Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.

Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments. A total of 375 patients who underwent polysomnography were included. The single-lead electrocardiogram signals obtained by polysomnography were used to train, validate and test the model. Moreover, the proposed model performance on a public dataset was compared with the findings of previous studies. In the test set, the accuracy of per-segment and per-recording detection were 82.55% and 85.33%, respectively. The accuracy values for mild, moderate and severe obstructive sleep apnea were 69.33%, 74.67% and 85.33%, respectively. In the public dataset, the accuracy of per-segment detection was 91.66%. A Bland-Altman plot revealed the consistency of true apnea-hypopnea index and predicted apnea-hypopnea index. We confirmed the feasibility of single-lead electrocardiogram signals and deep-learning model for obstructive sleep apnea detection and severity evaluation in both hospital and public datasets. The detection performance is high for patients with obstructive sleep apnea, especially those with severe obstructive sleep apnea.

Metabolomics analysis of the effects of chelerythrine on Ustilaginoidea virens.

Rice false smut (RFS) caused by Ustilaginoidea virens is widely distributed in major rice-producing regions. Previous studies have shown that treating RFS with chelerythrine can decrease the germination of fungus spores by 86.7% and induce fungal cell apoptosis. In the present study, the effects of chelerythrine on the metabolism of U. virens explored using metabolomics and analyses of differentially accumulated metabolites and altered metabolic pathways. The top 15 metabolites in random forest analysis were significantly different between groups. In positive ion mode, purine, phenylalanine metabolism, phenylalanine, tyrosine, tryptophan biosynthesis, pyrimidine metabolism, and nitrogen metabolism were dominant. Alanine, aspartate, glutamate metabolism, and phenylalanine metabolism were enriched in negative ion mode. Differentially expressed genes and altered metabolic pathways of U. virens were effected by chelerythrine. The findings support future research on the prevention and treatment of RFS by chelerythrine and provide a theoretical basis for targeted drug delivery.

Joint learning of multi-level dynamic brain networks for autism spectrum disorder diagnosis.

Graph convolutional networks (GCNs), with their powerful ability to model non-Euclidean graph data, have shown advantages in learning representations of brain networks. However, considering the complexity, multilayeredness, and spatio-temporal dynamics of brain activities, we have identified two limitations in current GCN-based research on brain networks: 1) Most studies have focused on unidirectional information transmission across brain network levels, neglecting joint learning or bidirectional information exchange among networks. 2) Most of the existing models determine node neighborhoods by thresholding or simply binarizing the brain network, which leads to the loss of edge weight information and weakens the model's sensitivity to important information in the brain network. To address the above issues, we propose a multi-level dynamic brain network joint learning architecture based on GCN for autism spectrum disorder (ASD) diagnosis. Specifically, firstly, constructing different-level dynamic brain networks. Then, utilizing joint learning based on GCN for interactive information exchange among these multi-level brain networks. Finally, designing an edge self-attention mechanism to assign different edge weights to inter-node connections, which allows us to pick out the crucial features for ASD diagnosis. Our proposed method achieves an accuracy of 81.5 %. The results demonstrate that our method enables bidirectional transfer of high-order and low-order information, facilitating information complementarity between different levels of brain networks. Additionally, the use of edge weights enhances the representation capability of ASD-related features.

SST: a snore shifted-window transformer method for potential obstructive sleep apnea patient diagnosis.

Objective.Obstructive sleep apnea (OSA) is a high-incidence disease that is seriously harmful and potentially dangerous. The objective of this study was to develop a noncontact sleep audio signal-based method for diagnosing potential OSA patients, aiming to provide a more convenient diagnostic approach compared to the traditional polysomnography (PSG) testing.Approach.The study employed a shifted window transformer model to detect snoring audio signals from whole-night sleep audio. First, a snoring detection model was trained on large-scale audio datasets. Subsequently, the deep feature statistical metrics of the detected snore audio were used to train a random forest classifier for OSA patient diagnosis.Main results.Using a self-collected dataset of 305 potential OSA patients, the proposed snore shifted-window transformer method (SST) achieved an accuracy of 85.9%, a sensitivity of 85.3%, and a precision of 85.6% in OSA patient classification. These values surpassed the state-of-the-art method by 9.7%, 10.7%, and 7.9%, respectively.Significance.The experimental results demonstrated that SST significantly improved the noncontact audio-based OSA diagnosis performance. The study's findings suggest a promising self-diagnosis method for potential OSA patients, potentially reducing the need for invasive and inconvenient diagnostic procedures.

