PRTA:Joint extraction of medical nested entities and overlapping relation via parameter sharing progressive recognition and targeted assignment decoding scheme.

Nested entities and relationship extraction are two tasks for analysis of electronic medical records. However, most of existing medical information extraction models consider these tasks separately, resulting in a lack of consistency between them. In this paper, we propose a joint medical entity-relation extraction model with progressive recognition and targeted assignment (PRTA). Entities and relations share the information of sequence and word embedding layers in the joint decoding stage. They are trained simultaneously and realize information interaction by updating the shared parameters. Specifically, we design a compound triangle strategy for the nested entity recognition and an adaptive multi-space interactive strategy for relationship extraction. Then, we construct a parameter-shared information space based on semantic continuity to decode entities and relationships. Extensive experiments were conducted on the Private Liver Disease Dataset (PLDD) provided by Beijing Friendship Hospital of Capital Medical University and public datasets (NYT, ACE04 and ACE05). The results show that our method outperforms existing SOTA methods in most indicators, and effectively handles nested entities and overlapping relationships.

Facial augmented reality based on hierarchical optimization of similarity aspect graph.

BACKGROUND: The existing face matching method requires a point cloud to be drawn on the real face for registration, which results in low registration accuracy due to the irregular deformation of the patient's skin that makes the point cloud have many outlier points. METHODS: This work proposes a non-contact pose estimation method based on similarity aspect graph hierarchical optimization. The proposed method constructs a distance-weighted and triangular-constrained similarity measure to describe the similarity between views by automatically identifying the 2D and 3D feature points of the face. A mutual similarity clustering method is proposed to construct a hierarchical aspect graph with 3D pose as nodes. A Monte Carlo tree search strategy is used to search the hierarchical aspect graph for determining the optimal pose of the facial 3D model, so as to realize the accurate registration of the facial 3D model and the real face. RESULTS: The proposed method was used to conduct accuracy verification experiments on the phantoms and volunteers, which were compared with four advanced pose calibration methods. The proposed method obtained average fusion errors of 1.13 ± 0.20 mm and 0.92 ± 0.08 mm in head phantom and volunteer experiments, respectively, which exhibits the best fusion performance among all comparison methods. CONCLUSIONS: Our experiments proved the effectiveness of the proposed pose estimation method in facial augmented reality.

Embedding-Alignment Fusion-Based Graph Convolution Network With Mixed Learning Strategy for 4D Medical Image Reconstruction.

In recent years, 4D medical image involving structural and motion information of tissue has attracted increasing attention. The key to the 4D image reconstruction is to stack the 2D slices based on matching the aligned motion states. In this study, the distribution of the 2D slices with the different motion states is modeled as a manifold graph, and the reconstruction is turned to be the graph alignment. An embedding-alignment fusion-based graph convolution network (GCN) with a mixed-learning strategy is proposed to align the graphs. Herein, the embedding and alignment processes of graphs interact with each other to realize a precise alignment with retaining the manifold distribution. The mixed strategy of self- and semi-supervised learning makes the alignment sparse to avoid the mismatching caused by outliers in the graph. In the experiment, the proposed 4D reconstruction approach is validated on the different modalities including Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US). We evaluate the reconstruction accuracy and compare it with those of state-of-the-art methods. The experiment results demonstrate that our approach can reconstruct a more accurate 4D image.

3D ARCNN: An Asymmetric Residual CNN for False Positive Reduction in Pulmonary Nodule.

Lung cancer is with the highest morbidity and mortality, and detecting cancerous lesions early is essential for reducing mortality rates. Deep learning-based lung nodule detection techniques have shown better scalability than traditional methods. However, pulmonary nodule test results often include a number of false positive outcomes. In this paper, we present a novel asymmetric residual network called 3D ARCNN that leverages 3D features and spatial information of lung nodules to improve classification performance. The proposed framework uses an internally cascaded multi-level residual model for fine-grained learning of lung nodule features and multi-layer asymmetric convolution to address the problem of large neural network parameters and poor reproducibility. We evaluate the proposed framework on the LUNA16 dataset and achieve a high detection sensitivity of 91.6%, 92.7%, 93.2%, and 95.8% for 1, 2, 4, and 8 false positives per scan, respectively, with an average CPM index of 0.912. Quantitative and qualitative evaluations demonstrate the superior performance of our framework compared to existing methods. 3D ARCNN framework can effectively reduce the possibility of false positive lung nodules in the clinical.

