Advancing musculoskeletal tumor diagnosis: Automated segmentation and predictive classification using deep learning and radiomics.

OBJECTIVES: Musculoskeletal (MSK) tumors, given their high mortality rate and heterogeneity, necessitate precise examination and diagnosis to guide clinical treatment effectively. Magnetic resonance imaging (MRI) is pivotal in detecting MSK tumors, as it offers exceptional image contrast between bone and soft tissue. This study aims to enhance the speed of detection and the diagnostic accuracy of MSK tumors through automated segmentation and grading utilizing MRI. MATERIALS AND METHODS: The research included 170 patients (mean age, 58 years ±12 (standard deviation), 84 men) with MSK lesions, who underwent MRI scans from April 2021 to May 2023. We proposed a deep learning (DL) segmentation model MSAPN based on multi-scale attention and pixel-level reconstruction, and compared it with existing algorithms. Using MSAPN-segmented lesions to extract their radiomic features for the benign and malignant classification of tumors. RESULTS: Compared to the most advanced segmentation algorithms, MSAPN demonstrates better performance. The Dice similarity coefficients (DSC) are 0.871 and 0.815 in the testing set and independent validation set, respectively. The radiomics model for classifying benign and malignant lesions achieves an accuracy of 0.890. Moreover, there is no statistically significant difference between the radiomics model based on manual segmentation and MSAPN segmentation. CONCLUSION: This research contributes to the advancement of MSK tumor diagnosis through automated segmentation and predictive classification. The integration of DL algorithms and radiomics shows promising results, and the visualization analysis of feature maps enhances clinical interpretability.

Coronary vessel segmentation in coronary angiography with a multi-scale U-shaped transformer incorporating boundary aggregation and topology preservation.

Coronary vessel segmentation plays a pivotal role in automating the auxiliary diagnosis of coronary heart disease. The continuity and boundary accuracy of the segmented vessels directly affect the subsequent processing. Notably, during segmentation, vessels with severe stenosis can easily cause boundary errors and breakage, resulting in isolated islands. To address these issues, we propose a novel multi-scale U-shaped transformer with boundary aggregation and topology preservation (UT-BTNet) for coronary vessel segmentation in coronary angiography. Specifically, considering the characteristics of coronary vessels, we first develop the UT-BTNet for coronary vessels segmentation, which combines the advantages of a convolutional neural networks (CNN) and a transformer, and is able to effectively extract the local and global features of angiographic images. Secondly, we innovatively employ boundary loss and topological loss in two stages, in addition to the traditional losses. In the first stage, boundary loss is adopted, which has the effect of boundary aggregation. In the second stage, topological loss is applied to preserve the topology of the vessels, after the network converges. In the experiment, in addition to the two metrics of Dice and intersection over union (IoU), we specifically propose two metrics of boundary intersection over union (BIoU) and Betti error to evaluate boundary accuracy and the continuity of segmentation results. The results show that the Dice is 0.9291, the IoU is 0.8687, the BIoU is 0.5094, and the Betti error is 0.3400. Compared with the other state-of-the-art methods, UT-BTNet achieves better segmentation results, while ensuring the continuity and boundary accuracy of the vessels, indicating its potential clinical value.

Sigma-1 receptor regulates the endoplasmic reticulum stress pathway in the protective mechanism of dexmedetomidine against hyperoxia-induced lung injury.

Perioperative hyperoxia therapy is of great significance to save the lives of patients, but little is known about the possible mechanisms that induce hyperoxia-induced acute lung injury (HALI) and the measures for clinical prevention and treatment. In this experiment, the models were established with a feeding chamber with automatic regulation of oxygen concentration. The results showed that with the increase in inhaled oxygen concentration and the prolongation of exposure time, the severity of lung injury also increases significantly, reaching the diagnostic indication of HALI after 48 h of inhaling 95 % oxygen concentration. Subsequently, according to the dynamic changes of apoptosis in lung specimens, and the expression changes in Sig-1R-regulated ER stress pathway proteins (Sig-1R, GRP78, p-PERK, ATF6, IRE1, Caspase-12, ATF4, CHOP, Caspase-3 and p-JNK), it was confirmed that the Sig-1R-regulated ER stress signaling pathway was involved in the occurrence of HALI. To explore the preventive and therapeutic effects of routine clinical medication on HALI during the perioperative period, our research group selected dexmedetomidine (Dex) with lung protection. The experimental results revealed that Dex partially reversed the changes in the expression levels of Sig-1R-regulated ER stress pathway proteins. These results preliminarily confirmed that Dex may inhibit apoptosis induced by high oxygen concentration through the Sig-1R-regulated ER stress signaling pathway, thus playing a protective role in HALI.

