Nail fold capillaroscopy is an important means of monitoring human health. Panoramic nail fold images improve the efficiency and accuracy of examinations. However, the acquisition of panoramic nail fold images is seldom studied and the problem manifests of few matching feature points when image stitching is used for such images. Therefore, this paper presents a method for panoramic nail fold image stitching based on vascular contour enhancement, which first solves the problem of few matching feature points by pre-processing the image with contrast-constrained adaptive histogram equalization (CLAHE), bilateral filtering (BF), and sharpening algorithms. The panoramic images of the nail fold blood vessels are then successfully stitched using the fast robust feature (SURF), fast library of approximate nearest neighbors (FLANN) and random sample agreement (RANSAC) algorithms. The experimental results show that the panoramic image stitched by this paper\'s algorithm has a field of view width of 7.43 mm, which improves the efficiency and accuracy of diagnosis.

Visual Transformers(ViT) have made remarkable achievements in the field of medical image analysis. However, ViT-based methods have poor classification results on some small-scale medical image classification datasets. Meanwhile, many ViT-based models sacrifice computational cost for superior performance, which is a great challenge in practical clinical applications. In this paper, we propose an efficient medical image classification network based on an alternating mixture of CNN and Transformer tandem, which is called Eff-CTNet. Specifically, the existing ViT-based method still mainly relies on multi-head self-attention (MHSA). Among them, the attention maps of MHSA are highly similar, which leads to computational redundancy. Therefore, we propose a group cascade attention (GCA) module to split the feature maps, which are provided to different attention heads to further improves the diversity of attention and reduce the computational cost. In addition, we propose an efficient CNN (EC) module to enhance the ability of the model and extract the local detail information in medical images. Finally, we connect them and design an efficient hybrid medical image classification network, namely Eff-CTNet. Extensive experimental results show that our Eff-CTNet achieves advanced classification performance with less computational cost on three public medical image classification datasets.

UNASSIGNED: To investigate the status quo and the needs of medical imaging technicians (MITs) in the radiology department of secondary and tertiary hospitals in China, so as to provide references and support for the development of the medical imaging technology industry and the relevant policymaking by health administrative departments.
UNASSIGNED: The questionnaire was developed by the Chinese Society of Imaging Technology. The radiology department of each hospital involved in the survey recommended one MIT to fill out the online questionnaire. The contents included: (a) the basic information of the hospital; (b) a general overview of the MITs in the hospital; (c) daily work; (d) career development and promotion; (e) research status and needs, etc. Differences in the number of MIT staff were compared using the Mann-Whitney U test and the chi-square test was used to compare the differences in the selected numbers of MITs in need between regions or between different levels of hospitals.
UNASSIGNED: In this investigation, valid questionnaires were finally obtained from a total of 5403 hospitals in 31 provinces in China. The total number of MITs of the hospitals covered in the sample was 67481. The number of MITs in each hospital was 9 (5, 16). The male-to-female ratio was 1.41:1. MITs who were 20 to 40 years old accounted for 78%. The proportions of MITs who had completed doctorate, master\'s, undergraduate, junior college, and technical secondary school or lower level education were 0.6%, 3.3%, 60.7%, 30.8%, and 4.55%, respectively. The proportions of chief MITs, deputy chief MITs, supervisor MITs, primary MITs, assistant technician and those below were 1.0%, 4.21%, 22.1%, 51.8%, and 20.9%, respectively. The overall professional satisfaction of MITs was good. \"Lack of opportunities for learning and communication\" was quoted as the main problem MITs encountered in regard to improving their job-related competency. 59.2% of the respondents had not published any academic papers in the past five years, and only 7.0% of the respondents had published in journals included in the Science Citation Index (SCI) in the past five years.
UNASSIGNED: MITs in China are on average relatively young and the number of MITs has greatly increased. At this stage, more attention should be given to the cultivation of talents and continuing education of MITs and the construction of the discipline should be further strengthened, so as to provide strong support for the development of the medical imaging technology industry in China.

