NVTrans-UNet: Neighborhood vision transformer based U-Net for multi-modal cardiac MR image segmentation.

With the rapid development of artificial intelligence and image processing technology, medical imaging technology has turned into a critical tool for clinical diagnosis and disease treatment. The extraction and segmentation of the regions of interest in cardiac images are crucial to the diagnosis of cardiovascular diseases. Due to the erratically diastolic and systolic cardiac, the boundaries of Magnetic Resonance (MR) images are quite fuzzy. Moreover, it is hard to provide complete information using a single modality due to the complex structure of the cardiac image. Furthermore, conventional CNN-based segmentation methods are weak in feature extraction. To overcome these challenges, we propose a multi-modal method for cardiac image segmentation, called NVTrans-UNet. Firstly, we employ the Neighborhood Vision Transformer (NVT) module, which takes advantage of Neighborhood Attention (NA) and inductive biases. It can better extract the local information of the cardiac image as well as reduce the computational cost. Secondly, we introduce a Multi-modal Gated Fusion (MGF) network, which can automatically adjust the contributions of different modal feature maps and make full use of multi-modal information. Thirdly, the bottleneck layer with Atrous Spatial Pyramid Pooling (ASPP) is proposed to expand the feature receptive field. Finally, the mixed loss is added to the cardiac image to focus the fuzzy boundary and realize accurate segmentation. We evaluated our model on MyoPS 2020 dataset. The Dice score of myocardial infarction (MI) was 0.642 ± 0.171, and the Dice score of myocardial infarction + edema (MI + ME) was 0.574 ± 0.110. Compared with the baseline, the MI increases by 11.2%, and the MI + ME increases by 12.5%. The results show the effectiveness of the proposed NVTrans-UNet in the segmentation of MI and ME.

A laparoscopic radical inguinal lymphadenectomy approach partly preserving great saphenous vein branches can benefit for patients with penile carcinoma.

BACKGROUND: Inguinal lymphadenectomy (iLAD) is effective for penile carcinoma treatment, but usually results in many complications. This study aims to clinically evaluate the feasibility and clinical significance of a laparoscopic radical iLAD approach partly preserving great saphenous vein branches for penile carcinoma patients. METHODS: A total of 48 patients with penile cancer who underwent laparoscopic radical iLAD with retention of the great saphenous vein in Henan Cancer Hospital from 2012 Jan to 2020 Dec were included in this study. Sixteen penile carcinoma patients who underwent laparoscopic radical iLAD preserving parts of superficial branches of the great saphenous vein were identified as the sparing group, and the matched 32 patients who incised those branches were identified as control group. This new procedure was performed by laparoscopy, preserving parts of superficial branches of the great saphenous vein, superficial lateral and medial femoral veins. Clinicopathological features and perioperative variables were recorded. Postoperative complications, including skin flap necrosis, lymphorrhagia, and lower extremity edema were analyzed retrospectively. RESULTS: We found that the operative time of the sparing group is significantly longer than the control group (p = 0.011). There was no statistical difference in intraoperative blood loss, the lymph node number per side, average time to remove the drainage tube and postoperative hospital stay between the two groups. Compared to the control group, the sparing group showed a significantly decreased incidence of lower extremity edema (p = 0.018). The preservation of parts of superficial branches of the great saphenous vein was mainly decreased the incidence of edema below ankle (p = 0.034). CONCLUSIONS: This study demonstrated that the iLAD with preserving parts of superficial branches of the great saphenous vein, with a decreased incidence of postoperative complications, is a safe and feasible approach for penile cancer.

Clinical significance of circulating tumour cells and Ki-67 in renal cell carcinoma.

