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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]

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]

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]

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]