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Background and Purpose: In urological surgery, accurate localization of the ureteral orifice is crucial for procedures such as ureteral stent insertion, assessment of ureteral orifice lesions, and prostate tumor resection. Consequently, we have developed and validated a computer-assisted ureteral orifice detection system that combines the YOLO deep convolutional neural network and the attention mechanism. Data: The cases were partitioned into a training set and a validation set at a 4:1 ratio, with 84 cases comprising 820 images in the training set and 20 cases containing 223 images in the validation set. Method: We improved the YOLO network structure to accomplish the detection task. Based on the one-stage strategy, we replaced the backbone of YOLOv5 with a structure composed of ConvNeXt blocks. Additionally, we introduced GRN (Global Response Normalization) modules and SE blocks into the blocks to enhance deep feature diversity. In the feature enhancement section, we incorporated the BiFormer attention structure, which provides long-distance context dependencies without adding excessive computational costs. Finally, we improved the prediction box loss function to WIoU (Wise-IoU), enhancing the accuracy of the prediction boxes. Results: Testing on 223 cystoscopy images demonstrated a precision of 0.928 and recall of 0.756 for our proposed ureteral orifice detection network. With an overlap threshold of 0.5, the mAP of our proposed image detection system reached 0.896. The entire model achieved a single-frame detection speed of 5.7 ms on the platform, with a frame rate of 175FPS. Conclusion: We have enhanced a deep learning framework based on the one-stage YOLO strategy, suitable for real-time detection of the ureteral orifice in endoscopic scenarios. The system simultaneously maintains high accuracy and good real-time performance. This method holds substantial potential as an excellent learning and feedback system for trainees and new urologists in clinical settings.

With the development of wireless communication technology, the number of devices accessing the communication network is increasing. This paper addresses critical issues such as low spectrum resource utilization and the energy constraints of devices. The investigation focuses on the system performance of the shared relay Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) network based on Simultaneous Wireless Information and Power Transfer (SWIPT) network model. Unlike the existing CR-NOMA model in which the secondary network user do not participate in the transmission of information from the primary network, we consider the secondary network near user as shared relay. The shared relay utilizes SWIPT technology to harvest energy using a nonlinear energy harvesting model. Additionally, the shared relay assists in transmitting information from the primary network base station to primary user, as well as from the secondary network base station to far user of the secondary network. Subsequently, we conduct a series of simulations to analyze the effects of Signal-to-Noise Ratio (SNR), power distribution factor, interference threshold, and time-switching (TS) factor on system performance. Furthermore, we compare and analyze the performance of our proposed network model against CR-NOMA network across three dimensions: outage probability, throughput, and energy efficiency. Our results demonstrate that the proposed network model exhibits superior outage performance and enhances user throughput compared to the CR-NOMA network. Additionally, it demonstrates improved energy efficiency compared to the shared relay CR-NOMA network, leading to an overall improvement in network performance. [ABSTRACT FROM AUTHOR]

Background: Diabetic kidney disease (DKD) is an intricate complication of diabetes with limited treatment options. Anoikis, a programmed cell death activated by loss of cell anchorage to the extracellular matrix, participated in various physiological and pathological processes. Our study aimed to elucidate the role of anoikis-related genes in DKD pathogenesis. Methods: Differentially expressed genes (DEGs) associated with anoikis in DKD were identified. Weighted gene co-expression network analysis (WGCNA) was conducted to identify DKD-correlated modules and assess their functional implications. A diagnostic model for DKD was developed using LASSO regression and Gene set variation analysis (GSVA) was performed for enrichment analysis. Experimental validation was employed to validate the significance of selected genes in the progression of DKD. Results: We identified 10 anoikis-related DEGs involved in key signaling pathways impacting DKD progression. WGCNA highlighted the yellow module's significant enrichment in immune response and regulatory pathways. Correlation analysis further revealed the association between immune infiltration and anoikis-related DEGs. Our LASSO regression-based diagnostic model demonstrated a well-predictive efficacy with seven identified genes. GSVA indicated that gene function in the high-risk group was primarily associated with immune regulation. Further experimental validation using diabetic mouse models and data analysis in the single-cell dataset confirmed the significance of PYCARD and SFN in DKD progression. High glucose stimulation in RAW264.7 and TCMK-1 cells showed significantly increased expression levels of both Pycard and Sfn. Co-expression analysis demonstrated distinct functions of PYCARD and SFN, with KEGG pathway analysis showing significant enrichment in immune regulation and cell proliferation pathway. Conclusion: In conclusion, our study provides valuable insights into the molecular mechanisms involved in DKD pathogenesis, specifically highlighting the role of anoikis-related genes in modulating immune infiltration. These findings suggest that targeting these genes may hold promise for future diagnostic and therapeutic approaches in DKD management. [ABSTRACT FROM AUTHOR]

