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Vessel segmentation is a fundamental, yet not well-solved problem in medical image analysis, due to the complicated geometrical and topological structures of human vessels. Unlike existing rule- and conventional learning-based techniques, which hardly capture the location of tiny vessel structures and perceive their global spatial structures, we propose Simultaneous Self- and Channel-attention Neural Network (termed SSCA-Net) to solve the multiscale structure-preserving vessel segmentation (MSVS) problem. SSCA-Net differs from the conventional neural networks in modeling image global contexts, showing more power to understand the global semantic information by both self- and channel-attention (SCA) mechanism and offering high performance on segmenting vessels with multiscale structures (e.g., DSC: 96.21% and MIoU: 92.70% on the intracranial vessel dataset). Specifically, the SCA module is designed and embedded in the feature decoding stage to learn SCA features at different layers, in which the self-attention is used to obtain the position information of the feature itself, and the channel attention is designed to guide the shallow features to obtain global feature information. To evaluate the effectiveness of our SSCA-Net, we compare it with several state-of-the-art methods on three well-known vessel segmentation benchmark datasets. Qualitative and quantitative results demonstrate clear improvements of our method over the state-of-the-art in terms of preserving vessel details and global spatial structures. [ABSTRACT FROM AUTHOR]

Coronary arteries have high curvatures, and hence, flow through them causes disturbed flow patterns, resulting in stenosis and atherosclerosis. This in turn decreases the myocardial flow perfusion, causing myocardial ischemia and infarction. Therefore, in order to understand the mechanisms of these phenomena caused by high curvatures and branching of coronary arteries, we have conducted elaborate hemodynamic analysis for both (i) idealized coronary arteries with geometrical parameters representing realistic curvatures and stenosis and (ii) patient-specific coronary arteries with stenoses. Firstly, in idealized coronary arteries with approximated realistic arterial geometry representative of their curvedness and stenosis, we have computed the hemodynamic parameters of pressure drop, wall shear stress (WSS) and wall pressure gradient (WPG), and their association with the geometrical parameters of curvedness and stenosis. Secondly, we have similarly determined the wall shear stress and wall pressure gradient distributions in four patient-specific curved stenotic right coronary arteries (RCAs), which were reconstructed from medical images of patients diagnosed with atherosclerosis and stenosis; our results show high WSS and WPG regions at the stenoses and inner wall of the arterial curves. This paper provides useful insights into the causative mechanisms of the high incidence of atherosclerosis in coronary arteries. It also provides guidelines for how simulation of blood flow in patient's coronary arteries and determination of the hemodynamic parameters of WSS and WPG can provide a medical assessment of the risk of development of atherosclerosis and plaque formation, leading to myocardial ischemia and infarction. The novelty of our paper is in our showing how in actual coronary arteries (based on their CT imaging) curvilinearity and narrowing complications affect the computed WSS and WPG, associated with risk of atherosclerosis. This is very important for cardiologists to be able to properly take care of their patients and provide remedial measures before coronary complications lead to myocardial infarctions and necessitate stenting or coronary bypass surgery. We want to go one step further and provide clinical application of our research work. For that, we are offering to cardiologists worldwide to carry out hemodynamic analysis of the medically imaged coronary arteries of their patients and compute the values of the hemodynamic parameters of WSS and WPG, so as to provide them an assessment of the risk of atherosclerosis for their patients. Graphical abstract Theme and aims: Coronary arteries have high curvatures, and hence flow through them causes disturbed flow patterns, resulting in stenosis and atherosclerosis. This in turn decreases the myocardial flow perfusion, causing myocardial ischemia and infarction. Therefore, in order to understand the mechanisms of these phenomena caused by high curvatures and branching of coronary arteries, we have conducted elaborate hemodynamic analysis for both (i) idealized coronary arteries with geometrical parameters representing curvatures and stenosis, and (ii) patient-specific coronary arteries with stenoses. Methods and results: Firstly, in idealized coronary arteries with approximated realistic arterial geometry representative of their curvedness and stenosis, we have computed the hemodynamic parameters of pressure drop, wall shear stress (WSS) and wall pressure gradient (WPG), and their association with the geometrical parameters of curvedness and stenosis. Then, we have determined the wall shear stress and wall pressure gradient distributions in four patient-specific curved stenotic right coronary arteries (RCAs), that were reconstructed from medical images of patients diagnosed with atherosclerosis and stenosis, as illustrated in Figure 1, in which the locations of the stenoses are highlighted by arrows. Figure 1: Three-dimensional CT visualization of arteries in patients with suspected coronary disease. The arteries can be seen as a combination of various curved segments with stenoses at unspecific locations highlighted by arrows. Our results show high WSS and WPG regions at the stenoses and inner wall of the arterial curves, as depicted in Figure 2. Therein, the encapsulations show (i) high WSS, and (ii) high WPG regions at the stenosis and inner wall of the arterial curves. Figure 2: WSS and WPG surface plot of realistic arteries (a), (b), (c) and (d), wherein the small squared parts are enlarged to show the detailed localized contour plots at the stenotic regions. Therein, the circular encapsulations show (i) high WSS and (ii) high WPG regions at the stenosis and inner wall of the arterial curves. Conclusion and novelty: This paper provides useful insights into the causative mechanisms of the high incidence of atherosclerosis in coronary arteries. It also provides guidelines for how simulation of blood flow in patient coronary arteries and determination of the hemodynamic parameters of WSS and WPG can provide a medical assessment of the risk of development of atherosclerosis and plaque formation, leading to myocardial ischemia and infarction. The novelty of our paper is our showing how in actual coronary arteries (based on their CT imaging), curvilinearity and narrowing complications affect the computed WSS and WPG associated with risk of atherosclerosis. This is very important for cardiologists to be able to properly take care of their patients and provide remedial measures before coronary complications lead to myocardial infarctions and necessitate stenting or coronary bypass surgery. [ABSTRACT FROM AUTHOR]

