Study on the Rural Revitalization and Urban-Rural Integration Efficiency in Anhui Province Based on Game Cross-Efficiency DEA Model.

By taking the 16 cities in Anhui Province for evaluation, the main influencing factors and indicator system for integrated urban-rural development in the new era were explored, to build the BCC model, cross-efficiency model, and game cross-efficiency model of DEA. The above models were applied for empirical analysis and comparative study on the rural revitalization and urban-rural integration efficiency in Anhui Province, to summarize the conclusions efficiency and give suggestions based on the above calculations.

Pyramid Convolutional RNN for MRI Image Reconstruction.

Fast and accurate MRI image reconstruction from undersampled data is crucial in clinical practice. Deep learning based reconstruction methods have shown promising advances in recent years. However, recovering fine details from undersampled data is still challenging. In this paper, we introduce a novel deep learning based method, Pyramid Convolutional RNN (PC-RNN), to reconstruct images from multiple scales. Based on the formulation of MRI reconstruction as an inverse problem, we design the PC-RNN model with three convolutional RNN (ConvRNN) modules to iteratively learn the features in multiple scales. Each ConvRNN module reconstructs images at different scales and the reconstructed images are combined by a final CNN module in a pyramid fashion. The multi-scale ConvRNN modules learn a coarse-to-fine image reconstruction. Unlike other common reconstruction methods for parallel imaging, PC-RNN does not employ coil sensitive maps for multi-coil data and directly model the multiple coils as multi-channel inputs. The coil compression technique is applied to standardize data with various coil numbers, leading to more efficient training. We evaluate our model on the fastMRI knee and brain datasets and the results show that the proposed model outperforms other methods and can recover more details. The proposed method is one of the winner solutions in the 2019 fastMRI competition.

End-to-end multimodal image registration via reinforcement learning.

Multimodal image registration is a vital initial step in several medical image applications for providing complementary information from different data modalities. Since images with different modalities do not exhibit the same characteristics, finding their accurate correspondences remains a challenge. For convolutional multimodal registration methods, two components are quite significant: descriptive image feature as well as the suited similarity metric. However, these two components are often custom-designed and are infeasible to the high diversity of tissue appearance across modalities. In this paper, we translate image registration into a decision-making problem, where registration is achieved via an artificial agent trained by asynchronous reinforcement learning. More specifically, convolutional long-short-term-memory is incorporated after stacked convolutional layers in this method to extract spatial-temporal image features and learn the similarity metric implicitly. A customized reward function driven by landmark error is advocated to guide the agent to the correct registration direction. A Monte Carlo rollout strategy is also leveraged to perform as a look-ahead inference in the testing stage, to increase registration accuracy further. Experiments on paired CT and MR images of patients diagnosed as nasopharyngeal carcinoma demonstrate that our method achieves state-of-the-art performance in medical image registration.

Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging.

Humans perceive physical properties such as motion and elastic force by observing objects in visual scenes. Recent research has proven that computers are capable of inferring physical properties from camera images like humans. However, few studies perceive the physical properties in more complex environment, i.e. humans have difficulty estimating physical quantities directly from the visual observation, or encounter difficulty visualizing the physical process in mind according to their daily experiences. As an appropriate example, fractional flow reserve (FFR), which measures the blood pressure difference across the vessel stenosis, becomes an important physical quantitative value determining the likelihood of myocardial ischemia in clinical coronary intervention procedure. In this study, we propose a novel deep neural network solution (TreeVes-Net) that allows machines to perceive FFR values directly from static coronary CT angiography images. Our framework fully utilizes a tree-structured recurrent neural network (RNN) with a coronary representation encoder. The encoder captures coronary geometric information providing the blood fluid-related representation. The tree-structured RNN builds a long-distance spatial dependency of blood flow information inside the coronary tree. The experiments performed on 13000 synthetic coronary trees and 180 real coronary trees from clinical patients show that the values of the area under ROC curve (AUC) are 0.92 and 0.93 under two clinical criterions. These results can demonstrate the effectiveness of our framework and its superiority to seven FFR computation methods based on machine learning.