Purpose: To introduce the diffusion signal characteristics presented by spherical harmonics (SH) basis into the q‐space imaging method based on Gaussian radial basis function (GRBF) to robustly reconstruct ensemble average diffusion propagator (EAP) in diffusion MRI (dMRI). Methods: We introduced the Laplacian regularization of the signal into the dMRI imaging method based on GRBF, and derived the relevant indicators of microstructure imaging and the orientation distribution function (ODF) providing fiber bundle direction information based on EAP. In addition, this method is combined with a multi‐compartment model to calculate the diameter of fiber bundle axons. The evaluation of the results included qualitative comparisons and quantitative assessments of the signal fitting. Results: The results show that the proposed method achieves the more significant accuracy improvement in reconstructing signal. Meanwhile, ODFs estimated by the proposed method show the sharper profiles and less spurious peaks, even under the sparse and noisy conditions. In the 36 sets of axon diameter estimation experiments, 34 and 30 sets of results showed that the proposed method reduced the mean and SD of axon diameter estimates, respectively. Moreover, compared with the current state‐of‐the‐art method, the mean and SD of axon diameter estimated by the proposed method are mostly lower, with 32 and 29 of 36 groups. Conclusion: The proposed method outperforms the GRBF regarding signal fitting and the estimation of the EAP and ODF with multi‐shell sparse samples. Moreover, it shows the potential to recover important features of microstructures with less uncertainty by using proposed method together with multi‐compartment models. [ABSTRACT FROM AUTHOR]

Natural fractures play a significant role in oil and gas reservoirs. Accurate predictions of fracture parameters are vital in reservoir prediction and oil and gas development. The birefringent phenomenon of shear waves in fractured media makes shear wave splitting (SWS) analysis an important tool in formulating fracture predictions. The traditional SWS analysis method is based on an orthogonal assumption of fast and slow shear waves. However, in an orthotropic medium composed of a background vertical transversely isotropic medium and a set of vertical fractures, fast and slow shear waves are not necessarily orthogonal. This causes the traditional SWS analysis method to fail. To solve this problem, we proposed an SWS analysis algorithm with a non-orthogonal assumption of fast and slow shear waves in this study. First, we introduced a parameter (difference angle) to characterize the angle between slow shear waves and the normal polarization directions of the fast shear waves. Subsequently, based on the traditional two-parameter scanning algorithm, a parameter was added to facilitate three-parameter scanning. In addition, we derived an expression for the two-parameter scanning objective function using the non-orthogonal assumption. Two-parameter scanning can accurately extract fast and slow wave time delay data, but it cannot determine an accurate fast shear wave polarization direction. Therefore, we optimized the three-parameter scanning algorithm as follows: first, we used two-parameter scanning to obtain the fast and slow wave time delays and then performed further scanning to determine the polarization direction of the fast shear wave and difference angle. The optimization algorithm significantly improved the computational efficiency. Subsequently, we tested the accuracy of this method using synthetic single-trace and three-component vertical seismic profile data. We demonstrated the implementation process of the three-parameter scanning method using actual data, separated fast and slow shear waves, and predicted fracture parameters. The final fracture parameters were verified. [ABSTRACT FROM AUTHOR]

Lithium is an important mineral resource and a critical element in the production of lithium batteries, which are currently in high demand. Oilfield brine has significant value as a raw material for lithium extraction. However, it is often considered a byproduct of oil and gas production and is either abandoned or reinjected underground. Exploration and development of oilfield brines can enhance the economic benefits of oilfields and avoid wasting resources. Current methods for predicting brine distribution rely on geological genetic analysis, which results in low accuracy and reliability. To address this issue, we propose a workflow for lithium brine prediction that uses seismic and logging data. We introduced waveform clustering control and used the mapping relationship between seismic waveforms and well-logging curves to predict high-quality reservoirs based on the electrical and physical properties of lithium brine reservoirs. In this workflow, the seismic waveforms were first clustered using singular value decomposition. The sample sets of well-logging properties were established for the target location. The target properties were divided into high- and low-frequency components and predicted separately. The predicted results of the high-quality reservoirs in the study area were verified using elemental content test results to demonstrate the effectiveness of the method. Our study indicates that well-logging property prediction constrained by waveform clustering can predict lithium brines in a carbonate reservoir. [ABSTRACT FROM AUTHOR]

