SearchMorph:Multi-scale Correlation Iterative Network for Deformable Registration

Deformable image registration can obtain dynamic information about images, which is of great significance in medical image analysis. The unsupervised deep learning registration method can quickly achieve high registration accuracy without labels. However, these methods generally suffer from uncorrelated features, poor ability to register large deformations and details, and unnatural deformation fields. To address the issues above, we propose an unsupervised multi-scale correlation iterative registration network (SearchMorph). In the proposed network, we introduce a correlation layer to strengthen the relevance between features and construct a correlation pyramid to provide multi-scale relevance information for the network. We also design a deformation field iterator, which improves the ability of the model to register details and large deformations through the search module and GRU while ensuring that the deformation field is realistic. We use single-temporal brain MR images and multi-temporal echocardiographic sequences to evaluate the model's ability to register large deformations and details. The experimental results demonstrate that the method in this paper achieves the highest registration accuracy and the lowest folding point ratio using a short elapsed time to state-of-the-art

Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics

IntroductionAI-assisted ultrasound diagnosis is considered a fast and accurate new method that can reduce the subjective and experience-dependent nature of handheld ultrasound. In order to meet clinical diagnostic needs better, we first proposed a breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics (hereafter, Auto BI-RADS). In this study, we prospectively verify its performance.MethodsIn this study, the model development was based on retrospective data including 480 ultrasound dynamic videos equivalent to 18122 static images of pathologically proven breast lesions from 420 patients. A total of 292 breast lesions ultrasound dynamic videos from the internal and external hospital were prospectively tested by Auto BI-RADS. The performance of Auto BI-RADS was compared with both experienced and junior radiologists using the DeLong method, Kappa test, and McNemar test.ResultsThe Auto BI-RADS achieved an accuracy, sensitivity, and specificity of 0.87, 0.93, and 0.81, respectively. The consistency of the BI-RADS category between Auto BI-RADS and the experienced group (Kappa:0.82) was higher than that of the juniors (Kappa:0.60). The consistency rates between Auto BI-RADS and the experienced group were higher than those between Auto BI-RADS and the junior group for shape (93% vs. 80%; P = .01), orientation (90% vs. 84%; P = .02), margin (84% vs. 71%; P = .01), echo pattern (69% vs. 56%; P = .001) and posterior features (76% vs. 71%; P = .0046), While the difference of calcification was not significantly different.DiscussionIn this study, we aimed to prospectively verify a novel AI tool based on ultrasound dynamic videos and ACR BI-RADS characteristics. The prospective assessment suggested that the AI tool not only meets the clinical needs better but also reaches the diagnostic efficiency of experienced radiologists

Eco-Environmental Geological Features of Mangrove in Dongchong, Shenzhen City

Mangroves are halophytic forest plant communities located on saline marshes in estuaries of tropical and subtropical bays. They are one of the most vulnerable ecosystems in the world and are severely threatened by urban development, environmental pollution, aquaculture, and other problems. The Dongchong mangrove forest is a relatively well-preserved mangrove forest in China, with a large area and a typical Excoecaria agallocha landscape, which has significant ornamental and ecological conservation value. The aim of this study is to provide basic support not only for mangrove ecological conservation and restoration, but also to construct and manage nature's reserves. The eco-environmental geological characteristics of mangroves were investigated using geology, geomorphology, pedology, ecology, and other methods, and a typical eco-environmental geological profile of the mangrove in Dongchong was drawn. The results show that the strata in the study area are mainly Quaternary sediments and rhyolites of the Nanshancun Formation of the Early Cretaceous. Faults are developed in the west of Dongchong Mangrove Wetland Park. The main environmental geological problems are uneven ground settlement, ground subsidence, and ground cracks. The concentration of F- in the surface water are high, and the water quality is slightly lower than that of the Class III water standard; however, the groundwater is freshwater with low salinity and hardness. Cd and Tl are locally significant in the surface soil of the study area. N, P, CaCO3, Org, and B are deficient in the soil, whereas the K content is mainly medium. A part of the soil is polluted by heavy metals, particularly As, followed by Cd. However, the mangrove leaves are rich in nutrients, indicating that the lack of soil nutrient elements and heavy-metal pollution are not the major factors limiting the growth of mangroves in this region. Improving the growth environment of mangroves should include expanding the landscape area and reinforcing reserve management. In the rock-soil-plant ecosystem, As, Pb, Cd, and B are significantly rich in the soil, indicating that their contents have a slight correlation with their parent rocks. Ni, Cu, Zn, and K are limited by the parent rock contents, and some elements including P, Mo, and Cr show enrichment capacity in the soil. The BCF >2 of mangroves of the mangrove forests in Dongchong are P and B, indicating that the mangrove trees have a higher absorption capacity for P and B. In contrast, the BCF values of As, Pb, and Cd are relatively low, which, in addition to the weak absorption capacity of the mangrove trees for these elements and combined with the geochemical characteristics of the soils in the study area, are also affected by the high Cd and As contents of the soil. All the data presented in this paper are from the project, "Ecological and Environmental Geological Survey of Shenzhen Nature Reserves," for which we express our sincere gratitude

An RDAU-NET model for lesion segmentation in breast ultrasound images.

Breast cancer is a common gynecological disease that poses a great threat to women health due to its high malignant rate. Breast cancer screening tests are used to find any warning signs or symptoms for early detection and currently, Ultrasound screening is the preferred method for breast cancer diagnosis. The localization and segmentation of the lesions in breast ultrasound (BUS) images are helpful for clinical diagnosis of the disease. In this paper, an RDAU-NET (Residual-Dilated-Attention-Gate-UNet) model is proposed and employed to segment the tumors in BUS images. The model is based on the conventional U-Net, but the plain neural units are replaced with residual units to enhance the edge information and overcome the network performance degradation problem associated with deep networks. To increase the receptive field and acquire more characteristic information, dilated convolutions were used to process the feature maps obtained from the encoder stages. The traditional cropping and copying between the encoder-decoder pipelines were replaced by the Attention Gate modules which enhanced the learning capabilities through suppression of background information. The model, when tested with BUS images with benign and malignant tumor presented excellent segmentation results as compared to other Deep Networks. A variety of quantitative indicators including Accuracy, Dice coefficient, AUC(Area-Under-Curve), Precision, Sensitivity, Specificity, Recall, F1score and M-IOU (Mean-Intersection-Over-Union) provided performances above 80%. The experimental results illustrate that the proposed RDAU-NET model can accurately segment breast lesions when compared to other deep learning models and thus has a good prospect for clinical diagnosis

The cap snatching of segmented negative sense rna viruses as a tool to map the transcription start sites of heterologous co-infecting viruses

Identification of the transcription start sites (TSSs) of a virus is of great importance to understand and dissect the mechanism of viral genome transcription but this often requires costly and laborious experiments. Many segmented negative-sense RNA viruses (sNSVs) cleave capped leader sequences from a large variety of mRNAs and use these cleaved leaders as primers for transcription in a conserved process called cap snatching. The recent developments in high-throughput sequencing have made it possible to determine most, if not all, of the capped RNAs snatched by a sNSV. Here, we show that rice stripe tenuivirus (RSV), a plant-infecting sNSV, co-infects Nicotiana benthamiana with two different begomoviruses and snatches capped leader sequences from their mRNAs. By determining the 5' termini of a single RSV mRNA with high-throughput sequencing, the 5' ends of almost all the mRNAs of the co-infecting begomoviruses could be identified and mapped on their genomes. The findings in this study provide support for the using of the cap snatching of sNSVs as a tool to map viral TSSs