Application of CPI cutoff value based on parentage testing of duos and trios typed by four autosomal kits.

In this study, we analyzed the application of four autosomal kits and the sensitivity of the combined paternity index (CPI) cutoff value (CPI≥10000) in parentage testing. First, 1442 real trios and 803 real duos were tested using the Goldeneye 25A kit. The Goldeneye 25A kit covers the autosomal short tandem repeat (STR) loci of the other three kits, so we calculated the CPI value of every case for the four kits. Second, three complex close relative kinship cases were also analyzed to evaluate the application of the CPI cutoff value. The CPI values of all trio cases were higher than 10000 using the four kits; the CPI values of all duo cases were higher than 10000 using the Goldeneye 25A kit; and the CPI values of a portion of the duo cases were lower than 10000 using the other three kits. In the three complex close relative cases, the alleged father or mother was not excluded using 40 autosomal STRs. Adding X chromosome short tandem repeats (X-STR) and samples of biological fathers or mothers, the conclusions were confirmed. The four kits were adequate to draw conclusions in the trio cases; the Goldeneye 25A Kit was adequate to draw conclusions in the duo cases; and the other three kits were not sufficient for a portion of the duo cases. The CPI cutoff value was sensitive for real trio and duo cases. In complex close relative kinship cases, high CPI values may result in false conclusions.

Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning.

Multi-modality examinations have been extensively applied in current clinical cancer management. Leveraging multi-modality medical images can be highly beneficial for automated tumor segmentation as they provide complementary information that could make the segmentation of tumors more accurate. This paper investigates CNN-based methods for automated nasopharyngeal carcinoma (NPC) segmentation using computed tomography (CT) and magnetic resonance (MR) images. Specially, a multi-modality convolutional neural network (M-CNN) is designed to jointly learn a multi-modal similarity metric and segmentation of paired CT-MR images. By jointly optimizing the similarity learning error and the segmentation error, the feature learning processes of both modalities are mutually guided. In doing so, the segmentation sub-networks are able to take advantage of the other modality's information. Considering that each modality possesses certain distinctive characteristics, we combine the higher-layer features extracted by a single-modality CNN (S-CNN) and M-CNN to form a combined CNN (C-CNN) for each modality, which is able to further utilize the complementary information of different modalities and improve the segmentation performance. The proposed M-CNN and C-CNN were evaluated on 90 CT-MR images of NPC patients. Experimental results demonstrate that our methods achieve improved segmentation performance compared to their counterparts without multi-modal information fusion and the existing CNN-based multi-modality segmentation methods.

Vessel tree tracking in angiographic sequences.

We present a method to track vessels in angiography [contrast filled vessels in two-dimensional (2-D) x-ray fluoroscopy]. Finding correspondence of a vessel tree from consecutive angiogram frames provides significant value in computer-aided clinical applications such as fast vessel tree segmentation, three-dimensional (3-D) vessel topology reconstruction from corresponding centerlines, cardiac motion understanding, etc. However, establishing an accurate vessel tree correspondence (vessel tree tracking) is a nontrivial problem due to nonlinear periodic cardiac and breathing motion in 2-D views, foreshortening, false bifurcations due to 3-D to 2-D projection, occlusion from other anatomies, etc. The vessel tree is represented by BSpline curves. The control points of the BSpline curves are landmarks that are the tracking targets. Our method maximizes the appearance similarity while preserving the vessel structure. A directed acyclic graph (DAG) is employed to represent the appearance and shape structure of the vessel tree: nodes from the DAG encode the appearance of the vessel tree landmarks, and the edges encode the relative locations between landmarks. The vessel tree tracking problem turns into finding the most similar tree from the DAG in the next frame, and it is solved using an efficient dynamic programming algorithm. We performed evaluations on 62 x-ray angiography sequences (above 1000 frames). Experiment results show our algorithm is robust to these challenges and delivers better performance, compared to four existing methods.