Accurate segmentation of brain gliomas (BG) is a crucial and challenging task for effective treatment planning in BG therapy. This study presents the weight loss function and dropout U‐Net with ConvNeXt block (WD‐UNeXt), which precisely segments BG from few shot MRI. The ConvNeXt block, which comprises the main body of the network, is a structure that can extract more detailed features from images. The weight loss function addresses the issue of category imbalance, thereby enhancing the network's ability to achieve more precise segmentation. The training set of BraTS2019 was used to train the network and apply it to test data. Dice similarity coefficient (DSC), sensitivity (Sen), specificity (Spec) and Hausdorff distance (HD) were used to assess the performance of the method. The experimental results demonstrate that the DSC of whole tumour, tumour core and enhancing tumour reached 0.934, 0.911 and 0.851, respectively. Sen of the sub‐regions achieved 0.922, 0.911 and 0.867. Spec and HD reached 1.000, 1.000, 1.000 and 3.224, 2.990, 2.844, respectively. Compared with the performance of state‐of‐the‐art methods, the DSC and HD of WD‐UNeXt were improved to varying degrees. Therefore, this method has considerable potential for the segmentation of BG. [ABSTRACT FROM AUTHOR]

The blast furnace tuyere is a key position in hot metal production and is primarily observed to assess the internal state of the furnace. However, detecting abnormal tuyere conditions has relied heavily on manual judgment, leading to certain limitations. We proposed a tuyere abnormality detection model based on knowledge distillation and a vision transformer (ViT) to address this issue. In this approach, ResNet50 is employed as the Teacher model to distill knowledge into the Student model, ViT. Firstly, we introduced spatial attention modules to enhance the model's perception and feature-extraction capabilities for different image regions. Furthermore, we simplified the depth of the ViT and improved its self-attention mechanism to alleviate training losses. In addition, we employed the knowledge distillation strategy to achieve model lightweighting and enhance the model's generalization capability. Finally, we evaluate the model's performance in tuyere abnormality detection and compare it with other classification methods such as VGG-19, ResNet-101, and ResNet-50. Experimental results showed that our model achieved a classification accuracy of 97.86% on a tuyere image dataset from a company, surpassing the original ViT model by 1.12% and the improved ViT model without knowledge distillation by 0.34%. Meanwhile, the model achieved a competitive classification accuracy of 90.31% and 77.65% on the classical fine-grained image datasets, Stanford Dogs and CUB-200-2011, respectively, comparable to other classification models. [ABSTRACT FROM AUTHOR]

BACKGROUND: Liver metastases is a pivotal factor of death in patients with colorectal cancer. The longitudinal data of colorectal liver metastases (CRLM) during treatment can monitor and reflect treatment efficacy and outcomes. OBJECTIVE: The objective of this study is to establish a radiomic model based on longitudinal magnetic resonance imaging (MRI) to predict chemotherapy response in patients with CRLM. METHODS: This study retrospectively enrolled longitudinal MRI data of five modalities on 100 patients. According to Response Evaluation Criteria in Solid Tumors (RECIST 1.1), 42 and 58 patients were identified as responders and non-responders, respectively. First, radiomic features were computed from different modalities of image data acquired pre-treatment and early-treatment, as well as their differences (Δ). Next, the features were screened by a two-sample t-test, max-relevance and min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO). Then, several ensemble radiomic models that integrate support vector machine (SVM), k-nearest neighbor (KNN), gradient boost decision tree (GBDT) and multi-layer perceptron (MLP) were established based on voting method to predict chemotherapy response. Data samples were divided into training and verification queues using a ratio of 8:2. Finally, we used the area under ROC curve (AUC) to evaluate model performance. RESULTS: Using the ensemble model developed using featue differences (Δ) computed from the longitudinal apparent diffusion coefficient (ADC) images, AUC is 0.9007±0.0436 for the training cohort. Applying to the testing cohort, AUC is 0.8958 and overall accuracy is 0.9. CONCLUSIONS: Study results demonstrate advantages and high performance of the ensemble radiomic model based on the radiomics feature difference of the longitudinal ADC images in predicting chemotherapy response, which has potential to assist treatment decision-making and improve clinical outcome. [ABSTRACT FROM AUTHOR]