Robot-assisted augmented reality surgical navigation based on optical tracking for mandibular reconstruction surgery.

PURPOSE: This work proposes a robot-assisted augmented reality (AR) surgical navigation system for mandibular reconstruction. The system accurately superimposes the preoperative osteotomy plan of the mandible and fibula into a real scene. It assists the doctor in osteotomy quickly and safely under the guidance of the robotic arm. METHODS: The proposed system mainly consists of two modules: the AR guidance module of the mandible and fibula and the robot navigation module. In the AR guidance module, we propose an AR calibration method based on the spatial registration of the image tracking marker to superimpose the virtual models of the mandible and fibula into the real scene. In the robot navigation module, the posture of the robotic arm is first calibrated under the tracking of the optical tracking system. The robotic arm can then be positioned at the planned osteotomy after the registration of the computed tomography image and the patient position. The combined guidance of AR and robotic arm can enhance the safety and precision of the surgery. RESULTS: The effectiveness of the proposed system was quantitatively assessed on cadavers. In the AR guidance module, osteotomies of the mandible and fibula achieved mean errors of 1.61 ± 0.62 and 1.08 ± 0.28 mm, respectively. The mean reconstruction error of the mandible was 1.36 ± 0.22 mm. In the AR-robot guidance module, the mean osteotomy errors of the mandible and fibula were 1.47 ± 0.46 and 0.98 ± 0.24 mm, respectively. The mean reconstruction error of the mandible was 1.20 ± 0.36 mm. CONCLUSIONS: The cadaveric experiments of 12 fibulas and six mandibles demonstrate the proposed system's effectiveness and potential clinical value in reconstructing the mandibular defect with a free fibular flap.

A joint learning method for incomplete and imbalanced data in electronic health record based on generative adversarial networks.

Electronic health records (EHR), present challenges of incomplete and imbalanced data in clinical predictions. Previous studies addressed these two issues with two-step separately, which caused the decrease in the performance of prediction tasks. In this paper, we propose a unified framework to simultaneously addresses the challenges of incomplete and imbalanced data in EHR. Based on the framework, we develop a model called Missing Value Imputation and Imbalanced Learning Generative Adversarial Network (MVIIL-GAN). We use MVIIL-GAN to perform joint learning on the imputation process of high missing rate data and the conditional generation process of EHR data. The joint learning is achieved by introducing two discriminators to distinguish the fake data from the generated data at sample-level and variable-level. MVIIL-GAN integrate the missing values imputation and data generation in one step, improving the consistency of parameter optimization and the performance of prediction tasks. We evaluate our framework using the public dataset MIMIC-IV with high missing rates data and imbalanced data. Experimental results show that MVIIL-GAN outperforms existing methods in prediction performance. The implementation of MVIIL-GAN can be found at https://github.com/Peroxidess/MVIIL-GAN.

Transplantation of Wnt3a-modified neural stem cells promotes neural regeneration and functional recovery after spinal cord injury via Wnt-Gli2 pathway.

Neural stem cell (NSCs) transplantation has great potential in the treatment of spinal cord injury (SCI). Previous studies have indicated that the Wnt pathway could regulate the expression of basic helix-loop-helix (bHLH) family factor Hes5 and Mash1 in NSCs, but not through the notch intracellular domain. This suggests that there are other signals involved in this process. The aim of this study was to investigate the role of Wnt-Gli2 pathway in the treatment of SCI by transplanting neural stem cells. NSCs were isolated from the striata of embryonic day 14 mice. Activation of the Wnt pathway was achieved using Wnt3a protein, while Gli2 was inhibited using Gli2-siRNA. Expression levels of Gli2 and bHLH factors were assessed using western blotting. NSCs proliferation was evaluated using CCK-8 assay, and neural differentiation was determined by immunofluorescence staining. Finally, the modified NSCs were transplanted into mice with SCI, and their effects were assessed using behavioral and histological tests. Our results demonstrated that Wnt3a promoted the expression of Mash1 through Gli2. Moreover, the expression of Ngn1 and Hes1 was up-regulated, while Hes5 was down-regulated. Wnt3a also promoted NSCs proliferation and neural differentiation through this signaling pathway. In vivo experiments showed that NSCs transplantation mediated by Wnt3a-Gli2 signaling increased the number of neurons and resulted in improved Basso Mouse Scale scores. In conclusion, our findings suggest that Gli2 plays a role in mediating the regulation of Wnt3a signaling on promoting NSCs proliferation and neural differentiation. This pathway is therefore important in NSCs-mediated SCI recovery.