Automatic Tissue Differentiation Based on Confocal Endomicroscopic Images for Intraoperative Guidance in Neurosurgery.

Diagnosis of tumor and definition of tumor borders intraoperatively using fast histopathology is often not sufficiently informative primarily due to tissue architecture alteration during sample preparation step. Confocal laser microscopy (CLE) provides microscopic information of tissue in real-time on cellular and subcellular levels, where tissue characterization is possible. One major challenge is to categorize these images reliably during the surgery as quickly as possible. To address this, we propose an automated tissue differentiation algorithm based on the machine learning concept. During a training phase, a large number of image frames with known tissue types are analyzed and the most discriminant image-based signatures for various tissue types are identified. During the procedure, the algorithm uses the learnt image features to assign a proper tissue type to the acquired image frame. We have verified this method on the example of two types of brain tumors: glioblastoma and meningioma. The algorithm was trained using 117 image sequences containing over 27 thousand images captured from more than 20 patients. We achieved an average cross validation accuracy of better than 83%. We believe this algorithm could be a useful component to an intraoperative pathology system for guiding the resection procedure based on cellular level information.

Employment of 4-(1,2,4-triazol-1-yl)phenol as a signal enhancer of the chemiluminescent luminol-H2O2-horseradish peroxidase reaction for detection of hepatitis C virus in real samples.

Highly sensitive detection of hepatitis C virus (HCV) in serum is a key method for diagnosing and classifying the extent of HCV infection. In this study, a p-phenol derivative, 4-(1,2,4-triazol-1-yl)phenol (4-TRP), was employed as an efficient enhancer of the luminol-hydrogen peroxide (H2O2)-horseradish peroxidase (HRP) chemiluminescence (CL) system for detection of HCV. Compared with a traditional enhancer, 4-TRP strongly enhanced CL intensity with the effect of prolonging and stabilizing light emission. The developed CL system was applied to detecting HCV core antigen (HCV-cAg) using a sandwich structure inside microwells. Our experimental results showed that there was good linear relationship between CL intensity and HCV-cAg concentration in the 0.6-3.6 pg/mL range (R = 0.99). The intra- and inter-assay coefficients of variation were 4.5-5.8% and 5.0-7.3%, respectively. In addition, sensitive determination of HCV-cAg in serum samples using the luminol-H2O2-HRP-4-TRP CL system was also feasible in clinical settings.

Model-guided extraction of coronary vessel structures in 2D X-ray angiograms.

Analysis of vessel structures in 2D X-ray angiograms is important for pre-operative evaluation and image-guided intervention. However, automated vessel segmentation in angiograms, especially extraction of the topology such as bifurcations and vessel crossings, remains challenging mainly due to the projective nature of angiography and background clutter. In this paper, a novel framework for model-guided coronary vessel extraction in 2D angiograms is presented. In this framework, a graph is constructed using a sparse set of pixels in the angiogram. With a single user-supplied click as the starting point, the vessel tree structure in the angiogram is automatically extracted from the graph. Ambiguities in this tree structure caused by 3D-to-2D projection are then resolved using topological information from the 3D vessel model of the same patient. By incorporating this prior shape information, the proposed method is effective in extraction of vessel topology, and is robust to background clutter and uneven illumination. Through quantitative evaluation on 20 angiograms, it is shown that this model-guided approach significantly improves detection of vessel structures and bifurcations.

Graph-based IVUS segmentation with efficient computer-aided refinement.