Porous Waterborne Polyurethane Films Templated from Pickering Foams for Fabrication of Synthetic Leather.

Waterborne polyurethane (WPU) latex nanoparticles with proven interfacial activity were utilized to stabilize air-water interfaces of Pickering foams through interfacial interaction with hydrophobic fumed silica particles (SPs). The rheological properties of the Pickering foam were tailored through adjustment of their SP content, which influenced their formability and stability. A Pickering foam stabilized with WPU and SPs was used as a template to prepare a WPU-SP composite porous film. The as-prepared film had intact open-cell porous structures, which increased its water absorption and water-vapor permeability. The porous film was used as a middle layer in the preparation of synthetic leather via a four-step "drying method". Compared with commercial synthetic leather, the lab-made synthetic leather with a middle layer made of the WPU-SP composite porous film exhibited a richer porous structure, acceptable wetting on a fabric substrate, a thicker porous layer, and higher water-vapor permeability. This work provides a novel and facile approach for preparing WPU-SP Pickering foams. Furthermore, the foams have the potential to function as a sustainable material for creating a porous-structured synthetic leather made from WPU, which may be utilized as an alternative to solvent-based synthetic leather.

Vocal Cord Leukoplakia Classification Using Siamese Network Under Small Samples of White Light Endoscopy Images.

OBJECTIVE: Accurate vocal cord leukoplakia classification is instructive for clinical diagnosis and surgical treatment. This article introduces a reliable very deep Siamese network for accurate vocal cord leukoplakia classification. STUDY DESIGN: A study of a classification network based on a retrospective database. SETTING: Academic university and hospital. METHODS: The white light image datasets of vocal cord leukoplakia used in this article were classified into 6 classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia, and squamous cell carcinoma. The classification performance was assessed by comparing it with 6 classical deep learning models, including AlexNet, VGG Net, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: Experiments show the superior classification performance of our proposed network compared to state-of-the-art methods. The overall accuracy is 0.9756. The values of sensitivity and specificity are very high as well. The confusion matrix provides information for the 6-class classification task and demonstrates the superiority of our proposed network. CONCLUSION: Our very deep Siamese network can provide accurate classification results of vocal cord leukoplakia, which facilitates early detection, clinical diagnosis, and surgical treatment. The excellent performance obtained in white light images can reduce the cost for patients, especially those living in developing countries.

Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images.

BACKGROUND: Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the laryngeal tasks. This article introduces and reliably evaluates existing deep learning models for vocal cord leukoplakia classification. METHODS: We created white light and narrow band imaging (NBI) image datasets of vocal cord leukoplakia which were classified into six classes: normal tissues (NT), inflammatory keratosis (IK), mild dysplasia (MiD), moderate dysplasia (MoD), severe dysplasia (SD), and squamous cell carcinoma (SCC). Vocal cord leukoplakia classification was performed using six classical deep learning models, AlexNet, VGG, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: GoogLeNet (i.e., Google Inception V1), DenseNet-121, and ResNet-152 perform excellent classification. The highest overall accuracy of white light image classification is 0.9583, while the highest overall accuracy of NBI image classification is 0.9478. These three neural networks all provide very high sensitivity, specificity, and precision values. CONCLUSION: GoogLeNet, ResNet, and DenseNet can provide accurate pathological classification of vocal cord leukoplakia. It facilitates early diagnosis, providing judgment on conservative treatment or surgical treatment of different degrees, and reducing the burden on endoscopists.

Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface.

BACKGROUND: Motor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition. METHODS: This paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label. RESULTS: Experiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods. CONCLUSION: The proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.

A shallow mirror transformer for subject-independent motor imagery BCI.

