The Psychoneuroimmunology associated with Tension Rules in Pediatric Cancers Individuals

Inspired by the aforementioned equivalence regime of rank minimization and GSC, WSNM can be translated into a non-convex weighted ℓp-norm minimization problem in GSC. By using the earned benchmark in sparse coding, the weighted ℓp-norm minimization is expected to obtain better performance than the three other norm minimization methods, i.e., ℓ1-norm, ℓp-norm and weighted ℓ1-norm. To verify the feasibility of the proposed benchmark, we compare the weighted ℓp-norm minimization against the three aforementioned norm minimization methods in sparse coding. Experimental results on image restoration applications, namely image inpainting and image compressive sensing recovery, demonstrate that the proposed scheme is feasible and outperforms many state-of-the-art methods.In clinical applications of super-resolution ultrasound imaging it is often not possible to achieve a full reconstruction of the microvasculature within a limited measurement time. This makes the comparison of studies and quantitative parameters of vascular morphology and perfusion difficult. Therefore, saturation models were proposed to predict adequate measurement times and estimate the degree of vessel reconstruction. Here we derive a statistical model for the microbubble counts in super-resolution voxels by a zero-inflated Poisson (ZIP) process. In this model, voxels either belong to vessels with probability Pv and count events with Poisson rate , or they are empty and remain zero. In this model,Pv represents the vessel voxel density in the super-resolution image after infinite measurement time. selleck chemicals For the parameters Pv and we give Cramir-Rao lower bounds (CRLB) for the estimation variance and derive maximum likelihood estimators (MLE) in a novel closed-form solution. These can be calculated with knowledge of only the counts at the end of the acquisition time. The estimators are applied to preclinical and clinical data and the MLE outperforms alternative estimators proposed before. The estimated degree of reconstruction lies between 38% and 74% after less than 90 s. Vessel probability Pv ranged from 4% - 20%. The rate parameter was estimated in the range of 0.5-1.3 microbubbles/voxel. For these parameter ranges, the CRLB gives standard deviations of less than 2%, which supports that the parameters can be estimated with good precision already for limited acquisition times.Tracking the myotendinous junction (MTJ) in consecutive ultrasound images is crucial for assessing the mechanics and pathological conditions of the muscle-tendon unit. However, poor image quality and boundary ambiguity conspire towards a lack of reliable and efficient identification of MTJ, restricting its application in motion analysis. In recent years, with the rapid development of deep learning, the region-based convolution neural network (RCNN) has shown great potential in the field of simultaneous objection detection and instance segmentation in medical images. This paper proposes a regionadaptive network, called RAN, to adaptively localize MTJ region and segment it in a single shot. Our model learns salient information of MTJ with a composite architecture, in which a region-based multi-task learning network explores the region containing MTJ, while a parallel end-to-end U-shape path extracts the MTJ structure from the adaptively selected region for combating data imbalance and boundary ambiguity. By demonstrating on ultrasound images of the gastrocnemius, we showed that the RAN achieves superior segmentation performance compared to the state-of-the-art Mask RCNN method with average Dice scores of 80.1%. Our method is promising in advancing muscle and tendon function examinations with ultrasound imaging.Vascular tree disentanglement and vessel type classification are two crucial steps of the graph-based method for retinal artery-vein (A/V) separation. Existing approaches treat them as two independent tasks and mostly rely on ad hoc rules (e.g. change of vessel directions) and hand-crafted features (e.g. color, thickness) to handle them respectively. However, we argue that the two tasks are highly correlated and should be handled jointly since knowing the A/V type can unravel those highly entangled vascular trees, which in turn helps to infer the types of connected vessels that are hard to classify based on only appearance. Therefore, designing features and models isolatedly for the two tasks often leads to a suboptimal solution of A/V separation. In view of this, this paper proposes a multi-task siamese network which aims to learn the two tasks jointly and thus yields more robust deep features for accurate A/V separation. Specifically, we first introduce Convolution Along Vessel (CAV) to extract the visual features by convolving a fundus image along vessel segments, and the geometric features by tracking the directions of blood flow in vessels. The siamese network is then trained to learn multiple tasks i) classifying A/V types of vessel segments using visual features only, and ii) estimating the similarity of every two connected segments by comparing their visual and geometric features in order to disentangle the vasculature into individual vessel trees. Finally, the results of two tasks mutually correct each other to accomplish final A/V separation. Experimental results demonstrate that our method can achieve accuracy values of 94.7%, 96.9%, and 94.5% on three major databases (DRIVE, INSPIRE, WIDE) respectively, which outperforms recent state-of-the-arts. Copyright (c) 2019 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to [email protected] the administered dose in SPECT myocardial perfusion imaging (MPI) has become an important clinical problem. In