OBJECTIVE: With the widespread clinical application of prostate magnetic resonance imaging (MRI), there has been an increasing demand for lesion detection and accurate diagnosis in prostate MR, which relies heavily on satisfactory image quality. Focusing on the primary sequences involved in Prostate Imaging Reporting and Data System (PI-RADS), this study have evaluated common quality issues in clinical practice (such as signal-to-noise ratio (SNR), artifacts, boundaries, and enhancement). The aim of the study was to determine the impact of image quality on clinically significant prostate cancer (csPCa) detection, positive predictive value (PPV) and radiologist\'s diagnosis in different sequences and prostate zones.
METHODS: This retrospective study included 306 patients who underwent prostate MRI with definitive pathological reports from February 2021 to December 2022. All histopathological specimens were evaluated according to the recommendations of the International Society of Urological Pathology (ISUP). An ISUP Grade Group ≥ 2 was considered as csPCa. Three radiologists from different centers respectively performed a binary classification assessment of image quality in the following ten aspects: (1) T2WI in the axial plane: SNR, prostate boundary conditions, the presence of artifacts; (2) T2WI in the sagittal or coronal plane: prostate boundary conditions; (3) DWI: SNR, delineation between the peripheral and transition zone, the presence of artifacts, the matching of DWI and T2WI images; (4) DCE: the evaluation of obturator artery enhancement, the evaluation of dynamic contrast enhancement. Fleiss\' Kappa was used to determine the inter-reader agreement. Wilson\'s 95% confidence interval (95% CI) was used to calculate PPV. Chi-square test was used to calculate statistical significance. A p-value < 0.05 was considered statistically significant.
RESULTS: High-quality images had a higher csPCa detection rate (56.5% to 64.3%) in axial T2WI, DWI, and DCE, with significant statistical differences in SNR in axial T2WI (p 0.002), the presence of artifacts in axial T2WI (p 0.044), the presence of artifacts in DWI (p < 0.001), and the matching of DWI and T2WI images (p < 0.001). High-quality images had a higher PPV (72.5% to 78.8%) and showed significant statistical significance in axial T2WI, DWI, and DCE. Additionally, we found that PI-RADS 3 (24.0% to 52.9%) contained more low-quality images compared to PI-RADS 4-5 (20.6% to 39.3%), with significant statistical differences in the prostate boundary conditions in axial T2WI (p 0.048) and the presence of artifacts in DWI (p 0.001). Regarding the relationship between csPCa detection and image quality in different prostate zones, this study found that significant statistical differences were only observed between high- (63.5% to 75.7%) and low-quality (30.0% to 50.0%) images in the peripheral zone (PZ).
CONCLUSIONS: Prostate MRI quality may have an impact on the diagnostic performance. The poorer image quality is associated with lower csPCa detection rates and PPV, which can lead to an increase in radiologist\'s ambiguous diagnosis (PI-RADS 3), especially for the lesions located at PZ.

In response to the issues of single-scale information loss and large model parameter size during the sampling process in U-Net and its variants for medical image segmentation, this paper proposes a multi-scale medical image segmentation method based on pixel encoding and spatial attention. Firstly, by redesigning the input strategy of the Transformer structure, a pixel encoding module is introduced to enable the model to extract global semantic information from multi-scale image features, obtaining richer feature information. Additionally, deformable convolutions are incorporated into the Transformer module to accelerate convergence speed and improve module performance. Secondly, a spatial attention module with residual connections is introduced to allow the model to focus on the foreground information of the fused feature maps. Finally, through ablation experiments, the network is lightweighted to enhance segmentation accuracy and accelerate model convergence. The proposed algorithm achieves satisfactory results on the Synapse dataset, an official public dataset for multi-organ segmentation provided by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), with Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) scores of 77.65 and 18.34, respectively. The experimental results demonstrate that the proposed algorithm can enhance multi-organ segmentation performance, potentially filling the gap in multi-scale medical image segmentation algorithms, and providing assistance for professional physicians in diagnosis.
针对医学图像分割中U型网络（U-Net）及其变体下采样过程中单尺度信息丢失、模型参数量较大的问题，本文提出了一种基于像素编码和空间注意力的多尺度医学图像分割方法。首先，通过重新设计变换器（Transformer）结构输入策略，提出了像素编码模块，使模型能够从多尺度图像特征中提取全局语义信息，获取更丰富的特征信息，同时在Transformer模块中引入可变形卷积，加快收敛速度的同时提升模块性能。其次，引入空间注意力模块并加入残差连接，使模型能够重点关注融合后特征图的前景信息。最后，通过消融实验实现网络轻量化并提升分割精度，加快模型收敛。本文所提算法在国际计算机医学图像辅助协会官方公开多器官分割公共数据集——突触（Synapse）数据库中得到令人满意的结果，戴斯相似性系数（DSC）和95%豪斯多夫距离系数（HD95）分别为77.65和18.34。实验结果表明，本文算法能够提高多器官分割结果，有望完善多尺度医学图像分割算法的空白，并为专业医师提供辅助诊断。.