In this paper, we have analysed pulsatile flow through partially occluded elastic arteries, to determine the haemodynamic parameters of wall shear stress (WSS), wall pressure gradient and pressure drops (ΔP), contributing to enhanced flow resistance and myocardial ischaemic regions which impair cardiac contractility and cause increased work load on the heart. In summary, it can be observed that stenoses in an artery significantly influence the haemodynamic parameters of wall shear stress and pressure drop in contrast to dilatations case. This deduces that stenosis plays a more critical role in plaque growth and vulnerability in contrast to dilatation, and should be the key element in cardiovascular pathology and diagnosis. Through quantitative analysis of WSS and ΔP, we have provided a clearer insight into the haemodynamics of atherosclerotic arteries. Determination of these parameters can be helpful to cardiologists, because it is directly implicated in the genesis and development of atherosclerosis. [ABSTRACT FROM AUTHOR]

Percutaneous coronary intervention (PCI) is a minimally invasive surgery procedure that is widely used in the treatment of coronary artery disease. This procedure requires interventional cardiologists to have high proficiency and therefore demands an extensive training period in order to ensure successful surgical outcome. In this paper, a realistic and real-time interactive simulator for training PCI procedure is presented. A set of new approaches for core simulation components is devised and integrated into the simulator. Trainees can interact with the virtual simulation environment with real instruments and essential maneuvers encountered in real PCI procedure. Although presently targeted at PCI, our simulator could be easily extended to mimic the necessities of any vascular interventional radiology procedures by updating vascular anatomy. Preliminary validation of the proposed physical model of instruments is conducted on vascular phantom to demonstrate its performance and effectiveness. [ABSTRACT FROM AUTHOR]

Interventional radiology (IR) is widely used in the treatment of cardiovascular disease. The manipulation of the guidewire and catheter is an essential skill in IR procedure. Computer-based training simulators can provide solutions to overcome many drawbacks of the traditional apprenticeship training during the procedure. In this paper, a physically-based approach to simulating the behavior of the guidewire is presented. Our approach models the guidewire as thin flexible elastic rods with different resolutions which are dynamically adaptive to the curvature of the vessel. More material characteristics of this deformable material are integrated into our discrete model to realistically simulate the behavior of the wire. A force correction strategy is proposed to adjust the elastic force to avoid endless collision detections. Several experimental tests on our simulator are given to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Abstract: Surface model of vascular structure plays a crucial role in many medical applications such as diagnosis of vascular diseases, surgery planning and virtual interventional vascular surgery. During the last two decades, many surface modeling methods for vascular structures are presented, but the performance and applicability of these methods have not been studied extensively. In this paper, a comparative study of some of the latest methods is carried out, to evaluate the strengths and weaknesses of these methods with regard to several evaluating criteria. Based on the comparative results, the applicability of each method for several specific applications is suggested. [Copyright &y& Elsevier]

The article presents a study which proposed a technique for the automatic detection of subarachnoid hemorrhage (SAH) on clinical non-contrast head computed tomography (CT) scans. Also cited are the proposed two steps in detecting SAH, including the identification of abnormality in the approximated subarachnoid space (SAS) through support vector machine (SVM) based pattern classification, and the components of the method including image preprocessing, brain segmentation, and feature extraction.

