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Pain killers Use on Likelihood and Fatality rate associated with Intestinal Malignancies: Latest Condition of Epidemiological Facts.
Cervical spinal cord injury (cSCI) can cause paralysis and impair hand function. Existing assessments in clinical settings do not reflect an individual's performance in their daily environment. Videos from wearable cameras (egocentric video) provide a novel avenue to analyze hand function in non-clinical settings. Due to the large amounts of video data generated by this approach, automated analysis methods are necessary. We propose to employ an unsupervised learning process to produce a summary of the grasping strategies used in an egocentric video. To this end, an approach was developed consisting of hand detection, pose estimation, and clustering algorithms. The performance of the method was examined with external evaluation indicators and internal evaluation indicators for an uninjured and injured participant, respectively. The results demonstrated that a Gaussian mixture model obtained the highest accuracy in terms of the maximum match, 0.63, and the Rand index, 0.26, for the uninjured participant, and a silhouette score of 0.13 for the injured participant.Preterm newborns are prone to late-onset sepsis, leading to a high risk of mortality. Video-based analysis of motion is a promising non-invasive approach because the behavior of the newborn is related to his physiological state. But it is needed to analyze only images where the newborn is solely present in incubator. In this context, we propose a method for video-based detection of newborn presence. We use deep transfer learning bottleneck features are extracted from a pre-trained deep neural network and then a classifier is trained with these features on our database. Moreover, we propose a strategy that allows to take advantage of temporal consistency. On a database of 11 newborns with 56 days of video recordings, the results show a balanced accuracy of 80%.Block matching techniques have been studied exhaustively for motion estimation in Ultrasound (US) images. Exhaustive Search (ES) is the most commonly used search algorithm for block matching in US images. However, ES can be computationally expensive and slow. In this paper, a faster search algorithm called the Adaptive Rood Pattern Search (ARPS) is adopted to US images along with subpixel matching to reduce the computational cost and enhance block matching. Both ES and ARPS were applied in the context of block matching based 2D speckle tracking and were compared using Number of Computations per Frame (NCF), Computational Time per Frame (CTF) and Root Mean Squared Error (RMSE) as metrics. Our simulations and experimental results proved that ARPS outperformed ES by a substantial margin. Adaptation of this technique could help improve the performance of real-time motion estimation drastically.Ultrasound images have an inherently low lateral resolution due to the size of transducers that are used in standard clinical scanners. GSK3368715 This makes for low resolution images, as well as imprecise lateral displacement estimation. In speckle tracking, the well known discipline of estimating displacement by tracking pixel movement, lateral interpolation is often used to get subsample accurate displacement estimation. Standard methods for interpolation are known as inverse distance weighting methods, of which the well known cubic interpolation method is a part. Kriging interpolation, however, is a stochastic approach that uses statistical data to calculate interpolated data points as opposed to the purely mathematical methods of more traditional interpolators. This analysis tests the efficacy of one variety of Kriging interpolation, called Simple Kriging, on ultrasound data. Simple Kriging is tested on its accuracy to interpolate a sparse ultrasound image frame, as well as its usefulness in interpolating the correlation map to estimate subsample displacement. The applied bias of the estimation using Simple Kriging is also tested by interpolating the autocorrelation map where displacement is zero. Simple Kriging is an alternative interpolation scheme that could be used with image data and its accuracy is comparable to the accuracy of using the cubic interpolation.The Uterine Junctional Zone (JZ) is identified as an important anatomical region in the implantation process during assisted reproduction. The JZ changes throughout the hormone stimulation cycle and has predictive value for implantation success. Despite advances in imaging technique, the assessment of JZ remains an enigma. The state-of-the-art method to assess the JZ is largely manual, which is time consuming, depends on operator experience, and often introduces subjective bias in assessment. In this paper, we present methods for automated visualization and quantification of the JZ in three-dimensional transvaginal ultrasound imaging (3D-TVUS). JZ is best visualized in the midcoronal plane of the 3D-TVUS uterus acquisition. We propose an algorithm pipeline, which uses a deep learning model to generate a point cloud representing the surface of the endometrium. A regularized midcoronal surface passing through the point cloud is rendered to obtain the midcoronal plane. The automated solution is designed to accommodate multiple structural deformations and pathologies in the uterus. An expert assisted reproduction clinician on 136 3D-TVUS volumes evaluated the results, and reliable performance was observed in more than 89% cases where the automated solution is able to reproduce, and sometimes even outperform the manual workflow. Automation speeds up the clinical workflow approximately by a factor of ten and reduces operator bias.Cardiovascular diseases are the biggest threat to human being's health all over the world, and carotid atherosclerotic plaque is the leading cause of ischemic cardiovascular diseases. To determine the location and shape of the plaque, it is of great significance to detect the intima-media (IM). In this paper, a new IM detection method based on convolution neural network (IMD-CNN) is proposed for the detection of IM of blood vessels in longitudinal ultrasonic images. In IMD-CNN, firstly the region of interest (ROI) is automatically extracted by morphological processing, then the patch-wise training data are constructed, and finally a simple CNN is trained to detect the IM. The experimental results obtained on 23 images show that the test accuracy of IMD-CNN is over 86% and the performance of IMD-CNN is also visually proved to be effective.
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