Automatic literature screening using the PAJO deep-learning model for clinical practice guidelines.

BACKGROUND: Clinical practice guidelines (CPGs) are designed to assist doctors in clinical decision making. High-quality research articles are important for the development of good CPGs. Commonly used manual screening processes are time-consuming and labor-intensive. Artificial intelligence (AI)-based techniques have been widely used to analyze unstructured data, including texts and images. Currently, there are no effective/efficient AI-based systems for screening literature. Therefore, developing an effective method for automatic literature screening can provide significant advantages. METHODS: Using advanced AI techniques, we propose the Paper title, Abstract, and Journal (PAJO) model, which treats article screening as a classification problem. For training, articles appearing in the current CPGs are treated as positive samples. The others are treated as negative samples. Then, the features of the texts (e.g., titles and abstracts) and journal characteristics are fully utilized by the PAJO model using the pretrained bidirectional-encoder-representations-from-transformers (BERT) model. The resulting text and journal encoders, along with the attention mechanism, are integrated in the PAJO model to complete the task. RESULTS: We collected 89,940 articles from PubMed to construct a dataset related to neck pain. Extensive experiments show that the PAJO model surpasses the state-of-the-art baseline by 1.91% (F1 score) and 2.25% (area under the receiver operating characteristic curve). Its prediction performance was also evaluated with respect to subject-matter experts, proving that PAJO can successfully screen high-quality articles. CONCLUSIONS: The PAJO model provides an effective solution for automatic literature screening. It can screen high-quality articles on neck pain and significantly improve the efficiency of CPG development. The methodology of PAJO can also be easily extended to other diseases for literature screening.

CT2X-IRA: CT to x-ray image registration agent using domain-cross multi-scale-stride deep reinforcement learning.

Objective.In computer-assisted minimally invasive surgery, the intraoperative x-ray image is enhanced by overlapping it with a preoperative CT volume to improve visualization of vital anatomical structures. Therefore, accurate and robust 3D/2D registration of CT volume and x-ray image is highly desired in clinical practices. However, previous registration methods were prone to initial misalignments and struggled with local minima, leading to issues of low accuracy and vulnerability.Approach.To improve registration performance, we propose a novel CT/x-ray image registration agent (CT2X-IRA) within a task-driven deep reinforcement learning framework, which contains three key strategies: (1) a multi-scale-stride learning mechanism provides multi-scale feature representation and flexible action step size, establishing fast and globally optimal convergence of the registration task. (2) A domain adaptation module reduces the domain gap between the x-ray image and digitally reconstructed radiograph projected from the CT volume, decreasing the sensitivity and uncertainty of the similarity measurement. (3) A weighted reward function facilitates CT2X-IRA in searching for the optimal transformation parameters, improving the estimation accuracy of out-of-plane transformation parameters under large initial misalignments.Main results.We evaluate the proposed CT2X-IRA on both the public and private clinical datasets, achieving target registration errors of 2.13 mm and 2.33 mm with the computation time of 1.5 s and 1.1 s, respectively, showing an accurate and fast workflow for CT/x-ray image rigid registration.Significance.The proposed CT2X-IRA obtains the accurate and robust 3D/2D registration of CT and x-ray images, suggesting its potential significance in clinical applications.

Building a trustworthy AI differential diagnosis application for Crohn's disease and intestinal tuberculosis.

BACKGROUND: Differentiating between Crohn's disease (CD) and intestinal tuberculosis (ITB) with endoscopy is challenging. We aim to perform more accurate endoscopic diagnosis between CD and ITB by building a trustworthy AI differential diagnosis application. METHODS: A total of 1271 electronic health record (EHR) patients who had undergone colonoscopies at Peking Union Medical College Hospital (PUMCH) and were clinically diagnosed with CD (n = 875) or ITB (n = 396) were used in this study. We build a workflow to make diagnoses with EHRs and mine differential diagnosis features; this involves finetuning the pretrained language models, distilling them into a light and efficient TextCNN model, interpreting the neural network and selecting differential attribution features, and then adopting manual feature checking and carrying out debias training. RESULTS: The accuracy of debiased TextCNN on differential diagnosis between CD and ITB is 0.83 (CR F1: 0.87, ITB F1: 0.77), which is the best among the baselines. On the noisy validation set, its accuracy was 0.70 (CR F1: 0.87, ITB: 0.69), which was significantly higher than that of models without debias. We also find that the debiased model more easily mines the diagnostically significant features. The debiased TextCNN unearthed 39 diagnostic features in the form of phrases, 17 of which were key diagnostic features recognized by the guidelines. CONCLUSION: We build a trustworthy AI differential diagnosis application for differentiating between CD and ITB focusing on accuracy, interpretability and robustness. The classifiers perform well, and the features which had statistical significance were in agreement with clinical guidelines.