OBJECTIVE: Motor imagery BCI plays an increasingly important role in motor disorders rehabilitation. However, the position and duration of the discriminative segment in an EEG trial vary from subject to subject and even trial to trial, and this leads to poor performance of subject-independent motor imagery classification. Thus, determining how to detect and utilize the discriminative signal segments is crucial for improving the performance of subject-independent motor imagery BCI. APPROACH: In this paper, a shallow mirror transformer is proposed for subject-independent motor imagery EEG classification. Specifically, a multihead self-attention layer with a global receptive field is employed to detect and utilize the discriminative segment from the entire input EEG trial. Furthermore, the mirror EEG signal and the mirror network structure are constructed to improve the classification precision based on ensemble learning. Finally, the subject-independent setup was used to evaluate the shallow mirror transformer on motor imagery EEG signals from subjects existing in the training set and new subjects. MAIN RESULTS: The experiments results on BCI Competition IV datasets 2a and 2b and the OpenBMI dataset demonstrated the promising effectiveness of the proposed shallow mirror transformer. The shallow mirror transformer obtained average accuracies of 74.48% and 76.1% for new subjects and existing subjects, respectively, which were highest among the compared state-of-the-art methods. In addition, visualization of the attention score showed the ability of discriminative EEG segment detection. This paper demonstrated that multihead self-attention is effective in capturing global EEG signal information in motor imagery classification. SIGNIFICANCE: This study provides an effective model based on a multihead self-attention layer for subject-independent motor imagery-based BCIs. To the best of our knowledge, this is the shallowest transformer model available, in which a small number of parameters promotes the performance in motor imagery EEG classification for such a small sample problem.

Salvianolic acid B exerts an anti-hepatocellular carcinoma effect by regulating the Hippo/YAP pathway and promoting pSmad3L to pSmad3C simultaneously.

Salvianolic acid B (Sal B) is a component obtained from Salvia miltiorrhiza and is empirically used for liver diseases. The TGF-ß/Smad and Hippo/YAP pathways may interact with each other in hepatocellular carcinoma (HCC). Previously, we found that Sal B mediates the TGF-ß/Smad pathway in mice and delays liver fibrosis-carcinoma progression by promoting the conversion of pSmad3L to pSmad3C, but the effect of Sal B on the Hippo/YAP pathway has not been determined. Therefore, we used a DEN/CCl4/C2H5OH-induced liver cancer model in mice to analyze liver index and tumor incidence, detect AST and ALT serological markers, observe liver pathology and the number of Ki67-positive cells to evaluate the anti-HCC effect of Sal B in vivo. We used a TGF-ß1-induced HepG2 cell model, and applied an MST1/2 inhibitor, XMU-MP-1, to detect the changes in pSmad3C/pSmad3L signaling induced by MST1/2 inhibition. Sal B significantly inhibited tumorigenesis in DEN/CCl4/C2H5OH-induced mice in vivo, and suppressed the growth of HepG2 cells by inhibiting cell proliferation and migration in vitro. Here, our study also validated the role of Sal B in reversing XMU-MP-1-induced proliferation and migration of HepG2 cells in vitro. Most importantly, we elucidated for the first time the potential mechanism of Sal B against HCC via the Hippo/YAP pathway, which may be specifically related to upregulation of MST1 and inhibition of its downstream effector protein YAP. In conclusion, these findings indicate that Sal B possesses anti- HCC effects both in vivo and in vitro by regulating the Hippo/YAP pathway and promoting pSmad3L to pSmad3C synchronously.

Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images.

This article uses microscopy images obtained from diverse anatomical regions of macaque brain for neuron semantic segmentation. The complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset increase the difficulty of neuron semantic segmentation. To address this problem, we propose a multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) to improve the semantic segmentation performance in major anatomical regions of the macaque brain. After evaluating microscopic images from 17 anatomical regions, the semantic segmentation performance of neurons is improved by 10.6%, 4.0%, 1.5%, and 1.2% compared with Random Forest, FCN-8s, U-Net, and UNet++, respectively. Especially for neurons with brighter staining intensity in the anatomical regions such as lateral geniculate, globus pallidus and hypothalamus, the performance is improved by 66.1%, 23.9%, 11.2%, and 6.7%, respectively. Experiments show that our proposed method can efficiently segment neurons with a wide range of staining intensities. The semantic segmentation results are of great significance and can be further used for neuron instance segmentation, morphological analysis and disease diagnosis. Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain. HIGHLIGHTS: Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain.