BACKGROUND: Renal cell carcinoma (RCC) is a common malignant tumour of the genitourinary system. We aimed to analyse the potential value of metastasis-related biomarkers, circulating tumour cells (CTCs) and the proliferative marker Ki-67 in the diagnosis of RCC. METHODS: Data from 24 laparoscopic radical nephrectomies (RNs) and 17 laparoscopic partial nephrectomies (PNs) were collected in 2018. The numbers and positive rates of CTCs and circulating tumour microemboli (CTM) in the peripheral blood were obtained at three different time points: just before surgery, immediately after surgery and 1 week after surgery. Ki-67 protein expression was evaluated in the RCC tissue by immunohistochemistry. RESULTS: Except for the statistically significant association between the preoperative CTC counts and tumour size, no association between the number and positive rate of perioperative CTCs and clinicopathological features was found. The CTC counts gradually decreased during the perioperative period, and at 1 week after surgery, they were significantly lower than those before surgery. High Ki-67 expression was significantly positively correlated with preoperative CTC counts. In addition, Ki-67 expression was higher in the high CTC group (≥ 5 CTCs). CONCLUSION: Our results suggest that surgical nephrectomy is associated with a decrease in CTC counts in RCC patients. CTCs can act as a potential biomarker for the diagnosis and prognosis of RCC. A careful and sufficient long-term follow-up is needed for patients with high preoperative CTC counts.

Sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive group-sparsity regularization.

OBJECTIVE: In order to solve the blurred structural details and over-smoothing effects in sparse representation dictionary learning reconstruction algorithm, this study aims to test sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive Group-Sparsity Regularization (AGSR-SART). METHODS: First, a new similarity measure is defined in which Covariance is introduced into Euclidean distance, Non-local image patches are adaptively divided into groups of different sizes as the basic unit of sparse representation. Second, the weight factor of the regular constraint terms is designed through the residuals represented by the dictionary, so that the algorithm takes different smoothing effects on different regions of the image during the iterative process. The sparse reconstructed image is modified according to the difference between the estimated value and the intermediate image. Last, The SBI (Split Bregman Iteration) iterative algorithm is used to solve the objective function. An abdominal image, a pelvic image and a thoracic image are employed to evaluate performance of the proposed method. RESULTS: In terms of quantitative evaluations, experimental results show that new algorithm yields PSNR of 48.20, the maximum SSIM of 99.06% and the minimum MAE of 0.0028. CONCLUSIONS: This study demonstrates that new algorithm can better preserve structural details in reconstructed CT images. It eliminates the effect of excessive smoothing in sparse angle reconstruction, enhances the sparseness and non-local self-similarity of the image, and thus it is superior to several existing reconstruction algorithms.

RA-SIFA: Unsupervised domain adaptation multi-modality cardiac segmentation network combining parallel attention module and residual attention unit.

BACKGROUND: Convolutional neural network has achieved a profound effect on cardiac image segmentation. The diversity of medical imaging equipment brings the challenge of domain shift for cardiac image segmentation. OBJECTIVE: In order to solve the domain shift existed in multi-modality cardiac image segmentation, this study aims to investigate and test an unsupervised domain adaptation network RA-SIFA, which combines a parallel attention module (PAM) and residual attention unit (RAU). METHODS: First, the PAM is introduced in the generator of RA-SIFA to fuse global information, which can reduce the domain shift from the respect of image alignment. Second, the shared encoder adopts the RAU, which has residual block based on the spatial attention module to alleviate the problem that the convolution layer is insensitive to spatial position. Therefore, RAU enables to further reduce the domain shift from the respect of feature alignment. RA-SIFA model can realize the unsupervised domain adaption (UDA) through combining the image and feature alignment, and then solve the domain shift of cardiac image segmentation in a complementary manner. RESULTS: The model is evaluated using MM-WHS2017 datasets. Compared with SIFA, the Dice of our new RA-SIFA network is improved by 8.4%and 3.2%in CT and MR images, respectively, while, the average symmetric surface distance (ASD) is reduced by 3.4 and 0.8mm in CT and MR images, respectively. CONCLUSION: The study results demonstrate that our new RA-SIFA network can effectively improve the accuracy of whole-heart segmentation from CT and MR images.

Capture of high energy orbit of Duffing oscillator with time-varying parameters.