UNASSIGNED: Necrotizing enterocolitis (NEC) is a severe neonatal intestinal disease, often occurring in preterm infants following the administration of hyperosmolar formula. It is one of the leading causes of neonatal mortality in the NICU, and currently, there are no clear standards for surgical intervention, which typically depends on the joint discretion of surgeons and neonatologists. In recent years, deep learning has been extensively applied in areas such as image segmentation, fracture and pneumonia classification, drug development, and pathological diagnosis.
UNASSIGNED: Investigating deep learning applications using bedside x-rays to help optimizing surgical decision-making in neonatal NEC.
UNASSIGNED: Through a retrospective analysis of anteroposterior bedside chest and abdominal x-rays from 263 infants diagnosed with NEC between January 2015 and April 2023, including a surgery group (94 cases) and a non-surgery group (169 cases), the infants were divided into a training set and a validation set in a 7:3 ratio. Models were built based on Resnet18, Densenet121, and SimpleViT to predict whether NEC patients required surgical intervention. Finally, the model\'s performance was tested using an additional 40 cases, including both surgical and non-surgical NEC cases, as a test group. To enhance the interpretability of the models, the study employed 2D-Grad-CAM technology to describe the models\' focus on significant areas within the x-ray images.
UNASSIGNED: Resnet18 demonstrated outstanding performance in binary diagnostic capability, achieving an accuracy of 0.919 with its precise lesion imaging and interpretability particularly highlighted. Its precision, specificity, sensitivity, and F1 score were significantly high, proving its advantages in optimizing surgical decision-making for neonatal NEC.
UNASSIGNED: The Resnet18 deep learning model, constructed using bedside chest and abdominal imaging, effectively assists clinical physicians in determining whether infants with NEC require surgical intervention.

Medical image quality is crucial for physicians to ensure accurate diagnosis and therapeutic strategies. However, due to the interference of noise, there are often various types of noise and artifacts in medical images. This not only damages the visual clarity of images, but also reduces the accuracy of information extraction. Considering that the edges of medical images are rich in high-frequency information, to enhance the quality of medical images, a dual attention mechanism, the channel-specific and spatial residual attention network (CSRAN) in the U-Net framework is proposed. The CSRAN seamlessly integrates the U-Net architecture with channel-wise and spatial feature attention (CSAR) modules, as well as low-frequency channel attention modules. Combined with the two modules, the ability of medical image processing to extract high-frequency features is improved, thereby significantly improving the edge effects and clarity of reconstructed images. This model can present better performance in capturing high-frequency information and spatial structures in medical image denoising and super-resolution reconstruction tasks. It cannot only enhance the ability to extract high-frequency features and strengthen its nonlinear representation capability, but also endow strong edge detection capabilities of the model. The experimental results further prove the superiority of CSRAN in medical image denoising and super-resolution reconstruction tasks.