Background: There is a shortage of clinicians with sufficient expertise in the diagnosis of Alzheimer's disease (AD), and cerebrospinal fluid biometric collection and positron emission tomography diagnosis are invasive. Therefore, it is of potential significance to obtain high-precision automatic diagnosis results from diffusion tensor imaging (DTI) through deep learning, and simultaneously output feature probability maps to provide clinical auxiliary diagnosis. Objective: We proposed a factorization machine combined neural network (FMCNN) model combining a multi-function convolutional neural network (MCNN) with a fully convolutional network (FCN), while accurately diagnosing AD and mild cognitive impairment (MCI); corresponding fiber bundle visualization results are generated to describe their status. Methods: First, the DTI data is preprocessed to eliminate the influence of external factors. The fiber bundles of the corpus callosum (CC), cingulum (CG), uncinate fasciculus (UNC), and white matter (WM) were then tracked based on deterministic fiber tracking. Then the streamlines are input into CNN, MCNN, and FMCNN as one-dimensional features for classification, and the models are evaluated by performance evaluation indicators. Finally, the fiber risk probability map is output through FMCNN. Results: After comparing the model performance indicators of CNN, MCNN, and FMCNN, it was found that FMCNN showed the best performance in the indicators of accuracy, specificity, sensitivity, and area under the curve. By inputting the fiber bundles of the 10 regions of interest (UNC_L, UNC_R, UNC, CC, CG, CG+UNC, CG+CC, CC+UNC, CG+CC+UNC, and WM into CNN, MCNN, and FMCNN, respectively), WM shows the highest accuracy in CNN, MCNN, and FMCNN, which are 88.41%, 92.07%, and 96.95%, respectively. Conclusion: The FMCNN proposed here can accurately diagnose AD and MCI, and the generated fiber probability map can represent the risk status of AD and MCI. [ABSTRACT FROM AUTHOR]

BACKGROUND: Pancreatic cancer is a highly lethal disease. The preoperative distinction between pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) remains a clinical challenge. OBJECTIVE: The goal of this study is to provide clinicians with supportive advice and avoid overtreatment by constructing a convolutional neural network (CNN) classifier to automatically identify pancreatic cancer using computed tomography (CT) images. METHODS: We construct a CNN model using a dataset of 6,173 CT images obtained from 107 pathologically confirmed pancreatic cancer patients at Shanghai Changhai Hospital from January 2017 to February 2022. We divide CT slices into three categories namely, SCN, MCN, and no tumor, to train the DenseNet201-based CNN model with multi-head spatial attention mechanism (MSAM-DenseNet201). The attention module enhances the network's attention to local features and effectively improves the network performance. The trained model is applied to process all CT image slices and finally realize the two categories classification of MCN and SCN patients through a joint voting strategy. RESULTS: Using a 10-fold cross validation method, this new MSAM-DenseNet201 model achieves a classification accuracy of 92.52%, a precision of 92.16%, a sensitivity of 92.16%, and a specificity of 92.86%, respectively. CONCLUSIONS: This study demonstrates the feasibility of using a deep learning network or classification model to help diagnose MCN and SCN cases. This, the new method has great potential for developing new computer-aided diagnosis systems and applying in future clinical practice. [ABSTRACT FROM AUTHOR]