A framework of myocardial bridge detection with x-ray angiography sequence.

BACKGROUND: Myocardial bridges are congenital anatomical abnormalities in which myocardium covers a segment of coronary arteries, leading to stenocardia, myocardial ischemia, and sudden cardiac death in severe cases. However, automatic diagnosis of myocardial bridge presents significant challenges. METHOD: A novel framework of myocardial bridge detection with x-ray angiography sequence is proposed, which can realize automatic detection of vessel stenosis and myocardial bridge. Firstly, we employ a novel neural network model for coronary vessel segmentation, which consists of both CNNs and transformer structures to effectively extract both local and global information of the vessels. Secondly, we describe the vessel segment information, establish the vessel tree in the image, and fuse the vessel tree information between sequences. Finally, based on vessel stenosis detection, we realize automatic detection of the myocardial bridge by querying the blood vessels between the image sequence information. RESULTS: In experiment, we evaluate the segmentation results using two metrics, Dice and ASD, and achieve scores of 0.917 and 1.39, respectively. In the stenosis detection, we achieve an average accuracy rate of 92.7% in stenosis detection among 262 stenoses. In multi-frame image processing, vessels in different frames can be well-matched, and the accuracy of myocardial bridge detection achieves 75%. CONCLUSIONS: Our experimental results demonstrate that the algorithm can automatically detect stenosis and myocardial bridge, providing a new idea for subsequent automatic diagnosis of coronary vessels.

LN-Net: Perfusion Pattern-Guided Deep Learning for Lymph Node Metastasis Diagnosis Based on Contrast-Enhanced Ultrasound Videos.

OBJECTIVE: The blood flow in lymph nodes reflects important pathological features. However, most intelligent diagnosis based on contrast-enhanced ultrasound (CEUS) video focuses only on CEUS images, ignoring the process of extracting blood flow information. In the work described here, a parametric imaging method for describing blood perfusion pattern was proposed and a multimodal network (LN-Net) to predict lymph node metastasis was designed. METHODS: First, the commercially available artificial intelligence object detection model YOLOv5 was improved to detect the lymph node region. Then the correlation and inflection point matching algorithms were combined to calculate the parameters of the perfusion pattern. Finally, the Inception-V3 architecture was used to extract the image features of each modality, with the blood perfusion pattern taken as the guiding factor in fusing the features with CEUS by sub-network weighting. DISCUSSION: The average precision of the improved YOLOv5s algorithm compared with baseline was improved by 5.8%. LN-Net predicted lymph node metastasis with 84.9% accuracy, 83.7% precision and 80.3% recall. Compared with the model without blood flow feature guidance, accuracy was improved by 2.6%. The intelligent diagnosis method has good clinical interpretability. CONCLUSION: A static parametric imaging map could describe a dynamic blood flow perfusion pattern, and as a guiding factor, it could improve the classification ability of the model with respect to lymph node metastasis.

Establishment and Evaluation of a Rat Model of Medial Malleolar Fracture with Vascular Injury.