This work investigates the time response of a Duffing oscillator with time-varying parameters (excitation frequency, linear stiffness, and mass) by approximate analytical and numerical methods. When the excitation frequency sweep covers the multisolution range, the characteristics of the response (maximum response, jump-up frequency, and jump-down frequency) mainly depend on the frequency sweep rate. If the frequency sweep is ended in the multisolution range, the sweep rate determines the energy orbit that the final response will capture. The results can be explained by comparing the state spaces of the oscillator with the change of basin of attraction of the high-energy orbit during the sweep. Furthermore, if the excitation is fixed at a specific frequency in the multisolution range, a method of natural frequency temporary modulation is proposed for the capture of the high-energy orbit. For practical realization, this method is completed by two ways, that is, the linear stiffness temporary modulation and mass temporary modulation. The modulation schedules of time-varying linear stiffness and mass are determined quantitatively, and it is proved that they could help capture the high-energy orbit similar to the excitation frequency sweep. The developed methods and results of this work can provide the guidelines to design nonlinear systems to work on preferred energy orbit.

SUD-GAN: Deep Convolution Generative Adversarial Network Combined with Short Connection and Dense Block for Retinal Vessel Segmentation.

Since morphology of retinal blood vessels plays a key role in ophthalmological disease diagnosis, retinal vessel segmentation is an indispensable step for the screening and diagnosis of retinal diseases with fundus images. In this paper, deep convolution adversarial network combined with short connection and dense block is proposed to separate blood vessels from fundus image, named SUD-GAN. The generator adopts U-shape encode-decode structure and adds short connection block between convolution layers to prevent gradient dispersion caused by deep convolution network. The discriminator is all composed of convolution block, and dense connection structure is added to the middle part of the convolution network to strengthen the spread of features and enhance the network discrimination ability. The proposed method is evaluated on two publicly available databases, the DRIVE and STARE. The results show that the proposed method outperforms the state-of-the-art performance in sensitivity and specificity, which were 0.8340 and 0.9820, and 0.8334 and 0.9897 respectively on DRIVE and STARE, and can detect more tiny vessels and locate the edge of blood vessels more accurately.

DCU-Net: Multi-scale U-Net for brain tumor segmentation.

BACKGROUND: Brain tumor segmentation plays an important role in assisting diagnosis of disease, treatment plan planning, and surgical navigation. OBJECTIVE: This study aims to improve the accuracy of tumor boundary segmentation using the multi-scale U-Net network. METHODS: In this study, a novel U-Net with dilated convolution (DCU-Net) structure is proposed for brain tumor segmentation based on the classic U-Net structure. First, the MR brain tumor images are pre-processed to alleviate the class imbalance problem by reducing the input of the background pixels. Then, the multi-scale spatial pyramid pooling is used to replace the max pooling at the end of the down-sampling path. It can expand the feature receptive field while maintaining image resolution. Finally, a dilated convolution residual block is combined to improve the skip connections in the training networks to improve the network's ability to recognize the tumor details. RESULTS: The proposed model has been evaluated using the Brain Tumor Segmentation (BRATS) 2018 Challenge training dataset and achieved the dice similarity coefficients (DSC) score of 0.91, 0.78 and 0.83 for whole tumor, core tumor and enhancing tumor segmentation, respectively. CONCLUSIONS: The experiment results indicate that the proposed model yields a promising performance in automated brain tumor segmentation.

Improving brain tumor segmentation on MRI based on the deep U-net and residual units.

BACKGROUNDAccurate segmentation of brain tumor depicting on magnetic resonance imaging (MRI) is an important step for doctors to determine optimal treatment plan of Gliomas, which are the common malignant brain tumors that seriously damage patients' health and life.OBJECTThis study aims to improve accuracy and efficiency of brain tumor segmentation on MRI using the advanced deep learning model.METHODIn this study, an improved model based on the U-net for accurate segmentation of brain tumor MRI images, called Deeper ResU-net, is proposed. First, a deep Deeper U-net is built, which has deeper network depth compared with U-net, uses Squeeze Operator to control network parameters and attempts to enhance the feature extraction ability. Then, Deeper ResU-net is formed to eliminate degradation phenomenon of the deep network, in which residual unit is designed and integrated into the Deeper U-net to keep the number of parameters unchanged.RESULTDeeper ResU-net makes the deep network conduct stable training without degrading. Evaluation result shows that the Deeper ResU-net has achieved competitive result with average DSC metrics of 0.9, 0.82, 0.88 for Complete tumor region, Core tumor region and Enhanced tumor region, respectively.CONCLUSIONBy extending the U-net model to a deeper layer and adding the residual structure to ensure effective and stable training of the model, the experiment results demonstrate that applying the improved Deeper ResU-net can effectively eliminate the degradation phenomenon of deep network and improve segmentation performance.