Abstract: Surface mesh denoising is an important preprocess for many digital geometrical applications. Unlike most previous smoothing algorithms, which are vertex- or normal-based, we present a smoothing approach based on gradient field. The proposed algorithm first applies an extended non-local means image filter to diffuse mesh gradient field and then reconstructs a smoothed surface from the smoothed field by solving the Poisson equations in the least-squares sense. Experimental results on various data demonstrate that the proposed approach can successfully remove noise from meshes while preserving geometrical detail features of the original object and avoiding significant volume shrinkage. [ABSTRACT FROM AUTHOR]

Abstract: Efficient visualization of vascular structures is essential for therapy planning and medical education. Existing techniques achieve high-quality visualization of vascular surfaces at the cost of low rendering speed and large size of resulting surface. In this paper, we present an approach for visualizing vascular structures by exploiting the local curvature information of a given surface. To handle complex topology of loop and multiple parents and/or multiple children, bidirectional adaptive sampling and modified normal calculations at joints are proposed. The proposed method has been applied to cerebral vascular trees, liver vessel trees, and aortic vessel trees. The experimental results show that it can obtain a high-quality surface visualization with fewer polygons in the approximation. [ABSTRACT FROM AUTHOR]

Background: While numerous allergy-related biomarkers and targeted treatment strategies have been developed and employed, there are still signifcant limitations and challenges in the early diagnosis and targeted treatment for allegic diseases. Our study aims to identify circulating proteins causally associated with allergic disease-related traits through Mendelian randomization (MR)-based analytical framework. Methods: Large-scale cis-MR was employed to estimate the effects of thousands of plasma proteins on five main allergic diseases. Additional analyses including MR Steiger analyzing and Bayesian colocalisation, were performed to test the robustness of the associations; These findings were further validated utilizing meta-analytical methods in the replication analysis. Both proteome- and transcriptome-wide association studies approach was applied, and then, a protein-protein interaction was conducted to examine the interplay between the identified proteins and the targets of existing medications. Results: Eleven plasma proteins were identified with links to atopic asthma (AA), atopic dermatitis (AD), and allergic rhinitis (AR). Subsequently, these proteins were classified into four distinct target groups, with a focus on tier 1 and 2 targets due to their higher potential to become drug targets. MR analysis and extra validation revealed STAT6 and TNFRSF6B to be Tier 1 and IL1RL2 and IL6R to be Tier 2 proteins with the potential for AA treatment. Two Tier 1 proteins, CRAT and TNFRSF6B, and five Tier 2 proteins, ERBB3, IL6R, MMP12, ICAM1, and IL1RL2, were linked to AD, and three Tier 2 proteins, MANF, STAT6, and TNFSF8, to AR. Conclusion: Eleven Tier 1 and 2 protein targets that are promising drug target candidates were identified for AA, AD, and AR, which influence the development of allergic diseases and expose new diagnostic and therapeutic targets. [ABSTRACT FROM AUTHOR]

This paper studies an integrated network planning and operation problem of sustainable closed-loop supply chain (CLSC) under demand uncertainty with particularly interest in the configuration of retail network. The problem is modeled as a two-stage stochastic program which is further extended to evaluate the impact of various carbon polices on planning and operation decisions of the CLSC. A branch and bound based integer L-shaped algorithm has been proposed to solve these models. Extensive numerical experiments are conducted and impacts of carbon policies are analyzed. Sensitivity analysis is carried out and several managerial insights are drawn. • Consider an integrated network planning and operation problem of sustainable closed-loop supply chain with retail network configuration. • Model the problem and its extension under various carbon policies into a two-stage stochastic program. • Propose a branch and bound based integer L-shaped algorithm to solve these models. [ABSTRACT FROM AUTHOR]