OBJECTIVE: A stable animal model was needed to study bone non-union caused by insufficient blood supply, the main object of this paper is to develop a medial malleolar fracture model with controllable arterial vascular injuries in rats for revealing the biochemical mechanism of non-union by insufficient blood supply. METHODS: A total of 18 rats were randomly divided into three equal groups: the Sham group, the Fracture group, and the Fracture + Vascular group. The animals were subjected to unilateral medial malleolar bone fracture and vascular injury using customized molding equipment. The fracture site was scanned by micro-CT, and vascular injury was evaluated by laser Doppler flowmetry (LDF) 24 h after modeling. Histological examination (HE), alkaline phosphatase (ALP) and tartrate-resistant acid phosphatase (TRAP) staining, immunohistochemistry and immunofluorescence were conducted on the medial malleolar fracture tissues of three rats randomly selected from each group 24 h after modeling. Subsequently, to further confirm the success of fracture modeling, the fracture sites of three other rats in each group underwent micro-CT scanning again 6 weeks after surgery. RESULTS: The results of a 24 h micro-CT showed that all rats used to create the fracture models showed controlled injury of the medial malleolus. The model was stable, and the satisfaction of the homemade equipment agreed with the expectation. LDF showed that the blood flow of rats in the Fracture + Vascular group decreased significantly 24 h after fracture injury, while collateral blood flow perfusion increased by 50% on average. The results of HE, ALP and TRAP staining in the medial malleolus showed that the number of osteoblasts (OBs) and osteoclasts (OCs) in the Fracture group increased significantly, but the number of OBs and OCs in the Fracture + Vascular group decreased sharply relative to the number in the Sham group 24 h later. Furthermore, immunohistochemistry and immunofluorescence results showed that the number of neovessels in the Fracture group was significantly increased, while the number of neovessels in the Fracture + Vascular group was significantly decreased, which was consistent with the above results. After 6 weeks of modeling, the micro-CT results showed that the fractures in the Fracture group had healed substantially, while those in the Fracture + Vascular group had not. CONCLUSION: This study provided a reproducible and stable experimental animal model for medial malleolar fractures with arterial injury.

Segmentation of the cervical lesion region in colposcopic images based on deep learning.

Background: Colposcopy is an important method in the diagnosis of cervical lesions. However, experienced colposcopists are lacking at present, and the training cycle is long. Therefore, the artificial intelligence-based colposcopy-assisted examination has great prospects. In this paper, a cervical lesion segmentation model (CLS-Model) was proposed for cervical lesion region segmentation from colposcopic post-acetic-acid images and accurate segmentation results could provide a good foundation for further research on the classification of the lesion and the selection of biopsy site. Methods: First, the improved Faster Region-convolutional neural network (R-CNN) was used to obtain the cervical region without interference from other tissues or instruments. Afterward, a deep convolutional neural network (CLS-Net) was proposed, which used EfficientNet-B3 to extract the features of the cervical region and used the redesigned atrous spatial pyramid pooling (ASPP) module according to the size of the lesion region and the feature map after subsampling to capture multiscale features. We also used cross-layer feature fusion to achieve fine segmentation of the lesion region. Finally, the segmentation result was mapped to the original image. Results: Experiments showed that on 5455 LSIL+ (including cervical intraepithelial neoplasia and cervical cancer) colposcopic post-acetic-acid images, the accuracy, specificity, sensitivity, and dice coefficient of the proposed model were 93.04%, 96.00%, 74.78%, and 73.71%, respectively, which were all higher than those of the mainstream segmentation model. Conclusion: The CLS-Model proposed in this paper has good performance in the segmentation of cervical lesions in colposcopic post-acetic-acid images and can better assist colposcopists in improving the diagnostic level.

Experimental and numerical studies of the impact breakage of granite with high ejection velocities.

The impact-induced fragmentation of rock is widely and frequently encountered when natural hazards occur in mountainous areas. This type of fragmentation is an important and complex natural process that should be described. In this study, laboratory impact tests under different impact velocities were first conducted using a novel gas-driven rock impact apparatus. The three-dimensional digital image correlation (3D DIC) technique was used to monitor the dynamic fragmentation process upon impact. Then, coupled 3D finite-discrete element method (FDEM) numerical simulations were performed to numerically investigate the energy and damage evolutions and fragmentation characteristics of the sample under different impact velocities. The laboratory test results show that as the impact velocity increases, the failure pattern of the rock sample gradually changes from shear failure to splitting failure, and the fragmentation intensity increases obviously. The strain localization area gradually increases as the impact velocity increases and as the location gradually deviates away from the impacting face. In the numerical simulation, the proposed model is validated by quasi-static uniaxial compression tests and impact tests. The numerical simulations clearly show the progressive fracture process of the samples, which agrees well with the experimental observations. The evolutions of energy and damage variables were also derived based on the simulation results, which are markedly affected by the impact velocity. The fragment size distributions based on mass and number can be well fitted using a generalized extreme value law. Finally, the distribution of the fragment flying velocity and angle are analyzed.

Design of lung nodules segmentation and recognition algorithm based on deep learning.