A new graph-based approach for segmentation of luminal and external elastic lamina (EEL) surface of coronary vessels in gated 20 MHz intravascular ultrasound (IVUS) image sequences (volumes) is presented. The approach consists of a fully automated segmentation stage ("new automated" or NA) and a user-guided computer-aided refinement ("new refinement" or NR) stage. Both approaches are based on the LOGISMOS approach for simultaneous dual-surface graph-based segmentation. This combination allows the user to efficiently combine general information about IVUS image appearance and case-specific IVUS morphology and therefore deal with frequently occurring issues like calcified plaque-causing signal shadowing-and imaging artifacts. The automated segmentation stage starts with pre-segmenting the lumen to automatically define the lumen centerline, which is used to transform the segmentation task into a LOGISMOS-family graph optimization problem. Following the automated segmentation, the user can inspect the result and correct local or regional segmentation inaccuracies by (iteratively) providing approximate clues regarding the location of the desired surface locations. This expert information is utilized to modify the previously calculated cost functions, locally re-optimizing the underlying modified graph without a need to start the new optimization from scratch. Validation of our method was performed on 41 gated 20 MHz IVUS data sets for which an expert-defined independent standard was available. Resulting from the automated stage of the approach (NA), the mean and standard deviation of the root mean square area errors for the luminal and external elastic lamina surfaces were 1.12 ±0.67 mm (2) and 2.35 ±1.61 mm (2) , respectively. Following the refinement stage (NR), the root mean square area errors significantly decreased to 0.82 ±0.44 mm (2) and 1.17 ±0.65 mm (2) for the same surfaces, respectively ( for both surfaces). The approach is delivering a previously unachievable speed of obtaining clinically relevant segmentations compared to the current approaches of automated segmentation followed by manual editing.

Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface.

Recently, the optimal surface finding (OSF) and layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) approaches have been reported with applications to medical image segmentation tasks. While providing high levels of performance, these approaches may locally fail in the presence of pathology or other local challenges. Due to the image data variability, finding a suitable cost function that would be applicable to all image locations may not be feasible. This paper presents a new interactive refinement approach for correcting local segmentation errors in the automated OSF-based segmentation. A hybrid desktop/virtual reality user interface was developed for efficient interaction with the segmentations utilizing state-of-the-art stereoscopic visualization technology and advanced interaction techniques. The user interface allows a natural and interactive manipulation of 3-D surfaces. The approach was evaluated on 30 test cases from 18 CT lung datasets, which showed local segmentation errors after employing an automated OSF-based lung segmentation. The performed experiments exhibited significant increase in performance in terms of mean absolute surface distance errors (2.54±0.75 mm prior to refinement vs. 1.11±0.43 mm post-refinement, p≪0.001). Speed of the interactions is one of the most important aspects leading to the acceptance or rejection of the approach by users expecting real-time interaction experience. The average algorithm computing time per refinement iteration was 150 ms, and the average total user interaction time required for reaching complete operator satisfaction was about 2 min per case. This time was mostly spent on human-controlled manipulation of the object to identify whether additional refinement was necessary and to approve the final segmentation result. The reported principle is generally applicable to segmentation problems beyond lung segmentation in CT scans as long as the underlying segmentation utilizes the OSF framework. The two reported segmentation refinement tools were optimized for lung segmentation and might need some adaptation for other application domains.

Expression of AFP and STAT3 is involved in arsenic trioxide-induced apoptosis and inhibition of proliferation in AFP-producing gastric cancer cells.

Alpha-fetoprotein (AFP)-producing gastric cancer (AFPGC), represented by the production of AFP, has a more aggressive behavior than common gastric cancer. The underlying mechanisms are not well understood. Arsenic trioxide (As(2)O(3)) is used clinically to treat acute promyelocytic leukemia(APL) and has activity in vitro against several solid tumor cell lines, with induction of apoptosis and inhibition of proliferation the prime effects. Signal transducer and activator of transcription 3 (STAT3) has an important role in tumorigenesis of various primary cancers and cancer cell by upregulating cell-survival and downregulating tumor suppressor proteins. Here, we found decreased expression of AFP and STAT3 after induction of apoptosis by As(2)O(3) in the AFPGC FU97 cells. Also, the level of the STAT3 target oncogene Bcl-2 was decreased with As(2)O(3), and that of the tumor suppressor Bax was increased. Furthermore, STAT3 expression and depth of invasion and lymph node metastasis were associated. Survival of patients with gastric cancer was lower with AFP and STAT3 double overexpression than with overexpression of either alone. Downregulation of AFP and STAT3 expression plays an important role in As(2)O(3)-induced apoptosis of AFPGC cells, which suggests a new mechanism of As(2)O(3)-induced cell apoptosis. As(2)O(3) may be a possible agent for AFPGC treatment.