Design of nonlinear active noise control earmuffs for excessively high noise level.

When exposed to high levels of noise, earmuffs are often used to avoid hearing loss. However, active noise control earmuffs may exhibit nonlinearities under excessive levels of noise, due to their low-power characteristics of the loudspeakers, and thus nonlinear control algorithms are required to improve the control performance. In this paper, an analytical model of a nonlinear active noise control earmuff is investigated. Based on this model, a robust state feedback control law is designed in the framework of linear matrix inequalities with respect to the parametric uncertainties of the loudspeaker and the limitation of control input. Then the backstepping approach is adopted to force the nonlinear part of the loudspeaker to track the derived state feedback signal and estimate the unknown parameters. Both recorded vehicle noise and multi-frequency noise are used to test the effectiveness of the proposed controller and the control performance is compared with that of a widely accepted nonlinear generalized functional link artificial neural network algorithm. Simulation results demonstrate that the proposed controller is capable of attenuating the interior noise and reducing harmonic and intermodulation distortions significantly.

Nonrigid registration of medical image based on adaptive local structure tensor and normalized mutual information.

Nonrigid registration of medical images is especially critical in clinical treatment. Mutual information is a popular similarity measure for medical image registration; however, only the intensity statistical characteristics of the global consistency of image are considered in MI, and the spatial information is ignored. In this paper, a novel intensity-based similarity measure combining normalized mutual information with spatial information for nonrigid medical image registration is proposed. The different parameters of Gaussian filtering are defined according to the regional variance, the adaptive Gaussian filtering is introduced into the local structure tensor. Then, the obtained adaptive local structure tensor is used to extract the spatial information and define the weighting function. Finally, normalized mutual information is distributed to each pixel, and the discrete normalized mutual information is multiplied with a weighting term to obtain a new measure. The novel measure fully considers the spatial information of the image neighborhood, gives the location of the strong spatial information a larger weight, and the registration of the strong gradient regions has a priority over the small gradient regions. The simulated brain image with single-modality and multimodality are used for registration validation experiments. The results show that the new similarity measure improves the registration accuracy and robustness compared with the classical registration algorithm, reduces the risk of falling into local extremes during the registration process.

Regulation of ELL2 stability and polyubiquitination by EAF2 in prostate cancer cells.

BACKGROUND: Elongation factor for RNA polymerase 2 (ELL2) and ELL associated factor 2 (EAF2) have been reported to have tumor suppressive properties in prostate epithelial cells. AIMS: We investigated ELL2 expression in human prostate cancer specimens, and ELL2 protein stability and ubiquitination in prostate cancer cells. MATERIALS AND METHODS: Immunostaining analysis of human prostate cancer specimens was used to determine ELL2 expression in tumor and normal tissues. ELL2 knockdown in prostate cancer cell lines LNCaP and C4-2 was used to compare proliferation and motility. Deletion and site-directed mutagenesis was used to identify amino acid residues in ELL2 that were important for degradation. RESULTS: ELL2 protein was downregulated in prostate cancer specimens and was up-regulated by androgens in prostate cancer cell lines LNCaP and C4-2. ELL2 knockdown enhanced prostate cancer cell proliferation and motility. ELL2 protein has a short half-life and was stabilized by proteasome inhibitor MG132. Amino acid residues K584 and K599 in ELL2 were important for ELL2 degradation. EAF2 could stabilize ELL2 and inhibited its polyubiquitination. CONCLUSION: Our findings provide further evidence that ELL2 is a potential tumor suppressor frequently down-regulated in clinical prostate cancer specimens and provides new insights into regulation of ELL2 protein level by polyubiquitination and EAF2 binding.