Multimodal learning on graphs for disease relation extraction.

Disease knowledge graphs have emerged as a powerful tool for artificial intelligence to connect, organize, and access diverse information about diseases. Relations between disease concepts are often distributed across multiple datasets, including unstructured plain text datasets and incomplete disease knowledge graphs. Extracting disease relations from multimodal data sources is thus crucial for constructing accurate and comprehensive disease knowledge graphs. We introduce REMAP, a multimodal approach for disease relation extraction. The REMAP machine learning approach jointly embeds a partial, incomplete knowledge graph and a medical language dataset into a compact latent vector space, aligning the multimodal embeddings for optimal disease relation extraction. Additionally, REMAP utilizes a decoupled model structure to enable inference in single-modal data, which can be applied under missing modality scenarios. We apply the REMAP approach to a disease knowledge graph with 96,913 relations and a text dataset of 1.24 million sentences. On a dataset annotated by human experts, REMAP improves language-based disease relation extraction by 10.0% (accuracy) and 17.2% (F1-score) by fusing disease knowledge graphs with language information. Furthermore, REMAP leverages text information to recommend new relationships in the knowledge graph, outperforming graph-based methods by 8.4% (accuracy) and 10.4% (F1-score). REMAP is a flexible multimodal approach for extracting disease relations by fusing structured knowledge and language information. This approach provides a powerful model to easily find, access, and evaluate relations between disease concepts.

USP7 promotes the osteoclast differentiation of CD14+ human peripheral blood monocytes in osteoporosis via HMGB1 deubiquitination.

Background: Abnormal osteoclast and osteoblast differentiation is an essential pathological process in osteoporosis. As an important deubiquitinase enzyme, ubiquitin-specific peptidase 7 (USP7) participates in various disease processes through posttranslational modification. However, the mechanism by which USP7 regulates osteoporosis remains unknown. Herein, we aimed to investigate whether USP7 regulates abnormal osteoclast differentiation in osteoporosis. Methods: The gene expression profiles of blood monocytes were preprocessed to analyze the differential expression of USP genes. CD14+ peripheral blood mononuclear cells (PBMCs) were isolated from whole blood collected from osteoporosis patients (OPs) and healthy donors (HDs), and the expression pattern of USP7 during the differentiation of CD14+ PBMCs into osteoclasts was detected by western blotting. The role of USP7 in the osteoclast differentiation of PBMCs treated with USP7 siRNA or exogenous rUSP7 was further investigated by the F-actin assay, TRAP staining and western blotting. Moreover, the interaction between high-mobility group protein 1 (HMGB1) and USP7 was investigated by coimmunoprecipitation, and the regulation of the USP7-HMGB1 axis in osteoclast differentiation was further verified. Osteoporosis in ovariectomized (OVX) mice was then studied using the USP7-specific inhibitor P5091 to identify the role of USP7 in osteoporosis. Results: The bioinformatic analyses and CD14+ PBMCs from osteoporosis patients confirmed that the upregulation of USP7 was associated with osteoporosis. USP7 positively regulates the osteoclast differentiation of CD14+ PBMCs in vitro. Mechanistically, USP7 promoted osteoclast formation by binding to and deubiquitination of HMGB1. In vivo, P5091 effectively attenuates bone loss in OVX mice. Conclusion: We demonstrate that USP7 promotes the differentiation of CD14+ PBMCs into osteoclasts via HMGB1 deubiquitination and that inhibition of USP7 effectively attenuates bone loss in osteoporosis in vivo.The translational potential of this article:The study reveals novel insights into the role of USP7 in the progression of osteoporosis and provides a new therapeutic target for the treatment of osteoporosis.

An alternately optimized generative adversarial network with texture and content constraints for deformable registration of 3D ultrasound images.