[Genetic polymorphisms of 19 STR loci in Shandong Han population].

OBJECTIVE: To investigate the genetic polymorphisms of 19 STR Loci in Shandong Han population in order to provide the genetic data for paternity testing. METHODS: The genotypes of 205 unrelated individuals in Shandong Han population were typed by Goldeneye 20A kit to get the allele frequencies and population genetic parameters of 19 STR loci. Four kits, Identifiler kit, SinoFiler kit, PowerPlex 16 kit, and Goldeneye 20A kit, were compared with each other and used in the analysis of a special paternity test case. RESULTS: The population genetic parameters of 19 STR loci in Shandong Han Population were obtained. The cumulative discrimination power (CDP) and cumulative probability of exclusion (CPE) ranked from high to low were Goldeneye 20A kit, SinoFiler kit, PowerPlex 16 kit and Identifiler kit, respectively. As duo case, the result of the real case showed that Identifiler kit had no excluding loci, and none of the SinoFiler kit, PowerPlex 16 kit or Goldeneye 20A kit could exclude fatherhood. CONCLUSION: Compared with Identifiler kit, SinoFiler kit, and PowerPlex 16 kit, Goldeneye 20A kit shows the higher efficiency than the others, but is not completely satisfied for duo cases.

Automated measurement of uptake in cerebellum, liver, and aortic arch in full-body FDG PET/CT scans.

PURPOSE: The purpose of this work was to develop and validate fully automated methods for uptake measurement of cerebellum, liver, and aortic arch in full-body PET/CT scans. Such measurements are of interest in the context of uptake normalization for quantitative assessment of metabolic activity and/or automated image quality control. METHODS: Cerebellum, liver, and aortic arch regions were segmented with different automated approaches. Cerebella were segmented in PET volumes by means of a robust active shape model (ASM) based method. For liver segmentation, a largest possible hyperellipsoid was fitted to the liver in PET scans. The aortic arch was first segmented in CT images of a PET/CT scan by a tubular structure analysis approach, and the segmented result was then mapped to the corresponding PET scan. For each of the segmented structures, the average standardized uptake value (SUV) was calculated. To generate an independent reference standard for method validation, expert image analysts were asked to segment several cross sections of each of the three structures in 134 F-18 fluorodeoxyglucose (FDG) PET/CT scans. For each case, the true average SUV was estimated by utilizing statistical models and served as the independent reference standard. RESULTS: For automated aorta and liver SUV measurements, no statistically significant scale or shift differences were observed between automated results and the independent standard. In the case of the cerebellum, the scale and shift were not significantly different, if measured in the same cross sections that were utilized for generating the reference. In contrast, automated results were scaled 5% lower on average although not shifted, if FDG uptake was calculated from the whole segmented cerebellum volume. The estimated reduction in total SUV measurement error ranged between 54.7% and 99.2%, and the reduction was found to be statistically significant for cerebellum and aortic arch. CONCLUSIONS: With the proposed methods, the authors have demonstrated that automated SUV uptake measurements in cerebellum, liver, and aortic arch agree with expert-defined independent standards. The proposed methods were found to be accurate and showed less intra- and interobserver variability, compared to manual analysis. The approach provides an alternative to manual uptake quantification, which is time-consuming. Such an approach will be important for application of quantitative PET imaging to large scale clinical trials.

Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach.

Segmentation of lungs with (large) lung cancer regions is a nontrivial problem. We present a new fully automated approach for segmentation of lungs with such high-density pathologies. Our method consists of two main processing steps. First, a novel robust active shape model (RASM) matching method is utilized to roughly segment the outline of the lungs. The initial position of the RASM is found by means of a rib cage detection method. Second, an optimal surface finding approach is utilized to further adapt the initial segmentation result to the lung. Left and right lungs are segmented individually. An evaluation on 30 data sets with 40 abnormal (lung cancer) and 20 normal left/right lungs resulted in an average Dice coefficient of 0.975±0.006 and a mean absolute surface distance error of 0.84±0.23 mm, respectively. Experiments on the same 30 data sets showed that our methods delivered statistically significant better segmentation results, compared to two commercially available lung segmentation approaches. In addition, our RASM approach is generally applicable and suitable for large shape models.

Development of an oligochip for genotyping human leukocyte antigen-B51 and its clinical application.

Human leukocyte antigen (HLA) has been associated with Behcet's disease (BD), among which HLA-B51 is the most strongly associated genetic marker. The sandwich hybridization technique was applied in the design of the specific oligonucleotide probes to ensure the specific and accurate results. The probe-spotted chip was hybridized with the polymerase chain reaction (PCR) amplicons including nine suballeles (B*5101-B*5109) of exons 2 and 3 of HLA-B51 gene to determine the HLA-B51 genotypes. The results were subsequently confirmed by (PCR-SSP) and sequencing and were identical to those from polymerase chain reaction-sequencing specific primers (PCR-SSP) in 27 patients with BD and 30 healthy controls. This suggests that we successfully developed the oligochip for Behcet's-associated gene HLA-B51, which can effectively and accurately identify the HLA-B51 genotypes.

Cross-reaction of SARS-CoV antigen with autoantibodies in autoimmune diseases.

To investigate the significance of the SARS-associated coronavirus (SARS-CoV) antibody, detected by ELISA and indirect immunofluorescence assays (IFA) for the SARS-CoV Vero E6 cell lysates, in non-SARS subjects, 114 serum samples from healthy controls and 104 serum specimens from autoimmune disease patients were collected. The results of ELISA showed that among 114 sera from healthy controls, 4 (3.5%) were positive of SARS-CoV-IgG antibody and 114 (100%) were all negative of SARS-CoV-IgM antibody; the specificity of SARS-CoV-IgG antibody for SARS patients was 96.5%, but the specificity of both SARS-CoV-IgG and -IgM antibodies for SARS patients was 100%. In 58 cases with SLE, positive rates of SARS-CoV-IgG and -IgM antibodies were 32.8% (19/58) and 8.6% (5/58), respectively, in which 11 cases (19%) were positive of both SARS-CoV-IgG and -IgM antibodies; in 10 cases with SS, positive rate of both SARS-CoV-IgG and -IgM antibodies was 10% (1/10); in 16 cases with MCTD, positive rate of SARS-CoV-IgG was 37.5% (6/16), positive rate of both SARS-CoV-IgG and -IgM antibodies was 6.3% (1/16); in 20 cases with RA, one case was positive (5%) of SARS-CoV-IgG. However, of all samples with positive SARS-CoV-IgG and -IgM antibodies for autoimmune diseases and healthy controls, SARS-CoV RNA and antibodies were all negative by RT-PCR and IFA. All sera for negative or positive ELISA results were also negative or positive results using ELISA with Vero E6 cells lysates. These studies showed that SARS-CoV Vero E6 cell lysates for the ELISA to detect SARS-CoV antibodies could lead to the false-positive reactions or cross-reactions of SARS-CoV antibodies in non-SARS diseases and healthy controls, and the false-positive reactions or cross-reactions were related to Vero E6 cell lysates and autoantibodies in non-SARS population.