• A global context attention (GCA) block is proposed to combine high-level features and shallow features to produce more expressive global feature information. • A squeeze and excitation pyramid pooling (SEPP) block is proposed to preserve more spatial information. • We integrate the proposed GCA block and SEPP block with encoder-decoder structure for medical image segmentation of different tasks. Medical image segmentation plays an important role in many clinical applications such as disease diagnosis, surgery planning, and computer-assisted therapy. However, it is a very challenging task due to variant images qualities, complex shapes of objects, and the existence of outliers. Recently, researchers have presented deep learning methods to segment medical images. However, these methods often use the high-level features of the convolutional neural network directly or the high-level features combined with the shallow features, thus ignoring the role of the global context features for the segmentation task. Consequently, they have limited capability on extensive medical segmentation tasks. The purpose of this work is to devise a neural network with global context feature information for accomplishing medical image segmentation of different tasks. The proposed global context network (GC-Net) consists of two components; feature encoding and decoding modules. We use multiple convolutions and batch normalization layers in the encoding module. On the other hand, the decoding module is formed by a proposed global context attention (GCA) block and squeeze and excitation pyramid pooling (SEPP) block. The GCA module connects low-level and high-level features to produce more representative features, while the SEPP module increases the size of the receptive field and the ability of multi-scale feature fusion. Moreover, a weighted cross entropy loss is designed to better balance the segmented and non-segmented regions. The proposed GC-Net is validated on three publicly available datasets and one local dataset. The tested medical segmentation tasks include segmentation of intracranial blood vessel, retinal vessels, cell contours, and lung. Experiments demonstrate that, our network outperforms state-of-the-art methods concerning several commonly used evaluation metrics. Medical segmentation of different tasks can be accurately and effectively achieved by devising a deep convolutional neural network with a global context attention mechanism. [ABSTRACT FROM AUTHOR]

Endovascular intervention is widely used in the treatment of cardiovascular and cerebrovascular diseases. This procedure requires physicians to be highly skilled at manipulating surgical devices such as catheters and guidewires. Virtual reality-based interventional simulators for surgical skills training have many advantages over traditional training methods. Simulation of the behavior of interventional devices is a very challenging task when developing such a simulator. In this paper, we proposed a shared centerline model incorporated with weighted material properties to represent the interventional devices. To efficiently and stably simulate the interactive behavior of these devices, we proposed an adaptive deviation-feedback approach to accelerate the convergence of the iterations. Additionally we proposed a track based control strategy to predict the behavior of the devices based on their past track to further decrease the amount of computation needed for the achievement of new equilibrium. The performance of our approach is experimentally validated via two endovascular interventional applications. [ABSTRACT FROM AUTHOR]

Background: Observational research reported the underlying correlation of metabolic syndrome (MetS) and its components with rotator cuff tendinopathy (RCT), but their causality remained unclear. This study aimed to investigate whether genetically predicted MetS was related to the risk of RCT. Methods: Both univariable and multivariable Mendelian randomization (MR) analysis was applied using summary-level data from the most comprehensive genome-wide association studies to estimate the associations of MetS and its component with RCT, with the inverse variance weighted (IVW) as the primary method, and the method of Causal Analysis Using Summary Effect Estimates (CAUSE) as a supplement for false positives detection. The mediation analysis was furtherly used for the assessment of direct and indirect effects. Results: Univariable analysis revealed that genetically predicted MetS (OR: 1.0793; 95% CI 1.0311 to 1.1297), body mass index (BMI) (OR 1.2239; 95% CI 1.1357 to 1.3189), and waist circumference (WAC) (OR 1.3177; 95% CI 1.2015 to 1.4451) had a significant positive association with the risk of RCT. Triglycerides and systolic blood pressure were suggestively associated with RCT risk. These associations were also identified by CAUSE. There was independent causality of BMI (OR: 1.1806; 95% CI 1.0788 to 1.2920) and WAC (OR 1.3716; 95% CI 1.2076 to 1.5580) on RCT after adjustment for confounders. No mediator was found in the causal associations. Conclusion: Our study revealed the genetic causality of MetS and its components, especially BMI and WAC, with RCT risk. Early prevention and diagnosis of excess central adiposity contributing to MetS are significant in the RCT risk management. [ABSTRACT FROM AUTHOR]