Objective.3D ultrasound non-rigid registration is significant for intraoperative motion compensation. Nevertheless, distorted textures in the registered image due to the poor image quality and low signal-to-noise ratio of ultrasound images reduce the accuracy and efficiency of the existing methods.Approach.A novel 3D ultrasound non-rigid registration objective function with texture and content constraints in both image space and multiscale feature space based on an unsupervised generative adversarial network based registration framework is proposed to eliminate distorted textures. A similarity metric in the image space is formulated based on combining self-structural constraint with intensity to strengthen the robustness to abnormal intensity change compared with common intensity-based metrics. The proposed framework takes two discriminators as feature extractors to formulate the texture and content similarity between the registered image and the fixed image in the multiscale feature space respectively. A distinctive alternating training strategy is established to jointly optimize the combination of various similarity loss functions to overcome the difficulty and instability of training convergence and balance the training of generator and discriminators.Main results.Compared with five registration methods, the proposed method is evaluated both with small and large deformations, and achieves the best registration accuracy with average target registration error of 1.089 mm and 2.139 mm in cases of small and large deformations, respectively. The performance on peak signal to noise ratio (PSNR) and structural similarity (SSIM) also proves the effective constraints on distorted textures of the proposed method (PSNR is 31.693 dB and SSIM is 0.9 in the case of small deformation; PSNR is 28.177 dB and SSIM is 0.853 in the case of large deformation).Significance.The proposed 3D ultrasound non-rigid registration method based on texture and content constraints with the distinctive alternating training strategy can eliminate the distorted textures with improving the registration accuracy.

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

A novel Anti-ROS osteoblast-specific delivery system for ankylosing spondylitis treatment via suppression of both inflammation and pathological new bone formation.

Ankylosing spondylitis (AS) is a common rheumatic disorder distinguished by chronic inflammation and heterotopic ossification at local entheses sites. Currently available medications, including nonsteroidal anti-inflammatory drugs (NSAIDs), disease-modifying anti-rheumatic drugs (DMARDs) and TNF inhibitors, are limited by side effects, high costs and unclear inhibitory effects on heterotopic ossification. Herein, we developed manganese ferrite nanoparticles modified by the aptamer CH6 (CH6-MF NPs) that can efficiently scavenge ROS and actively deliver siRNA into hMSCs and osteoblasts in vivo for effective AS treatment. CH6-MF NPs loaded with BMP2 siRNA (CH6-MF-Si NPs) effectively suppressed abnormal osteogenic differentiation under inflammatory conditions in vitro. During their circulation and passive accumulation in inflamed joints in the Zap70mut mouse model, CH6-MF-Si NPs attenuated local inflammation and rescued heterotopic ossification in the entheses. Thus, CH6-MF NPs may be an effective inflammation reliever and osteoblast-specific delivery system, and CH6-MF-Si NPs have potential for the dual treatment of chronic inflammation and heterotopic ossification in AS.

M-CSAFN: Multi-Color Space Adaptive Fusion Network for Automated Port-Wine Stains Segmentation.

Automatic segmentation of port-wine stains (PWS) from clinical images is critical for accurate diagnosis and objective assessment of PWS. However, this is a challenging task due to the color heterogeneity, low contrast, and indistinguishable appearance of PWS lesions. To address such challenges, we propose a novel multi-color space adaptive fusion network (M-CSAFN) for PWS segmentation. First, a multi-branch detection model is constructed based on six typical color spaces, which utilizes rich color texture information to highlight the difference between lesions and surrounding tissues. Second, an adaptive fusion strategy is used to fuse complementary predictions, which address the significant differences within the lesions caused by color heterogeneity. Third, a structural similarity loss with color information is proposed to measure the detail error between predicted lesions and truth lesions. Additionally, a PWS clinical dataset consisting of 1413 image pairs was established for the development and evaluation of PWS segmentation algorithms. To verify the effectiveness and superiority of the proposed method, we compared it with other state-of-the-art methods on our collected dataset and four publicly available skin lesion datasets (ISIC 2016, ISIC 2017, ISIC 2018, and PH2). The experimental results show that our method achieves remarkable performance in comparison with other state-of-the-art methods on our collected dataset, achieving 92.29% and 86.14% on Dice and Jaccard metrics, respectively. Comparative experiments on other datasets also confirmed the reliability and potential capability of M-CSAFN in skin lesion segmentation.

Adaptive tetrahedral interpolation for reconstruction of uneven freehand 3D ultrasound.