暂无摘要。

BACKGROUND: Biliary atresia (BA), a progressive condition affecting canalicular-bile duct function/anatomy, requires prompt surgical intervention for favorable outcomes. Therefore, we conducted a network meta-analysis of common diagnostic methods to assess their performance and provide evidence-based support for clinical decision-making.
METHODS: We reviewed literature in PubMed, EMBASE, and Cochrane for BA diagnostics. The search included gamma-glutamyl transferase (GGT), direct/combined bilirubin, matrix metalloproteinase 7 (MMP-7), ultrasonic triangular cord sign (TCS), hepatic scintigraphy (HS), and percutaneous cholangiocholangiography/percutaneous transhepatic cholecysto-cholangiography (PCC/PTCC). QUADAS-2 assessed study quality. Heterogeneity and threshold effect were evaluated using I2 and Spearman\'s correlation. We combined effect estimates, constructed SROC models, and conducted a network meta-analysis based on the ANOVA model, along with meta-regression and subgroup analysis, to obtain precise diagnostic performance assessments for BA.
RESULTS: A total of 40 studies were included in our analysis. GGT demonstrated high diagnostic accuracy for BA with a sensitivity of 81.5% (95% CI 0.792-0.836) and specificity of 72.1% (95% CI 0.693-0.748). Direct bilirubin/conjugated bilirubin showed a sensitivity of 87.6% (95% CI 0.833-0.911) but lower specificity of 59.4% (95% CI 0.549-0.638). MMP-7 exhibited a total sensitivity of 91.5% (95% CI 0.893-0.934) and a specificity of 84.3% (95% CI 0.820-0.863). TCS exhibited a sensitivity of 58.1% (95% CI 0.549-0.613) and high specificity of 92.9% (95% CI 0.911-0.944). HS had a high sensitivity of 98.4% (95% CI 0.968-0.994) and moderate specificity of 79.0% (95% CI 0.762-0.816). PCC/PTCC exhibited excellent diagnostic performance with a sensitivity of 100% (95% CI 0.900-1.000) and specificity of 87.0% (95% CI 0.767-0.939). Based on the ANOVA model, the network meta-analysis revealed that MMP-7 ranked second overall, with PCC/PTCC ranking first, both exhibiting superior diagnostic accuracy compared to other techniques. Our analysis showed no significant bias in most methodologies, but MMP-7 and hepatobiliary scintigraphy exhibited biases, with p values of 0.023 and 0.002, respectively.
CONCLUSIONS: MMP-7 and ultrasound-guided PCC/PTCC show diagnostic potential in the early diagnosis of BA, but their clinical application is restricted due to practical limitations. Currently, the cutoff value of MMP-7 is unclear, and further evidence-based medical research is needed to firmly establish its diagnostic value. Until more evidence is available, MMP-7 is not suitable for widespread diagnostic use. Therefore, considering cost and operational simplicity, liver function tests combined with ultrasound remain the most clinically valuable non-invasive diagnostic methods for BA.

Some bacteria, such as Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium), have an inherent ability to locate solid tumours, making them a versatile platform that can be combined with other tools to improve the tumour diagnosis and treatment. In anti-cancer therapy, bacteria function by carrying drugs directly or expressing exogenous therapeutic genes. The application of bacterial imaging in tumour diagnosis, a novel and promising research area, can indeed provide dynamic and real-time monitoring in both pre-treatment assessment and post-treatment detection. Different imaging techniques, including optical technology, acoustic imaging, magnetic resonance imaging (MRI) and nuclear medicine imaging, allow us to observe and track tumour-associated bacteria. Optical imaging, including bioluminescence and fluorescence, provides high-sensitivity and high-resolution imaging. Acoustic imaging is a real-time and non-invasive imaging technique with good penetration depth and spatial resolution. MRI provides high spatial resolution and radiation-free imaging. Nuclear medicine imaging, including positron emission tomography (PET) and single photon emission computed tomography (SPECT) can provide information on the distribution and dynamics of bacterial population. Moreover, strategies of synthetic biology modification and nanomaterial engineering modification can improve the viability and localization ability of bacteria while maintaining their autonomy and vitality, thus aiding the visualization of gut bacteria. However, there are some challenges, such as the relatively low bacterial abundance and heterogeneously distribution within the tumour, the high dimensionality of spatial datasets and the limitations of imaging labeling tools. In summary, with the continuous development of imaging technology and nanotechnology, it is expected to further make in-depth study on tumour-associated bacteria and develop new bacterial imaging methods for tumour diagnosis.