Lung nodules are small, round, or oval-shaped masses of tissue in the lung region. Early diagnosis and treatment of lung nodules can significantly improve the quality of patients’ lives. Because of their small size and the interlaced nature of chest anatomy, detection of lung nodules using different medical imaging techniques becomes challenging. Recently, several methods for computer aided diagnosis (CAD) were proposed to improve the detection of lung nodules with good performances. However, the current methods are unable to achieve high sensitivity and high specificity. In this paper, we propose using deep feature fusion from the non-medical training and hand-crafted features to reduce the false positive results. Based on our experimentation of the public dataset, our results show that, the deep fusion feature can achieve promising results in terms of sensitivity and specificity (69.3% and 96.2%) at 1.19 false positive per image, which is better than the single hand-crafted features (62% and 95.4%) at 1.45 false positive per image. As it stands, fusion features that were used to classify our candidate nodules have resulted in a more promising outcome as compared to the single features from deep learning features and the hand-crafted features. This will improve the current CAD method based on the use of deep feature fusion to more effectively diagnose the presence of lung nodules. [ABSTRACT FROM AUTHOR]

Background: The effective geometric modeling of vascular structures is crucial for diagnosis, therapy planning and medical education. These applications require good balance with respect to surface smoothness, surface accuracy, triangle quality and surface size.Methods: Our method first extracts the vascular boundary voxels from the segmentation result, and utilizes these voxels to build a three-dimensional (3D) point cloud whose normal vectors are estimated via covariance analysis. Then a 3D implicit indicator function is computed from the oriented 3D point cloud by solving a Poisson equation. Finally the vessel surface is generated by a proposed adaptive polygonization algorithm for explicit 3D visualization.Results: Experiments carried out on several typical vascular structures demonstrate that the presented method yields both a smooth morphologically correct and a topologically preserved two-manifold surface, which is scale-adaptive to the local curvature of the surface. Furthermore, the presented method produces fewer and better-shaped triangles with satisfactory surface quality and accuracy.Conclusions: Compared to other state-of-the-art approaches, our method reaches good balance in terms of smoothness, accuracy, triangle quality and surface size. The vessel surfaces produced by our method are suitable for applications such as computational fluid dynamics simulations and real-time virtual interventional surgery. [ABSTRACT FROM AUTHOR]

Background. Ankylosing spondylitis (AS) is a common immune-related systemic chronic inflammatory osteoarthropathy. Previous studies have proven that biologic agents, including IL-17A inhibitors (IL17Ai), TNF-α inhibitor FC fusion protein (TNFiFCP), or fully human monoclonal antibody (TNFiNMA) and JAK inhibitor (JAKi), are effective for AS treatment. Our study is aimed at comparing the clinical efficacy, tolerability, and safety of different biological agents, including novel IL-6 inhibitor (IL6i), IL-23 inhibitor (IL23i), and IL-17 A/F dual variable domain inhibitor (IL17AFi) in AS. Method. PubMed, Scopus, Embase, CNKI, and the Cochrane Library were systematically searched. A frequentist framework network meta-analysis with a random-effects model was performed. Ranking effects were calculated by surface under the cumulative ranking analysis (SUCRA) and cluster-rank analysis. Results. IL17AFi reported both the highest ASAS40 (SUCRA = 91.4 %) and ASAS20 (SUCRA = 92.5 %) response, while IL6i and IL23i reported the lowest responses (SUCRA = 6.6 % and 19.9%, respectively). With the exceptions of IL6i (RR 0.60, 95% CI (0.22 to 1.67) for ASAS40 and 1.36 (0.71 to 2.58) for ASAS20) and IL23i (0.98 (0.68 to 1.40) for ASAS40 and 0.91 (0.70 to 1.19) for ASAS20), all biological drugs demonstrated statistically superior ASAS responses than placebo. TNFiFMA performed best in the suppression of disease activity (SUCRA = 77.4 %, SMD 2.35, and 95% CI (1.11 to 3.59)) and functional improvement (SUCRA = 68.8 %, SMD 1.67, and 95% CI (0.59 to 2.74)). There were no significant differences in tolerability or safety between biologic drugs and placebo. Conclusions. The novel IL-17 A/F dual variable domain inhibitor, bimekizumab, may be an ideal future treatment choice for AS, while IL-23 and IL-6 inhibitors demonstrate little potential in the treatment of AS. For patients with rapid disease progression and severe functional limitation, TNF-α inhibitors, especially infliximab, are safe and effective and could be a first-line treatment choice. [ABSTRACT FROM AUTHOR]