Effects of imidacloprid and thiamethoxam on the development and reproduction of the soybean aphid Aphis glycines.

The soybean aphid Aphis glycines Matsumura (Hemiptera: Aphididae) is a primary pest of soybeans and poses a serious threat to soybean production. Our studies were conducted to understand the effects of different concentrations of insecticides (imidacloprid and thiamethoxam) on A. glycines and provided critical information for its effective management. Here, we found that the mean generation time and adult and total pre-nymphiposition periods of the LC50 imidacloprid- and thiamethoxam-treatment groups were significantly longer than those of the control group, although the adult pre-nymphiposition period in LC30 imidacloprid and thiamethoxam treatment groups was significantly shorter than that of the control group. Additionally, the mean fecundity per female adult, net reproductive rate, intrinsic rate of increase, and finite rate of increase of the LC30 imidacloprid-treatment group were significantly lower than those of the control group and higher than those of the LC50 imidacloprid-treatment group (P < 0.05). Moreover, both insecticides exerted stress effects on A. glycines, and specimens treated with the two insecticides at the LC50 showed a significant decrease in their growth rates relative to those treated with the insecticides at LC30. These results provide a reference for exploring the effects of imidacloprid and thiamethoxam on A. glycines population dynamics in the field and offer insight to agricultural producers on the potential of low-lethal concentrations of insecticides to stimulate insect reproduction during insecticide application.

Astragaloside IV inhibits hepatocellular carcinoma by continually suppressing the development of fibrosis and regulating pSmad3C/3L and Nrf2/HO-1 pathways.

ETHNOPHARMACOLOGICAL RELEVANCE: Astragalus is a medicinal herb used in China for the prevention and treatment of diseases such as diabetes and cancer. As one of the main active ingredients of astragalus, Astragaloside IV (AS-IV) has a wide range of pharmacological effects, including anti-inflammation and anti-cancer effects. AIM OF THE STUDY: Different phosphorylated forms of Smad3 differentially regulate the progression of hepatic carcinoma. The phosphorylation of the COOH-terminal of Smad3 (pSmad3C) and activation of the Nrf2/HO-1 pathway inhibits hepatic carcinoma, while phosphorylation of the linker region of Smad3 (pSmad3L) promotes progression. Thus, pSmad3C/3L and Nrf2/HO-1 pathways are potential targets for drug of anti-cancer development. AS-IV is anti-apoptotic and can inhibit hepatocellular carcinoma cell (HCC) proliferation, invasion, and tumor growth in nude mice. However, it is not clear whether AS-IV has a therapeutic effect on inhibiting the progression of primary liver cancer by regulating the pSmad3C/3L and Nrf2/HO-1 pathway. The purpose of this study is to investigate whether AS-IV inhibits hepatocellular carcinoma by regulating pSmad3C/3L and Nrf2/HO-1 pathway. MATERIALS AND METHODS: primary liver cancer in mice induced by DEN/CCl4/C2H5OH (DCC) and HSC-T6/HepG2 cell models activated by TGF-ß1 was investigated for the mechanisms of AS-IV. In vivo assays included liver biopsy, histopathology and post-mortem analysis included immunohistochemistry, immunofluorescent, and Western blotting analysis, and in vitro assays included immunofluorescent, and Western blotting analysis. RESULTS: AS-IV significantly inhibited the development of primary liver cancer, reflecting improved liver biopsy, histopathology. The incidence and multiplicity of primary liver cancer were markedly decreased by AS-IV treatment at the 20th week. AS-IV had observable effects on the TGF-ß1/Smad and Nrf2/HO-1 expression in vivo, especially up-regulated pSmad3C, pNrf2, HO-1, and NQO1, while it down-regulated pSmad2C, pSmad2L, pSmad3L, PAI-1, and α-SMA at the 12th week and the 20th week. Furthermore, in vitro analysis further confirmed that AS-IV regulated the expression of pSmad3C/3L and Nrf2/HO-1 pathway in HSC-T6 and HepG2 cells activated by TGF-ß1. CONCLUSION: AS-IV administration delays the occurrence of primary liver cancer by continually suppressing the development of fibrosis, the mechanism of the therapeutic effect involving the regulation of the pSmad3C/3L and Nrf2/HO-1 pathways, especially in regulation reversibility and antagonism of pSmad3C and pSmad3L and promoting the phosphorylation of Nrf2.