BACKGROUND: Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation network and 3D ResNet50 classification network. The common convolutional layers in encoding and decoding paths of U-Net are replaced by residual units while the loss function is changed to Dice loss after using cross entropy loss to accelerate network convergence. Since the lung nodules are small and rich in 3D information, the ResNet50 is improved by replacing the 2D convolutional layers with 3D convolutional layers and reducing the sizes of some convolution kernels, 3D ResNet50 network is obtained for the diagnosis of benign and malignant lung nodules. RESULTS: 3D Res U-Net was trained and tested on 1044 CT subcases in the LIDC-IDRI database. The segmentation result shows that the Dice coefficient of 3D Res U-Net is above 0.8 for the segmentation of lung nodules larger than 10 mm in diameter. 3D ResNet50 was trained and tested on 2960 lung nodules in the LIDC-IDRI database. The classification result shows that the diagnostic accuracy of 3D ResNet50 is 87.3% and AUC is 0.907. CONCLUSION: The 3D Res U-Net module improves segmentation performance significantly with the comparison of 3D U-Net model based on residual learning mechanism. 3D Res U-Net can identify small nodules more effectively and improve its segmentation accuracy for large nodules. Compared with the original network, the classification performance of 3D ResNet50 is significantly improved, especially for small benign nodules.

Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously threatens people's health, with high morbidity and mortality worldwide. At present, the clinical diagnosis methods of COPD are time-consuming, invasive, and radioactive. Therefore, it is urgent to develop a non-invasive and rapid COPD severity diagnosis technique suitable for daily screening in clinical practice. RESULTS: This study established an effective model for the preliminary diagnosis of COPD severity using lung sounds with few channels. Firstly, the time-frequency-energy features of 12 channels lung sounds were extracted by Hilbert-Huang transform. And then, channels and features were screened by the reliefF algorithm. Finally, the feature sets were input into a support vector machine to diagnose COPD severity, and the performance with Bayes, decision tree, and deep belief network was compared. Experimental results show that high classification performance using only 4-channel lung sounds of L1, L2, L3, and L4 channels can be achieved by the proposed model. The accuracy, sensitivity, and specificity of mild COPD and moderate + severe COPD were 89.13%, 87.72%, and 91.01%, respectively. The classification performance rates of moderate COPD and severe COPD were 94.26%, 97.32%, and 89.93% for accuracy, sensitivity, and specificity, respectively. CONCLUSION: This model provides a standardized evaluation with high classification performance rates, which can assist doctors to complete the preliminary diagnosis of COPD severity immediately, and has important clinical significance.

Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle.

BACKGROUND: There is a high incidence of injury to the lateral ligament of the ankle in daily living and sports activities. The anterior talofibular ligament (ATFL) is the most frequent types of ankle injuries. It is of great clinical significance to achieve intelligent localization and injury evaluation of ATFL due to its vulnerability. METHODS: According to the specific characteristics of bones in different slices, the key slice was extracted by image segmentation and characteristic analysis. Then, the talus and fibula in the key slice were segmented by distance regularized level set evolution (DRLSE), and the curvature of their contour pixels was calculated to find useful feature points including the neck of talus, the inner edge of fibula, and the outer edge of fibula. ATFL area can be located using these feature points so as to quantify its first-order gray features and second-order texture features. Support vector machine (SVM) was performed for evaluation of ATFL injury. RESULTS: Data were collected retrospectively from 158 patients who underwent MRI, and were divided into normal (68) and tear (90) group. The positioning accuracy and Dice coefficient were used to measure the performance of ATFL localization, and the mean values are 87.7% and 77.1%, respectively, which is helpful for the following feature extraction. SVM gave a good prediction ability with accuracy of 93.8%, sensitivity of 88.9%, specificity of 100%, precision of 100%, and F1 score of 94.2% in the test set. CONCLUSION: Experimental results indicate that the proposed method is reliable in diagnosing ATFL injury. This study may provide a potentially viable method for aided clinical diagnoses of some ligament injury.

Berberine Attenuates Cerebral Ischemia-Reperfusion Injury Induced Neuronal Apoptosis by Down-Regulating the CNPY2 Signaling Pathway.