To determine if the effect of manipulative acupuncture monitored by laser speckle contrast imaging (LSCI) can improve facial blood perfusion in patients with severe Bell's palsy. This randomized, single-blind, controlled trial included 120 newly diagnosed patients (within 14 days) with severe Bell's palsy (House–Brackmann grading system (HBGS) ≥ grade IV). The patients were randomized (1 : 1) to receive either acupoints acupuncture combined with manipulations of twirling, lifting, and thrusting treatments (manipulative acupuncture) or acupoints acupuncture therapy alone (simple acupuncture). These treatments consisted of a total of 24 sessions, three times per week, and each treatment lasted for 30 min. Following 8 weeks of treatment and 6 months after the initial onset of facial palsy, facial nerve functioning was scored (HBGS) and clinical efficacy was measured. The patients' facial blood perfusion significantly improved following manipulative acupuncture assisted by LSCI compared with that at baseline (P < 0.01). At the conclusion of the 8-week treatment, both groups showed improvement; however, the recovery rate was significantly different (manipulative acupuncture 53.3% vs. simple acupuncture 33.9%, P < 0.05). Follow-up analysis at 6 months after the onset of facial palsy revealed a significantly higher recovery rate (91.7% vs. 78.0%; P < 0.05). In addition, the number of treatments in the observation group was less than that in the simple acupuncture therapy group (P < 0.05). Compared with simple acupuncture therapy, manipulative acupuncture therapy led to a more significant recovery rate in the treatment of severe Bell's palsy and required a shorter course of treatment. This trial was registered with ChiCTR1800019463. [ABSTRACT FROM AUTHOR]

Background: Colonoscopy plays an important role in the clinical screening and management of colorectal cancer. The traditional 'see one, do one, teach one' training style for such invasive procedure is resource intensive and ineffective. Given that colonoscopy is difficult, and time-consuming to master, the use of virtual reality simulators to train gastroenterologists in colonoscopy operations offers a promising alternative.Methods: In this paper, a realistic and real-time interactive simulator for training colonoscopy procedure is presented, which can even include polypectomy simulation. Our approach models the colonoscopy as thick flexible elastic rods with different resolutions which are dynamically adaptive to the curvature of the colon. More material characteristics of this deformable material are integrated into our discrete model to realistically simulate the behavior of the colonoscope.Conclusion: We present a simulator for training colonoscopy procedure. In addition, we propose a set of key aspects of our simulator that give fast, high fidelity feedback to trainees. We also conducted an initial validation of this colonoscopic simulator to determine its clinical utility and efficacy. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) data analysis is of great significance to the diagnosis of cardiovascular disease. ECG compression should be processed in real time, and the data should be based on lossless compression and have high predictability. In terms of the real time aspect, short-time Fourier transformation is applied to the processing of signal wave for reducing computational time. For the lossless compression requirement, wavelet-transformation that is a coding algorithm can be used to avoid loss of data. In practice, compression is required to avoid storing redundant recording data that are not useful in the diagnosis platform. The obtained data can be preprocessed to remove noise by using wavelet transform, and then a multi-objective optimize neural network model is used to extract feature information. Compared with the existing traditional methods such as direct data processing method and transform method, our proposed compression model has self-learning ability to achieve high data compression ratio at 1:19 without losing important ECG information and compromising quality. Upon testing, we demonstrated that the proposed ECG data compression method based on multi-objective optimization neural network is effective and efficient in clinical practice. [ABSTRACT FROM AUTHOR]

An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity. [ABSTRACT FROM AUTHOR]

An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity. [ABSTRACT FROM AUTHOR]

Polygonized isosurfaces of anatomical structures commonly suffer from severe artifacts (e.g., noise and staircases), due to inhomogeneous binary volumes. Most state-of-the-art techniques can reduce these artifacts but inevitably ruining anatomical structures’ morphology. Given an initial polygonization of an isosurface, we first eliminate these apparent staircases based on a context-aware Laplace filter, and then solve the morphology-preserving problem of anatomical structures as an optimization of the local spatial quadrics (LSQ) of fitted Bézier surfaces during mesh evolution. This results in a conceptually simple approach that provides a unified framework for not only handling artifacts, but also for enabling the morphology preservation of anatomical structures. [ABSTRACT FROM AUTHOR]

Highlights: [•] A multi-pattern neighborhood system of MRF to segment fine cerebral vessels. [•] An analytical expression for estimating the regularization parameter of MRF. [•] An iterative algorithm to estimate the parameter based on MPL and space coding. [ABSTRACT FROM AUTHOR]