Smad3 gene C-terminal phosphorylation site mutation exacerbates CCl4-induced hepatic fibrogenesis by promoting pSmad2L/C-mediated signaling transduction.

Current researches have confirmed that Smads, mediators of TGF-ß signaling, are strictly controlled by domain-specific site phosphorylation in the process of hepatic disease. Usually, Smad3 phospho-isoform pSmad3L and pSmad3C are reversible and antagonistic; pSmad2L/C could act together with pSmad3L by stimulating PAI-1 expression and ECM synthesis to transmit fibrogenic signals. Our recent study found that pSmad3C mutation is supposed to perform a vigorous role on the early phase of liver injury and abates salvianolic acid B's anti-hepatic fibrotic-carcinogenesis. However, whether pSmad3C mutation expedites pSmad2L/C-mediated signaling transduction during hepatic fibrogenesis remains vague. Presently, Smad3 gene C-terminal phosphorylation site mutation heterozygote (pSmad3C+/-) mice were constructed to probe if and how pSmad3C retards CCl4-induced hepatic fibrogenesis by inhibiting pSmad2L/C-mediated signaling transduction. Twelve 6-week-old pSmad3C+/- C57BL/6J mice were intraperitoneally injection with CCl4 for 6 weeks to induce liver fibrogenesis. Results showed that pSmad3C mutation aggravates the relative liver weight, biochemical parameters, collagenous fibers and fibrotic septa formation, contributes to fibrogenesis in HT-CCl4 mice. Furthermore, fibrotic-related proteins TGF-ß1, pSmad2C, pSmad2L, and PAI-1 were also increased in CCl4-induced pSmad3C+/- mice. These results suggest that pSmad3C mutation exacerbates hepatic fibrogenesis which relates to intensifying pSmad2L/C-mediated signaling transduction.

Evaluation of automated segmentation algorithms for neurons in macaque cerebral microscopic images.

Accurate cerebral neuron segmentation is required before neuron counting and neuron morphological analysis. Numerous algorithms for neuron segmentation have been published, but they are mainly evaluated using limited subsets from a specific anatomical region, targeting neurons of clear contrast and/or neurons with similar staining intensity. It is thus unclear how these algorithms perform on cerebral neurons in diverse anatomical regions. In this article, we introduce and reliably evaluate existing machine learning algorithms using a data set of microscopy images of macaque brain. This data set highlights various anatomical regions (e.g., cortex, caudate, thalamus, claustrum, putamen, hippocampus, subiculum, lateral geniculate, globus pallidus, etc.), poor contrast, and staining intensity differences of neurons. The evaluation was performed using 10 architectures of six classic machine learning algorithms in terms of typical Recall, Precision, F-score, aggregated Jaccard index (AJI), as well as a performance ranking of algorithms. F-score of most of the algorithms is superior to 0.7. Deep learning algorithms facilitate generally higher F-scores. U-net with suitable layer depth has been evaluated to be excellent classifiers with F-score of 0.846 and 0.837 when performing cross validation. The evaluation and analysis indicate the performance gap among algorithms in various anatomical regions and the strengths and limitations of each algorithm. The comparative result highlights at the same time the importance and difficulty of neuron segmentation and provides clues for future improvement. To the best of our knowledge, this work is the first comprehensive study for neuron segmentation in such large-scale anatomical regions. Neuron segmentation plays a critical role in extracting cerebral information, such as neuron counting and neuron morphological analysis. Accurate automated cerebral neuron segmentation is a challenging task due to different kinds, poor contrast, staining intensity differences, and fuzzy boundaries of neurons. The comprehensive evaluation and analysis of performance among existing machine learning algorithms in diverse anatomical regions allows to make clear of the strengths and limitations of state-of-the-art algorithm. The comprehensive study provides clues for future improvement and creation of automated methods.