A reactive molecular dynamics study of graphene oxide sheets in different saturated states: structure, reactivity and mechanical properties.

For GO related nanocomposite design, it is of great importance to understand the behavior of water molecules ultra-confined in the interlayer region of graphene oxide (GO) sheets. In this research, to gain molecular insights into the influence of water on the properties of GO sheets, reactive force field molecular dynamics was employed to model a GO sheet with a water content of 1.3 wt%, 11.5 wt%, 18 wt% and 23.5 wt%. The epoxy and hydroxyl groups in the GO sheet exhibit high reactivity: the proton transferred from hydroxyl to dissociated epoxy contributes to carbonyl formation, which enhances the polarity of the GO sheet and strengthens the H-bond network between the functional groups. The epoxy, hydroxyl and newly formed carbonyl groups contribute to the structural hydrogen bonding with high stability. With increasing water content, the interlayer structural H-bonds between functional groups are gradually substituted by those contributed by water molecules, which weakens the interlayer stiffness and cohesive strength for GO sheets. Furthermore, the reactive force field allows coupling between the mechanical response and chemical reactions during uniaxial tensile deformation in the intra-layer direction. On the one hand, the relative epoxy bond is stretched until it is broken and transformed into a carbonyl group to resist tensile loading. On the other hand, interlayer water molecules, attacking the deformed GO sheets, are dissociated into carboxyl groups in the broken region.

Reactive force-field molecular dynamics study on graphene oxide reinforced cement composite: functional group de-protonation, interfacial bonding and strengthening mechanism.

Graphene oxide (GO) reinforced cement nanocomposites open up a new path for sustainable concrete design. In this paper, reactive force-field molecular dynamics was utilized to investigate the structure, reactivity and interfacial bonding of calcium silicate hydrate (C-S-H)/GO nanocomposite functionalized by hydroxyl (C-OH), epoxy (C-O-C), carboxyl (COOH) and sulfonic (SO3H) groups with a coverage of 10%. The silicate chains in the hydrophilic C-S-H substrate provided numerous non-bridging oxygen sites and counter ions (Ca ions) with high reactivity, which allowed interlayer water molecules to dissociate into Si-OH and Ca-OH. On the other hand, protons dissociated from the functional groups and transferred to non-bridging sites in C-S-H, producing carbonyl (C[double bond, length as m-dash]O) and Si-OH. The de-protonation degree of the different groups in the vicinity of the C-S-H surface was in the following order: COOH (SO3H) > C-OH > C-O-C. In the GO-COOH sheet, most COOH groups were de-protonated to COO- groups, which enhanced the polarity and hydrophilicity of the GO sheets and formed stable COOCa bonds with neighboring Ca ions. The de-protonated COO- could also accept H bonds from Si-OH in the C-S-H gel, which further strengthened the interfacial connection. On the contrary, in the GO-Oo sheet, only 8% of the epoxy group was stretched open by the Ca ions and transformed to carbonyl group, showing weak polarity and connection with the C-S-H sheet. Furthermore, uniaxial tensile test on different C-S-H/GO models revealed that C-S-H reinforced with GO-COOH and GO-OH had better interfacial cohesive strength and ductility than that observed under tensile loading. Under the reaction force field, the dissociation of water, the proton exchange between the C-S-H and GO structure, and Oc-Ca-Os bond breakage occurred to resist tensile loading. The weakest mechanical behavior observed in the G/C-S-H, GO-Oo/C-S-H and GO-SO3H/C-S-H composites was attributed to the poor bonding, dissociation of functional groups and instability of atoms in the interface region. Hopefully, the molecular-scale strengthening mechanisms could provide a scientific guide for sustainable design of cement composites.

Molecular structure, dynamics, and mechanical behavior of sodium aluminosilicate hydrate (NASH) gel at elevated temperature: a molecular dynamics study.