Berberine (BBR) has a neuroprotective effect against ischemic stroke, but its specific protective mechanism has not been clearly elaborated. This study explored the effect of BBR on the canopy FGF signaling regulator 2 (CNPY2) signaling pathway in the ischemic penumbra of rats. The model of cerebral ischemia-reperfusion injury (CIRI) was established by the thread embolization method, and BBR was gastrically perfused for 48 h or 24 h before operation and 6 h after operation. The rats were randomly divided into four groups: the Sham group, BBR group, CIRI group, and CIRI + BBR group. After 2 h of ischemia, followed by 24 h of reperfusion, we confirmed the neurologic dysfunction and apoptosis induced by CIRI in rats (p < 0.05). In the ischemic penumbra, the expression levels of CNPY2-regulated endoplasmic reticulum stress-induced apoptosis proteins (CNPY2, glucose-regulated protein 78 (GRP78), double-stranded RNA-activated protein kinase-like ER kinase (PERK), C/EBP homologous protein (CHOP), and Caspase-3) were significantly increased, but these levels were decreased after BBR treatment (p < 0.05). To further verify the inhibitory effect of BBR on CIRI-induced neuronal apoptosis, we added an endoplasmic reticulum-specific agonist and a PERK inhibitor to the treatment. BBR was shown to significantly inhibit the expression of apoptotic proteins induced by endoplasmic reticulum stress agonist, while the PERK inhibitor partially reversed the ability of BBR to inhibit apoptotic protein (p < 0.05). These results confirm that berberine may inhibit CIRI-induced neuronal apoptosis by downregulating the CNPY2 signaling pathway, thereby exerting a neuroprotective effect.

Study on quantitative detection of turbid solution components based on ellipse fitting.

It is of great significance to detect the components of turbid solutions using hyperspectral imaging technology in analytical chemistry. To solve the problems including complex computations and poor interpretations in previous researches, this study proposed a novel quantitative detection model based on contour extraction and ellipse fitting for turbid solutions. A wedge-shaped sample reservoir was firstly designed to increase the dimensions of light spot information. Subsequently, the visual features of the spot were extracted from their hyperspectral images using ellipse fitting. Partial least squares regression was performed for the concentrations of Intralipid-20% and the ellipse eigenvectors, and it gave a good prediction ability with the correlation coefficient (Rp) of 0.98 and the root-mean-square error (RMSEP) of 0.07%. Experimental results indicate that ellipse fitting model shows excellent performances in more reasonable interpretation, better stability, less computation, clearer visualization effect and lower requirements for data acquisition process, compared with conventional light intensity model and abstract textural features model. It can be concluded that using ellipse fitting method based on hyperspectral imaging to detect compositions of complex mixed solutions is a potential progress.

Cascading detection model for prediction of apnea-hypopnea events based on nasal flow and arterial blood oxygen saturation.

PURPOSE: Sleep apnea and hypopnea syndrome (SAHS) seriously affects sleep quality. In recent years, much research has focused on the detection of SAHS using various physiological signals and algorithms. The purpose of this study is to find an efficient model for detection of apnea-hypopnea events based on nasal flow and SpO2 signals. METHODS: A 60-s detector and a 10-s detector were cascaded for precise detection of apnea-hypopnea (AH) events. Random forests were adopted for classification of data segments based on morphological features extracted from nasal flow and arterial blood oxygen saturation (SpO2). Then the segments' classification results were fed into an event detector to locate the start and end time of every AH event and predict the AH index (AHI). RESULTS: A retrospective study of 24 subjects' polysomnography recordings was conducted. According to segment analysis, the cascading detection model reached an accuracy of 88.3%. While Pearson's correlation coefficient between estimated AHI and reference AHI was 0.99, in the diagnosis of SAHS severity, the proposed method exhibited a performance with Cohen's kappa coefficient of 0.76. CONCLUSIONS: The cascading detection model is able to detect AH events and provide an estimate of AHI. The results indicate that it has the potential to be a useful tool for SAHS diagnosis.

An active hyperspectral imaging system based on a multi-LED light source.

Hyperspectral imaging (HSI) is a popular method of substance identification and mapping in many fields. Light-emitting diodes (LEDs) can be introduced as an active light source, which have been widely used with the distinct advantages of small size, low energy consumption, long lifetime, and fast switching. In this paper, we propose an active HSI system that is based on a multi-wavelength LED-array light source. This LED-based HSI system has a simple and stable configuration, without the complex dispersive spectrometer and mechanical scanning device. The proposed HSI system has been validated using the standard color checker, showing a reliable spectral performance. Moreover, the spatial-spectral information of Chinese paper-cuttings has been successfully extracted, which indicates the great potential in practical applications.