface changes and morphological grading change. In clinical practice, given the above findings, it might therefore not be necessary to align the axial cuts to each individual disc level which could be more time-consuming than obtaining a single series of axial cuts perpendicular to the middle of the lumbar spine or to the most stenotic level. In conclusion, morphological grading seems to offer an alternative means of assessing severity of spinal stenosis that is little affected by image acquisition technique.

On the origin of the MR image phase contrast: an in vivo MR microscopy study of the rat brain at 14.1 T.

Recent studies at high magnetic fields using the phase of gradient-echo MR images have shown the ability to unveil cortical substructure in the human brain. To investigate the contrast mechanisms in phase imaging, this study extends, for the first time, phase imaging to the rodent brain. Using a 14.1 T horizontal bore animal MRI scanner for in vivo micro-imaging, images with an in-plane resolution of 33 microm were acquired. Phase images revealed, often more clearly than the corresponding magnitude images, hippocampal fields, cortical layers (e.g. layer 4), cerebellar layers (molecular and granule cell layers) and small white matter structures present in the striatum and septal nucleus. The contrast of the phase images depended in part on the orientation of anatomical structures relative to the magnetic field, consistent with bulk susceptibility variations between tissues. This was found not only for vessels, but also for white matter structures, such as the anterior commissure, and cortical layers in the cerebellum. Such susceptibility changes could result from variable blood volume. However, when the deoxyhemoglobin content was reduced by increasing cerebral blood flow (CBF) with a carbogen breathing challenge, contrast between white and gray matter and cortical layers was not affected, suggesting that tissue cerebral blood volume (and therefore deoxyhemoglobin) is not a major source of the tissue phase contrast. We conclude that phase variations in gradient-echo images are likely due to susceptibility shifts of non-vascular origin.

Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.

We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each q-space point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

PHYTOCHROME KINASE SUBSTRATE4 modulates phytochrome-mediated control of hypocotyl growth orientation

Gravity and light are major factors shaping plant growth. Light perceived by phytochromes leads to seedling deetiolation, which includes the deviation from vertical hypocotyl growth and promotes hypocotyl phototropism. These light responses enhance survival of young seedlings during their emergence from the soil. The PHYTOCHROME KINASE SUBSTRATE (PKS) family is composed of four members in Arabidopsis (Arabidopsis thaliana): PKS1 to PKS4. Here we show that PKS4 is a negative regulator of both phytochrome A- and B-mediated inhibition of hypocotyl growth and promotion of cotyledon unfolding. Most prominently, pks4 mutants show abnormal phytochrome-modulated hypocotyl growth orientation. In dark-grown seedlings hypocotyls change from the original orientation defined by seed position to the upright orientation defined by gravity and light reduces the magnitude of this shift. In older seedlings with the hypocotyls already oriented by gravity, light promotes the deviation from vertical orientation. Based on the characterization of pks4 mutants we propose that PKS4 inhibits changes in growth orientation under red or far-red light. Our data suggest that in these light conditions PKS4 acts as an inhibitor of asymmetric growth. This hypothesis is supported by the phenotype of PKS4 overexpressers. Together with previous findings, these results indicate that the PKS family plays important functions during light-regulated tropic growth responses

Sparse regularization for fiber ODF reconstruction: from the suboptimality of ℓ2 and ℓ1 priors to ℓ0.

Diffusion MRI is a well established imaging modality providing a powerful way to probe the structure of the white matter non-invasively. Despite its potential, the intrinsic long scan times of these sequences have hampered their use in clinical practice. For this reason, a large variety of methods have been recently proposed to shorten the acquisition times. Among them, spherical deconvolution approaches have gained a lot of interest for their ability to reliably recover the intra-voxel fiber configuration with a relatively small number of data samples. To overcome the intrinsic instabilities of deconvolution, these methods use regularization schemes generally based on the assumption that the fiber orientation distribution (FOD) to be recovered in each voxel is sparse. The well known Constrained Spherical Deconvolution (CSD) approach resorts to Tikhonov regularization, based on an ℓ(2)-norm prior, which promotes a weak version of sparsity. Also, in the last few years compressed sensing has been advocated to further accelerate the acquisitions and ℓ(1)-norm minimization is generally employed as a means to promote sparsity in the recovered FODs. In this paper, we provide evidence that the use of an ℓ(1)-norm prior to regularize this class of problems is somewhat inconsistent with the fact that the fiber compartments all sum up to unity. To overcome this ℓ(1) inconsistency while simultaneously exploiting sparsity more optimally than through an ℓ(2) prior, we reformulate the reconstruction problem as a constrained formulation between a data term and a sparsity prior consisting in an explicit bound on the ℓ(0)norm of the FOD, i.e. on the number of fibers. The method has been tested both on synthetic and real data. Experimental results show that the proposed ℓ(0) formulation significantly reduces modeling errors compared to the state-of-the-art ℓ(2) and ℓ(1) regularization approaches.

Generating controlled image sets in cognitive neuroscience research.

The investigation of perceptual and cognitive functions with non-invasive brain imaging methods critically depends on the careful selection of stimuli for use in experiments. For example, it must be verified that any observed effects follow from the parameter of interest (e.g. semantic category) rather than other low-level physical features (e.g. luminance, or spectral properties). Otherwise, interpretation of results is confounded. Often, researchers circumvent this issue by including additional control conditions or tasks, both of which are flawed and also prolong experiments. Here, we present some new approaches for controlling classes of stimuli intended for use in cognitive neuroscience, however these methods can be readily extrapolated to other applications and stimulus modalities. Our approach is comprised of two levels. The first level aims at equalizing individual stimuli in terms of their mean luminance. Each data point in the stimulus is adjusted to a standardized value based on a standard value across the stimulus battery. The second level analyzes two populations of stimuli along their spectral properties (i.e. spatial frequency) using a dissimilarity metric that equals the root mean square of the distance between two populations of objects as a function of spatial frequency along x- and y-dimensions of the image. Randomized permutations are used to obtain a minimal value between the populations to minimize, in a completely data-driven manner, the spectral differences between image sets. While another paper in this issue applies these methods in the case of acoustic stimuli (Aeschlimann et al., Brain Topogr 2008), we illustrate this approach here in detail for complex visual stimuli.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.