In the various stages from life chemistry to biological evolution, major geological events are usually accompanied by the extinction of species and the gestation of new life, which are important factors that determine the direction of ecological evolution. The study of the terrane accretive orogeny during the Jinning–Chengjiang period has had a great impact on the crustal rock composition, the atmospheric environment and the water circulation system, etc. The living environment of the primitive biological community has undergone tremendous changes; for example, the crust produced a superimposed contraction and extensional rift effect, resulting in collisional magmatic activity and strong earthquakes, and a large number of species became extinct. In turn, biological activity and organic matter also contributed significantly to migration. Given the periodic cycle of geological processes, and being fully utilized by nature, the origin and evolution of life grow in a spiral. Based on the analysis of the petrochemical composition characteristics of metamorphic volcanic–sedimentary rock series and intermediate-acid intrusive rocks in Pengguan terrane, this paper proposes that the subduction mechanism of oceanic crust in the study area is formed by the NW subduction of the Longmenshan–Anning oceanic crust on the SE side. The relationship between the average change of chemical composition of the diabase in the Masongling and Xinkaidong areas and around the world, ion radius and compressibility, shows that when the element ion compressibility is less than 4.46 compared with the average world value, the oxide content of Si, Ti, Al and P is markedly lower, while when it is equal to or greater than 4.46, the oxide content of Fe, Mn, Mg and K increases, as opposed to that of Ca and Na. [ABSTRACT FROM AUTHOR]

PURPOSE: To investigate the value of a CT-based radiomics model in identification of Crohn's disease (CD) active phase and remission phase. METHODS: CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS: Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION: This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance. [ABSTRACT FROM AUTHOR]

In the present work, the molecular dynamics study method has been utilised to investigate the effects of pressure on the volatilisation of pure Bi nanoparticles and Bi–Fe core–shell nanoparticles during continuous heating. Computational total energy curves between 300 and 2000 K are used to explore the structural changes of nanoparticles under different pressures. On the other hand, the radial distribution function is examined to determine the structural evolution of a system. The calculated root mean square displacement curves under pressures of 0–5 GPa between 300 and 2000 K are employed to explore the diffusion of Bi element and the calculated volatilisation rate of Bi element is used to quantitatively obtain the volatilisation characteristics. The volatilisation patterns of pure Bi nanoparticles and Bi–Fe core–shell nanoparticles are different under different temperatures and pressures. The results show that the volatilisation of Bi element is increasing during the heating from 300 to 2000 K, and the volatilisation decreases with the increase of pressure. Furthermore, the Fe shell can effectively decrease the volatilisation of the Bi element. [ABSTRACT FROM AUTHOR]

Unlike the conventional seismic acquisition, simultaneous-source acquisition allows the overlap in the record, which can enhance acquisition efficiency and improve image quality. To further explore the advantage of simultaneous-source technology, a double blending survey is proposed, which contains the self-interference and cross-interference. In the designed survey, the self- and cross-interference are controlled by two factors (inline shot interval and source number). When the inline shot interval is larger than the efficient record length (ERL), the blending scheme is degraded to the cross simultaneous-source survey. When the source number is equal to one, the blending scheme is degraded to the self simultaneous-source survey. To suppress intense hybrid interference, the mixed regularization term integrating sparse constraint and rank reduction constraint is applied to provide stronger regularization ability. Compared with the individual penalty term, the mixed constraint can further suppress hybrid interference and obtain better deblending performance. Deblended results on synthetic and field data examples confirm the performance of mixed constraint and demonstrate that the designed hybrid simultaneous-source survey can further enhance the efficiency of acquisition. [ABSTRACT FROM AUTHOR]

Some studies have found that heart-type fatty acid-binding protein (H-FABP) is related to the prognosis of patients with sepsis. This study aimed to explore whether H-FABP could predict the 28-day mortality in patients with sepsis. Seven databases were searched, and the studies were screened based on the inclusion and exclusion criteria to assess the quality. The pooled sensitivity (SEN), specificity (SPE) positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC) of the summary receiver operating characteristic (SROC) curve were calculated along with the 95% confidence interval (CI) values. Deeks' funnel plot was used to ascertain any publication bias. Meta-regression analysis was performed to explore the possible sources of heterogeneity. Seven studies were assessed that included 822 patients with sepsis. The pooled SEN was 0.76 (95% CI, 0.71–0.81), SPE was 0.66 (95% CI, 0.61–0.70), PLR was 2.21 (95% CI, 1.73–2.83), NLR was 0.36 (95% CI, 0.29–0.54), DOR was 6.23 (95% CI, 4.27–9.11) and the pooled AUC was 0.8137. There was no publication bias. Race, literature language, sampling time, threshold division and threshold effect were not the causes for the large heterogeneity. This meta-analysis suggests that H-FABP has high accuracy in predicting the 28-day mortality rate of patients with sepsis. [ABSTRACT FROM AUTHOR]

