Global Localization and Orientation of the Cervical Spine in X-ray Imaging

Injuries in cervical spine X-ray images are often missed by emergency physicians. Many of these missing injuries cause further complications. Automated analysis of the images has the potential to reduce the chance of missing injuries. Towards this goal, this paper proposes an automatic localization of the spinal column in cervical spine X-ray images. The framework employs a random classification forest algorithm with a kernel density estimation-based voting accumulation method to localize the spinal column and to detect the orientation. The algorithm has been evaluated with 90 emergency room X-ray images and has achieved an average detection accuracy of 91% and an orientation error of 3.6◦. The framework can be used to narrow the search area for other advanced injury detection systems

Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae

X-ray is a common modality for diagnosing cervical vertebrae injuries. Many injuries are missed by emergency physicians which later causes life threatening complications. Computer aided analysis of X-ray images has the potential to detect missed injuries. Segmentation of the vertebrae is a crucial step towards automatic injury detection system. Active shape model (ASM) is one of the most successful and popular method for vertebrae segmentation. In this work, we propose a new ASM search method based on random classification forest and a kernel density estimation-based prediction technique. The proposed method have been tested on a dataset of 90 emergency room X-ray images containing 450 vertebrae and outperformed the classical Mahalanobis distancebased ASM search and also the regression forest-based method

Hough Forest-based Corner Detection for Cervical Spine Radiographs

The cervical spine (neck region) is highly sensitive to trauma related injuries, which must be analysed carefully by emergency physicians. In this work, we propose a Hough Forest-based corner detection method for cervical spine radiographs, as a first step towards a computer-aided diagnostic tool. We propose a novel patch-based model based on two-stage supervised learning (classification and regression) to estimate the corners of cervical vertebral bodies. Our method is evaluated using 106 cervical x-ray images consisting of 530 vertebrae and 2120 corners, which have been demarcated manually by an expert radiographer. The results show promising performance of the proposed algorithm, with a lowest median error of 1.98 m

A Review on Bone Mineral Density Loss in Total Knee Replacements Leading to Increased Fracture Risk

This is the author accepted manuscript. The final version is available from Humana Press via the DOI in this record.The link between low bone mineral density (BMD) scores leading to greater fracture risk is well established in the literature; what is not fully understood is the impact of total knee replacements/revisions or arthroplasties on BMD levels. This literature review attempts to answer this question. Several different databases using specific key terms were searched, with additional papers retrieved via bibliographic review. Based on the available evidence, total knee replacements/revisions and arthroplasties lower BMD and thus increase fracture risk. This review also addresses the possible implications of this research and possible options to reduce this risk.Author Michael Gundry’s current PhD is in part funded by the Stryker Institute with research investigating changes in BMD in bone surrounding cone implants on TKR revision patients. There is no grant number, but it is stated as an industry-funded, non-commercial study subject to a Masters Service Agreement between Stryker UK and the Royal Devon and Exeter Hospital. Additionally, authors Knapp and Hopkins have no conflict of interest to declare

Patch-based Corner Detection for Cervical Vertebrae in X-ray Images

Corners hold vital information about size, shape and morphology of a vertebra in an x-ray image, and recent literature [1, 2] has shown promising performance for detecting vertebral corners using a Hough forest-based architecture. To provide spatial context, this method generates a set of 12 patches around a vertebra and uses a machine learning approach to predict corners of a vertebral body through a voting process. In this paper, we extend this framework in terms of patch generation and prediction methods. During patch generation, the square region of interest has been replaced with data-driven rectangular and trapezoidal region of interest which better aligns the patches to the vertebral body geometry, resulting in more discriminative feature vectors. The corner estimation or the prediction stage has been improved by utilising more efficient voting process using a single kernel density estimation. In addition, advanced and more complex feature vectors are introduced. We also present a thorough evaluation of the framework with different patch generation methods, forest training mechanisms and prediction methods. In order to compare the performance of this framework with a more general method, a novel multi-scale Harris corner detector-based approach is introduced that incorporates a spatial prior through a naive Bayes method. All these methods have been tested on a dataset of 90 X-ray images and achieved an average corner localization error of 2.01 mm, representing a 33% improvement in localisation accuracy compared to the previous state-of-the-art method [2]

High Efficiency Differentiation of Human Pluripotent Stem Cells to Cardiomyocytes and Characterization by Flow Cytometry

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From parametricity to conservation laws, via Noether's Theorem

Invariance is of paramount importance in programming languages and in physics. In programming languages, John Reynolds' theory of relational parametricity demonstrates that parametric polymorphic programs are invariant under change of data representation, a property that yields "free" theorems about programs just from their types. In physics, Emmy Noether showed that if the action of a physical system is invariant under change of coordinates, then the physical system has a conserved quantity: a quantity that remains constant for all time. Knowledge of conserved quantities can reveal deep properties of physical systems. For example, the conservation of energy is by Noether's theorem a consequence of a system's invariance under time-shifting. In this paper, we link Reynolds' relational parametricity with Noether's theorem for deriving conserved quantities. We propose an extension of System Fω with new kinds, types and term constants for writing programs that describe classical mechanical systems in terms of their Lagrangians. We show, by constructing a relationally parametric model of our extension of Fω, that relational parametricity is enough to satisfy the hypotheses of Noether's theorem, and so to derive conserved quantities for free, directly from the polymorphic types of Lagrangians expressed in our system

Spectroscopy and carrier dynamics in CdSe self-assembled quantum dots embedded in ZnxCdyMg1−x−ySe

Time-resolved and steady-state photoluminescence,reflectivity, and absorption experiments were performed on CdSequantum dots in ZnxCdyMg1−x−ySe barriers. Studies of the capture times of the photoexcited carriers into the quantum dots and of electron-hole recombination times inside the dots were performed. Photoluminescence rise time yielded capture times from 20 ps to 30 ps. All samples exhibit fast and slow photoluminescence decays, consistent with observing two independent but energetically overlapping decays. The faster relaxation times for the sample emitting in the blue range is 90 ps, whereas for the two samples emitting in the green it is 345 ps and 480 ps. The slower relaxation times for the sample emitting in blue is 310 ps, whereas for the samples emitting in green is 7.5 ns. These results are explained on the basis of the structural differences among the quantum-dot samples