this study we investigate the potential benefit of applying a deep learning (DL) approach for suppressing the elevated imaging noise in low-dose SPECT-MPI studies. We adopt a supervised learning approach to train a neural network by using image pairs obtained from full-dose (target) and low-dose (input) acquisitions of the same patients. In the experiments, we made use of acquisitions from 1,052 subjects and demonstrated the approach for two commonly used reconstruction methods in clinical SPECT-MPI 1) filtered backprojection (FBP), and 2) ordered-subsets expectation-maximization (OSEM) with corrections for attenuation, scatter and resolution. We evaluated the DL output for the clinical task of perfusion-defect detection at a number of successively reduced dose levels (1/2, 1/4, 1/8, 1/16 of full dose). The results indicate that the proposed DL approach can achieve substantial noise reduction and lead to improvement in the diagnostic accuracy of low-dose data. In particular, at = dose, DL yielded an area-under-the-ROC-curve (AUC) of 0.799, which is nearly identical to the AUC=0.801 obtained by OSEM at full-dose (p-value=0.73); similar results were also obtained for FBP reconstruction. Moreover, even at 1/8 dose, DL achieved AUC=0.770 for OSEM, which is above the AUC=0.755 obtained at full-dose by FBP. These results indicate that, compared to conventional reconstruction filtering, DL denoising can allow for additional dose reduction without sacrificing the diagnostic accuracy in SPECT-MPI.Over-segmenting a video into supervoxels has strong potential to reduce the complexity of computer vision applications. Content-sensitive supervoxels (CSS) are typically smaller in content-dense regionsand larger in content-sparse regions. In this paper, we propose to compute feature-aware CSS (FCSS) that are regularly shaped 3D primitive volumes well aligned with local object/region/motion boundaries in video.To compute FCSS, we map a video to a 3-dimensional manifold, in which the volume elements of video manifold give a good measure of the video content density. Then any uniform tessellation on manifold can induce CSS. Our idea is that among all possible uniform tessellations, FCSS find one whose cell boundaries well align with local video boundaries. To achieve this goal, we propose a novel tessellation method that simultaneously minimizes the tessellation energy and maximizes the average boundary distance.Theoretically our method has an optimal competitive ratio O(1). We also present a simple extension of FCSS to streaming FCSS for processing long videos that cannot be loaded into main memory at once. We evaluate FCSS, streaming FCSS and ten representative supervoxel methods on four video datasets and two novel video applications. The results show that our method simultaneously achieves state-of-the-art performance with respect to various evaluation criteria.Semi-supervised clustering is one of important research topics in cluster analysis, which uses pre-given knowledge as constraints to improve the clustering performance. While clustering a data set, people often get prior constraints from different information sources, which may have different representations and contents, to guide clustering process. However, most of existing semi-supervised clustering algorithms are based on single-source constraints and rarely consider to integrate multi-source constraints to enhance the clustering quality. To solve the problem, we analyze the relations among different types of constraints and propose an uniform representation for them. Based it, we propose a new semi-supervised clustering algorithm to find out a clustering that has good cluster structure and high consensus of all the sources of constraints. In the algorithm, we construct an optimization objective model and its solution method to achieve the aim. This algorithm can integrate multi-source constraints well to reduce the effect of incorrect constraints from single sources and find out a high-quality clustering. By the experimental studies on several benchmark data sets, we illustrate the effectiveness of the proposed algorithm, compared to other semi-supervised clustering algorithms.OBJECTIVE Integrate tracked ultrasound and AI methods to provide a safer and more accessible alternative to X-ray for scoliosis measurement. We propose automatic ultrasound segmentation for 3-dimensional spine visualization and scoliosis measurement to address difficulties in using ultrasound for spine imaging. METHODS We trained a convolutional neural network for spine segmentation on ultrasound scans using data from eight healthy adult volunteers. We tested the trained network on eight pediatric patients. We evaluated image segmentation and 3-dimensional volume reconstruction for scoliosis measurement. RESULTS As expected, fuzzy segmentation metrics reduced when trained networks were translated from healthy volunteers to patients. Recall decreased from 0.72 to 0.64 (8.2% decrease), and precision from 0.31 to 0.27 (3.7% decrease). However, after finding optimal thresholds for prediction maps, binary segmentation metrics performed better on patient data. Recall decreased from 0.98 to 0.97 (1.6% decrease), and precision from 0.10 to 0.06 (4.5% decrease). Segmentation prediction maps were reconstructed to 3-dimensional volumes and scoliosis was measured in all patients. Measurement in these reconstructions took less than 1 minute and had a maximum error of 2.2° compared to X-ray. CONCLUSION automatic spine segmentation makes scoliosis measurement both efficient and accurate in tracked ultrasound scans. SIGNIFICANCE Automatic segmentation may overcome the limitations of tracked ultrasound that so far prevented its use as an alternative of X-ray in scoliosis measurement.