Abstract: Advanced 3D optical and laser scanners can generate mesh models with high-resolution details, while inevitably introducing noises from various sources and mesh irregularity due to inconsistent sampling. Noises and irregularity of a scanned model prohibit its use in practical applications where high quality models are required. However, optimizing a noisy mesh while preserving its geometric features is a challenging task. We present a robust two-step approach to meet the challenges of noisy mesh optimization. In the first step, we propose a joint bilateral filter to remove noises on a mesh while maintaining its volume and preserving its features. In the second step, we develop a constrained Laplacian smoothing scheme by adding two kinds of constraints into the original Laplacian equation. As most noises have been removed in the first step, we can easily detect feature edges from the model and add them as constraints in the Laplacian smoothing. As a result, the constrained scheme can simultaneously preserve sharp features and avoid volume shrinkage during mesh smoothing. By integrating these two steps, our approach can effectively remove noises, maintain features, improve regularity for a noisy mesh, as well as avoid side-effects such as volume shrinkage. Extensive qualitative and quantitative experiments have been performed on meshes with synthetic and raw noises to demonstrate the feasibility and effectiveness of our approach. [Copyright &y& Elsevier]

Sculpting pattern onto free-form surfaces is a potential method for many applications such as jewelry, glass blowmolding, injection molding, even automatically spray painting on the surfaces, etc. In this paper, a method of tool path generation for pattern sculpting on free-form surfaces is developed. First, 2D pattern is converted into discrete data points for tool path generation from digital image using image processing technique. To map the discrete pattern onto the part surface, mesh mapping is introduced, which is used for flattening the part surfaces onto a plane. On this plane, the discrete pattern is scaled, rotated, and translated to a desirable location. Then using the mapping of the part surface to the plane as a guide, the pattern to be sculpted is inversely mapped back onto the part surface. On this basis, according to the selected cutter and the depth of cut, the tool path is generated. Since the method does not rely on processing on the surface parametric domain, it is not limited to single surface patch, and is also equally applicable to multiple trimmed surfaces and mesh surfaces, etc. During calculation of the projected pattern, the proposed method does not involve any iterative numerical procedures, thus it is also robust and efficient. Finally, the method is implemented and tested on several typical examples to verify its validity. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the clinical efficacy and feasibility of one-stage surgical treatment for upper thoracic spinal tuberculosis by internal fixation, debridement, and combined interbody and posterior fusion via a posterior-only approach. Methods: Fourteen patients (eight males, six females) with upper thoracic tuberculosis whose lesions were confined to two adjacent segments were admitted to our hospital. Their ages ranged from 23 to 72 years (average, 50 years). The American Spinal Injury Association (ASIA) impairment scale was used to assess neurological function. ASIA classification showed that preoperatively, one patient was grade A, two patients were grade B, eight patients were grade C, and three patients were grade D. All patients were treated with one-stage surgical treatment by internal fixation, debridement, and combined interbody and posterior fusion via a posterior-only approach. Patients were evaluated preoperatively and postoperatively by measurement of thoracic kyphotic angles using Cobb angle evaluation, determination of erythrocyte sedimentation rate (ESR), evaluation of ASIA impairment scale, and radiological examination. Results: Operation time ranged from 70 to 135 min, (average, 110 min). Intraoperative blood loss ranged from 200 to 950 mL (average, 450 mL). All patients were followed up for 22 to 48 months postoperatively (average, 31.5 months). No sinus tract formation, cerebrospinal meningitis, or recurrence of tuberculosis occurred. All patients had significant postoperative improvement in ASIA classification scores. The thoracic kyphotic angles were significantly decreased to 12°-26° postoperatively, and at final follow-up were 13°-28°. The ESR recovered to normal within 6 months postoperatively in all patients. Bone fusion was achieved within 3-8 months (average, 5.5 months). Conclusions: One-stage surgical treatment for upper thoracic spinal tuberculosis by internal fixation, debridement, and combined interbody and posterior fusion via a posterior-only approach can be an effective and feasible treatment method. [ABSTRACT FROM AUTHOR]

Objective: In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist's workload on scrutinizing the medical images.Method: We present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into.Results: We only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively.Conclusion: The results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs. [ABSTRACT FROM AUTHOR]