Effect of rotenone-induced stress on physiologically active substances in adult Aphis glycines.

The aim of this study was to determine the effect of rotenone stress on Aphis glycines Matsumura (Hemiptera: Aphididae) populations in different habitats of Northeast China. The changes in kinase expression activity of endogenous substances (proteins, total sugars, trehalose, cholesterol, and free amino acids), detoxifying enzymes (cytochrome P450 and glutathione S-transferase), and metabolic enzymes (proteases and phosphofructokinases) in specimens from three populations were compared before and after stress with rotenone at median lethal concentration (LC50) and their response mechanisms were analyzed. Following a 24 h treatment with rotenone, the average LC50 rotenone values in A. glycines specimens from field populations A and B, and a laboratory population were 4.39, 4.61, and 4.03 mg/L, respectively. The degree of changes in the kinase expression activity of endogenous substances also differed, which indicated a difference in the response of A. glycines specimens from varying habitats to LC50 rotenone stress. The content of endogenous substances, detoxifying enzymes, and metabolic enzymes, except for that of free amino acids, changed significantly in all populations treated with rotenone at LC50 compared with that in the control (P < 0.05). The decrease in protein and trehalose content, and the obstruction of cholesterol transportation owing to decreased feeding in stressed individuals were the causes of A. glycines death after rotenone treatment. Aphis glycines resistance to rotenone may be related to cytochrome P450 expression.

Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images.

Accurate lung segmentation is an essential step in developing a computer-aided lung disease diagnosis system. However, because of the high variability of computerized tomography (CT) images, it remains a difficult task to accurately segment lung tissue in CT slices using a simple strategy. Motived by the aforementioned, a novel CT lung segmentation method based on the integration of multiple strategies was proposed in this paper. Firstly, in order to avoid noise, the input CT slice was smoothed using the guided filter. Then, the smoothed slice was transformed into a binary image using an optimized threshold. Next, a region growing strategy was employed to extract thorax regions. Then, lung regions were segmented from the thorax regions using a seed-based random walk algorithm. The segmented lung contour was then smoothed and corrected with a curvature-based correction method on each axis slice. Finally, with the lung masks, the lung region was automatically segmented from a CT slice. The proposed method was validated on a CT database consisting of 23 scans, including a number of 883 2D slices (the number of slices per scan is 38 slices), by comparing it to the commonly used lung segmentation method. Experimental results show that the proposed method accurately segmented lung regions in CT slices.

Evaluation of MTANNs for eliminating false-positive with different computer aided pulmonary nodules detection software.

MTANN (Massive Training Artificial Neural Network) is a promising tool, which applied to eliminate false-positive for thoracic CT in recent years. In order to evaluate whether this method is feasible to eliminate false-positive of different CAD schemes, especially, when it is applied to commercial CAD software, this paper evaluate the performance of the method for eliminating false-positives produced by three different versions of commercial CAD software for lung nodules detection in chest radiographs. Experimental results demonstrate that the approach is useful in reducing FPs for different computer aided lung nodules detection software in chest radiographs.

A joint ROI extraction filter for computer aided lung nodule detection.

Extraction of regions of interest plays an important rule in computer aided lung nodules detection. However, because of the complex background and structure, accurate and robust extraction of ROIs in medical image still remains a problem. Aim at this problem, a two-stage operations joint filter: Hessian-LoB, is proposed. The first stage is blobs (which being taken as candidate ROIs) detection and the second stage is ROIs extraction. In the first stage, the derivatives of a Hessian matrix at multiple scales are convolved with input images to localize blobs. Then in the second stage, Laplacian of bilateral filter (LoB) is convolved with the detected blobs to extract the final ROIs. Experiments show that the proposed filter can deal with images with noise and low brightness contrast, and is effectively in ROI extraction for lung nodule detection.

A new method of detecting pulmonary nodules with PET/CT based on an improved watershed algorithm.

BACKGROUND: Integrated 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives. METHOD: Our proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method. RESULTS: Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).