Sodium aluminosilicate hydrate (NASH) gel is the primary adhesive constituent in environmentally friendly geopolymer. In this study, to understand the thermal behavior of the material, molecular dynamics was utilized to investigate the molecular structure, dynamic property, and mechanical behavior of NASH gel subjected to temperature elevation from 300 K to 1500 K. The aluminosilicate skeleton in NASH gel provides plenty of oxygen sites to accept H-bond from the invading water molecules. Upon heating, around 18.2% of water molecules are decomposed and produce silicate and aluminate hydroxyls. About 87% of hydroxyls are associated with the aluminate skeleton, which weakens the Al-O bonds and disturbs the O-Al-O angle and the local structure, transforming it from an aluminate tetrahedron to a pentahedron and octahedron. With increasing temperature, both Al-O-Si and Si-O-Si bonds are stretched to be broken and the network structure of the NASH gel is gradually transformed into a branch and chain structure. Furthermore, the self-diffusivity of water molecules and sodium dramatically increases with the elevation of temperature, because the decrease in connectivity of the aluminosilicate network reduces the chemical and geometric restriction on the water and ions in NASH gel under higher temperatures. The high temperature also contributes to around 63% of the water molecules further dissociating and hydroxyl groups forming; meanwhile proton exchange between the water molecules and aluminosilicate network frequently takes place. In addition, a uniaxial tensile test was utilized to study the mechanical behavior of the NASH gel at different temperatures. During the tensile test, the aluminosilicate network was found to depolymerize into a branch or chain structure which plays a critical role in resisting the tensile loading. In this process, the breakage of the aluminosilicate skeleton is accompanied with hydrolytic reactions that further deteriorate the structure. Due to the reduction of the chemical bond stability at elevated temperature, both the tensile strength and stiffness of the NASH gel are weakened significantly. However, the ductility of the NASH gel is improved because of the higher extent of structural arrangement at the yield stage and partly due to the lower water attack. Hopefully, the present study can provide valuable molecular insights on the design of alkali-activated materials with high sustainability and durability.

An online secondary path modeling method with regularized step size and self-tuning power scheduling.

This paper investigates the active noise control algorithms and improves them by using online secondary path modeling. The proposed method uses three adaptive filters to track the convergence of the system as well as reduce the target noise. By theoretical analysis, the optimized step size and injected random noise gain are derived. The step size is varied according to the convergence of three adaptive filters and the gain of injected random noise is proportional to the power of modeling error, which makes the method more stable even in the presence of strong perturbation. Compared with previous methods, the proposed method improves the convergence rate and estimation accuracy for both the active control system and the secondary path modeling process with less increase of computational complexity. The simulation results verify the above analysis by controlling three different kinds of noise.

A systematic analysis of the resistance and sensitivity of HER2YVMA receptor tyrosine kinase mutant to tyrosine kinase inhibitors in HER2-positive lung cancer.

Human epidermal growth factor receptor 2 (HER2) has become a well-established target for the treatment of HER2-positive lung cancer. However, a frequently observed in-frame mutation that inserts amino acid quadruplex Tyr776-Val777-Met778-Ala779 at G776 (G776(YVMA)) in HER2 kinase domain can cause drug resistance and sensitivity, largely limiting the application of reversible tyrosine kinase inhibitors in lung cancer therapy. A systematic investigation of the intermolecular interactions between the HER2(YVMA) mutant and clinical small-molecule inhibitors would help to establish a complete picture of drug response to HER2 G776(YVMA) insertion in lung cancer, and to design new tyrosine kinase inhibitors with high potency and selectivity to target the lung cancer-related HER2(YVMA) mutant. Here, we combined homology modeling, ligand grafting, structure minimization, molecular simulation and binding affinity analysis to profile a number of tyrosine kinase inhibitors against the G776(YVMA) insertion in HER2. It is found that the insertion is far away from HER2 active pocket and thus cannot contact inhibitor ligand directly. However, the insertion is expected to induce marked allosteric effect on some regions around the pocket, including A-loop and hinges connecting between the N- and C-lobes of HER2 kinase domain, which may exert indirect influence to inhibitor binding. Most investigated inhibitors exhibit weak binding strength to both wild-type and mutant HER2, which can be attributed to steric hindrance that impairs ligand compatibility with HER2 active pocket. However, the cognate inhibitor lapatinib and the non-cognate inhibitor bosutinib were predicted to have low affinity for wild-type HER2 but high affinity for HER2(YVMA) mutant, which was confirmed by subsequent kinase assay experiments; the inhibitory potencies of bosutinib against wild-type and mutant HER2 were determined to be IC(50) > 1000 and =27 nM, respectively, suggesting that the bosutinib might be exploited as a selective inhibitor for mutant over wild-type HER2. Structural examination revealed that formation of additional non-bonded interactions such as hydrogen bonds and hydrophobic contacts with HER2 A-loop region due to G776(YVMA) insertion is the primary factor to improve bosutinib affinity upon the mutation.

