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.

Direct observation of molecular arrays in the organized smooth endoplasmic reticulum.

BACKGROUND Tubules and sheets of endoplasmic reticulum perform different functions and undergo inter-conversion during different stages of the cell cycle. Tubules are stabilized by curvature inducing resident proteins, but little is known about the mechanisms of endoplasmic reticulum sheet stabilization. Tethering of endoplasmic reticulum membranes to the cytoskeleton or to each other has been proposed as a plausible way of sheet stabilization. RESULTS Here, using fluorescence microscopy we show that the previously proposed mechanisms, such as membrane tethering via GFP-dimerization or coiled coil protein aggregation do not explain the formation of the calnexin-induced organized smooth endoplasmic reticulum membrane stacks. We also show that the LINC complex proteins known to serve a tethering function in the nuclear envelope are excluded from endoplasmic reticulum stacks. Finally, using cryo-electron microscopy of vitreous sections methodology that preserves cellular architecture in a hydrated, native-like state, we show that the sheet stacks are highly regular and may contain ordered arrays of macromolecular complexes. Some of these complexes decorate the cytosolic surface of the membranes, whereas others appear to span the width of the cytosolic or luminal space between the stacked sheets. CONCLUSION Our results provide evidence in favour of the hypothesis of endoplasmic reticulum sheet stabilization by intermembrane tethering.

Numerical study of the laser-tip coupling in surface plasmon assisted stacked-double-gate field emitter arrays

Recently, sub-wavelength-pitch stacked double-gate metal nanotip arrays have been proposed to realize high current, high brightness electron bunches for ultrabright cathodes for x-ray free-electron laser applications. With the proposed device structure, ultrafast field emission of photoexcited electrons is efficiently driven by vertical incident near infrared laser pulses, via near field coupling of the surface plasmon polariton resonance of the gate electrodes with the nanotip apex. In this work, in order to gain insight in the underlying physical processes, the authors report detailed numerical studies of the proposed device. The results indicate the importance of the interaction of the double-layer surface plasmon polariton, the position of the nanotip, as well as the incident angle of the near infrared laser pulses.

Response profiles of murine spiral ganglion neurons on multi-electrode arrays.

OBJECTIVE Cochlear implants (CIs) have become the gold standard treatment for deafness. These neuroprosthetic devices feature a linear electrode array, surgically inserted into the cochlea, and function by directly stimulating the auditory neurons located within the spiral ganglion, bypassing lost or not-functioning hair cells. Despite their success, some limitations still remain, including poor frequency resolution and high-energy consumption. In both cases, the anatomical gap between the electrode array and the spiral ganglion neurons (SGNs) is believed to be an important limiting factor. The final goal of the study is to characterize response profiles of SGNs growing in intimate contact with an electrode array, in view of designing novel CI devices and stimulation protocols, featuring a gapless interface with auditory neurons. APPROACH We have characterized SGN responses to extracellular stimulation using multi-electrode arrays (MEAs). This setup allows, in our view, to optimize in vitro many of the limiting interface aspects between CIs and SGNs. MAIN RESULTS Early postnatal mouse SGN explants were analyzed after 6-18 days in culture. Different stimulation protocols were compared with the aim to lower the stimulation threshold and the energy needed to elicit a response. In the best case, a four-fold reduction of the energy was obtained by lengthening the biphasic stimulus from 40 μs to 160 μs. Similarly, quasi monophasic pulses were more effective than biphasic pulses and the insertion of an interphase gap moderately improved efficiency. Finally, the stimulation with an external electrode mounted on a micromanipulator showed that the energy needed to elicit a response could be reduced by a factor of five with decreasing its distance from 40 μm to 0 μm from the auditory neurons. SIGNIFICANCE This study is the first to show electrical activity of SGNs on MEAs. Our findings may help to improve stimulation by and to reduce energy consumption of CIs and thereby contribute to the development of fully implantable devices with better auditory resolution in the future.

Global Optimization with Sparse and Local Gaussian Process Models

We present a novel surrogate model-based global optimization framework allowing a large number of function evaluations. The method, called SpLEGO, is based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected region. The method subsequently employs the standard expected improvement criterion to deal with the exploration-exploitation trade-off within selected local models, leading to a decision on where to perform the next function evaluation(s). The potential of our approach is demonstrated using the so-called Sparse Pseudo-input GP as a global model. The algorithm is tested on four benchmark problems, whose number of starting points ranges from 102 to 104. Our results show that SpLEGO is effective and capable of solving problems with large number of starting points, and it even provides significant advantages when compared with state-of-the-art EI algorithms.