Objective.Freehand 3D ultrasound volume reconstruction has received considerable attention in medical research because it can freely perform spatial imaging at a low cost. However, the uneven distribution of the original ultrasound images in space reduces the reconstruction effect of the traditional method.Approach.An adaptive tetrahedral interpolation algorithm is proposed to reconstruct 3D ultrasound volume data. The algorithm adaptively divides the unevenly distributed images into numerous tetrahedrons and interpolates the voxel value in each tetrahedron to reconstruct 3D ultrasound volume data.Main results.Extensive experiments on simulated and clinical data confirm that the proposed method can achieve more accurate reconstruction than six benchmark methods. Specifically, the averaged interpolation error at the gray level can be reduced by 0.22-0.82, and the peak signal-to-noise ratio and the mean structure similarity can be improved by 0.32-1.83 dB and 0.01-0.05, respectively.Significance.With the parallel implementation of the algorithm, one 3D ultrasound volume data with size 279 × 279 × 276 can be reconstructed from 100 slices 2D ultrasound images with size 200 × 200 at 1.04 s. Such a quick and accurate approach has practical value in medical research.

OrbitNet-A fully automated orbit multi-organ segmentation model based on transformer in CT images.

The delineation of orbital organs is a vital step in orbital diseases diagnosis and preoperative planning. However, an accurate multi-organ segmentation is still a clinical problem which suffers from two limitations. First, the contrast of soft tissue is relatively low. It usually cannot clearly show the boundaries of organs. Second, the optic nerve and the rectus muscle are difficult to distinguish because they are spatially adjacent and have similar geometry. To address these challenges, we propose the OrbitNet model to automatically segment orbital organs in CT images. Specifically, we present a global feature extraction module based on the transformer architecture called FocusTrans encoder, which enhance the ability to extract boundary features. To make the network focus on the extraction of edge features in the optic nerve and rectus muscle, the SA block is used to replace the convolution block in the decoding stage. In addition, we use the structural similarity measure (SSIM) loss as a part of the hybrid loss function to learn the edge differences of the organs better. OrbitNet has been trained and tested on the CT dataset collected by the Eye Hospital of Wenzhou Medical University. The experimental results show that our proposed model achieved superior results. The average Dice Similarity Coefficient (DSC) is 83.9%, the value of average 95% Hausdorff Distance (HD95) is 1.62 mm, and the value of average Symmetric Surface Distance (ASSD) is 0.47 mm. Our model also has good performance on the MICCAI 2015 challenge dataset.

Current status of indoor dust PBDE pollution and its physical burden and health effects on children.

Polybrominated diphenyl ethers (PBDEs) are widely detected in indoor dust, which has been identified as a more important route of PBDE exposure for children than food intake. The physical burden and health hazards to children of PBDE exposure in house dust have not been adequately summarized; therefore, this article reviews the current status of PBDE pollution in indoor dust associated with children, highlighting the epidemiological evidence for physical burden and health risks in children. We find that PBDEs remain at high levels in indoor dust, including in homes, schools, and cars, especially in cars showing a significant upward trend. There is a trend towards an increase in the proportion of BDE-209 in household dust, which is indicative of recent PBDE contamination. Conversely, PBDE congeners in car and school indoor dust tended to shift from highly brominated to low brominated, suggesting a shift in current pollution patterns. Indoor dust exposure causes significantly higher PBDE burdens in children, especially infants in early life, than in adults. Exposure to dust also affects breast milk, putting infants at high risk of exposure. Although evidence is limited, available epidemiological studies suggest that exposure to indoor dust PBDEs promotes neurobehavioral problems and cancer development in children.

Automatic spatial calibration of freehand ultrasound probe with a multilayer N-wire phantom.

The classic N-wire phantom has been widely used in the calibration of freehand ultrasound probes. One of the main challenges of the phantom is accurately identifying N-fiducials in ultrasound images, especially with multiple N-wire structures. In this study, a method using a multilayer N-wire phantom for the automatic spatial calibration of ultrasound images is proposed. All dots in the ultrasound image are segmented, scored, and classified according to the unique geometric features of the multilayer N-wire phantom. A recognition method for identifying N-fiducials from the dots is proposed for calibrating the spatial transformation of the ultrasound probe. At depths of 9, 11, 13, and 15 cm, the reconstruction error of 50 points is 1.24 ± 0.16, 1.09 ± 0.06, 0.95 ± 0.08, 1.02 ± 0.05 mm, respectively. The reconstruction mockup test shows that the distance accuracy is 1.11 ± 0.82 mm at a depth of 15 cm.