MSRA-Net: multi-channel semantic-aware and residual attention mechanism network for unsupervised 3D image registration.

OBJECTIVE: Convolutional neural network (CNN) is developing rapidly in the field of medical image registration, and the proposed U-Net further improves the precision of registration. However, this method may discard certain important information in the process of encoding and decoding steps, consequently leading to a decline in accuracy. To solve this problem, a multi-channel semantic-aware and residual attention mechanism network (MSRA-Net) is proposed in this paper. Approach: Our proposed network achieves efficient information aggregation by cleverly extracting the features of different channels. Firstly, a context-aware module (CAM) is designed to extract valuable contextual information. And the depth-wise separable convolution is employed in the CAM to alleviate the computational burden. Then, a new multi-channel semantic-aware module (MCSAM) is designed for more comprehensive fusion of up-sampling features. Additionally, the residual attention module (RAM) is introduced in the up-sampling process to extract more semantic information and minimize information loss. Main results: This study utilizes Dice score, ASSD and negative Jacobian determinant evaluation metrics to evaluate the influence of registration. The experimental results demonstrate that our proposed MSRA-Net has the highest accuracy compared to several state-of-the-art methods. Moreover, our network has demonstrated the highest Dice score across multiple datasets, thereby indicating that the superior generalization capabilities of our model. Significance: The proposed MSRA-Net offers a novel approach to improve medical image registration accuracy, with implications for various clinical applications.Our implementation is available at https://github.com/shy922/MSRA-Net.

Case report: Isolated eyelid metastasis of ccRCC 5 years after receiving radical nephrectomy.

Introduction: The most common sites of clear cell renal cell carcinoma(ccRCC) metastasis are the lung, bones, liver and brain; eyelid metastasis is a rare occurrence. Case presentation: We report a case of ccRCC metastasis to the left eyelid after radical nephrectomy, and remission after sunitinib treatment. Conclusions: Although the probability of eyelid metastasis rate is very low, tumor metastasis to the eyelid skin is possible after radical nephrectomy. Therefore, any rash like changes on the skin during the review procedure cannot be ignored by the physician.

DiffGAN: An adversarial diffusion model with local transformer for MRI reconstruction.

Magnetic resonance imaging (MRI) is non-invasive and crucial for clinical diagnosis, but it has long acquisition time and aliasing artifacts. Accelerated imaging techniques can effectively reduce the scanning time of MRI, thereby decreasing the anxiety and discomfort of patients. Vision Transformer (ViT) based methods have greatly improved MRI image reconstruction, but their computational complexity and memory requirements for the self-attention mechanism grow quadratically with image resolution, which limits their use for high resolution images. In addition, the current generative adversarial networks in MRI reconstruction are difficult to train stably. To address these problems, we propose a Local Vision Transformer (LVT) based adversarial Diffusion model (Diff-GAN) for accelerating MRI reconstruction. We employ a generative adversarial network (GAN) as the reverse diffusion model to enable large diffusion steps. In the forward diffusion module, we use a diffusion process to generate Gaussian mixture distribution noise, which mitigates the gradient vanishing issue in GAN training. This network leverages the LVT module with the local self-attention, which can capture high-quality local features and detailed information. We evaluate our method on four datasets: IXI, MICCAI 2013, MRNet and FastMRI, and demonstrate that Diff-GAN can outperform several state-of-the-art GAN-based methods for MRI reconstruction.