Purpose: The shape and position of the inferior alveolar canal (IAC) are analyzed to effectively reduce the risk of iatrogenic injury based on cone‐beam computer tomography (CBCT). To assist dental clinicians to make better use of the IAC information, we propose an IAC segmentation method based on CBCT images. Methods: In this paper, CBCT images are first preprocessed by the Hounsfield unit values clipping and gray normalization. Secondly, based on the multi‐plane reconstruction (MPR) and curved surface reconstruction, the curved MPR image sets are generated by the smooth dental arch curve with a sampling distance of 1.00 pixels. Then, the K‐means clustering algorithm is used to cluster the texture parameters of the gray level‐gradient co‐occurrence matrix enhanced by the gradient directions to improve the image contrast of the IAC. Finally, the IAC edges are roughly segmented by the 2D line‐tracking method, and smoothed by the fourth‐order polynomial to obtain the final segmentation result. Results: Twenty‐one real clinical dental CBCT datasets were used to test the proposed method. The manual segmentation results of two specialized dental clinicians were used as quantitative evaluation criteria. The dice similarity index (DSI), average symmetric surface distance (ASSD), and mean curve distance (MCD) of the left IAC are 0.93 (SD = 0.01), 0.16 mm (SD = 0.05 mm), and 1.59 mm (SD = 0.25 mm), respectively; the DSI, ASSD, and MCD of the right IAC are 0.93 (SD = 0.02), 0.16 mm (SD = 0.05 mm), and 1.60 mm (SD = 0.30 mm), respectively. Conclusions: The proposed method provides an effective image enhancement and segmentation solution to analyze the shape and position of the IAC. Experimental results show that the relationships between the IAC and other structures can be accurately reflected in the panoramic images without superimposition and geometric distortion, and the smooth edges of the IAC can be segmented. [ABSTRACT FROM AUTHOR]

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor voxel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity. [ABSTRACT FROM AUTHOR]

BACKGROUND: Effective detection of Alzheimer's disease (AD) is still difficult in clinical practice. Therefore, establishment of AD detection model by means of machine learning is of great significance to assist AD diagnosis. OBJECTIVE: To investigate and test a new detection model aiming to help doctors diagnose AD more accurately. METHODS: Diffusion tensor images and the corresponding T1w images acquired from subjects (AD = 98, normal control (NC) = 100) are used to construct brain networks. Then, 9 types features (198×90×9 in total) are extracted from the 3D brain networks by a graph theory method. Features with low correction in both groups are selected through the Pearson correlation analysis. Finally, the selected features (198×33, 198×26, 198×30, 198×42, 198×36, 198×23, 198×29, 198×14, 198×25) are separately used into train 3 machine learning classifier based detection models in which 60% of study subjects are used for training, 20% for validation and 20% for testing. RESULTS: The best detection accuracy levels of 3 models are 90%, 98% and 90% with the corresponding sensitivity of 92%, 96%, and 72% and specificity of 88%, 100% and 94% when using a random forest classifier trained with the Shortest Path Length (SPL) features (198×14), a support vector machine trained with the Degree Centrality features (198×33), and a convolution neural network trained with SPL features, respectively. CONCLUSIONS: This study demonstrates that the new method and models not only improve the accuracy of detecting AD, but also avoid bias caused by the method of direct dimensionality reduction from high dimensional data. [ABSTRACT FROM AUTHOR]

Sparse representation (SR)-based denoising method attenuates random seismic noise trace-by-trace, which may not be able to employ the coherency between neighboring traces. To address this, we propose a multiple measurement vector (MMV)-based algorithm for robust denoising of multichannel seismic gathers. Seismic reflectors in common offset gather (COG) is characterized by horizontal events, which satisfy the common sparsity assumption, making it ideal for the MMV approach. ℓ2,1-norm regularization, which enforces two constraints on source matrix, i.e., temporal sparsity and the horizontal continuity, is then adopted in the MMV model to stabilize the lateral variation between channels. Besides, ℓ2,1-norm regularization provides a reasonable intrinsic structure to reduce the multisolution of the algorithm. Celebrating the strengths of iteratively reweighted (IR), we present a novel MMV algorithm, IR ℓ2,1 norm minimization (IR-ℓ2,1), that further improves the performance. The formulated IR-ℓ2,1 can be minimized by the alternating direction method of multipliers. Both synthetic and field data applications confirm the effectiveness of the proposed denoising method. [ABSTRACT FROM AUTHOR]