Search

Iatrogenic radial nerve injures are a common complication during the placement of external fixator pins at the lateral aspect of the humeral shaft. This study uses a three-dimensional measurement technique to locate a safe entry point for humeral pins when externally fixating the elbow. Methods: We fixed a guide wire to the radial nerve by a suture string, and used computed tomography (CT) to scan the upper limbs of cadaver specimens. Then, we measured the deviation angles of the radial nerve on the CT scans, and the distance from the radial nerve to the "elbow rotation center" (ERC). Result: The average distance from the radial nerve to the ERC was 87.3 ± 8.5 mm (range: 68–100 mm), 58.3 ± 11.3 mm (range: 32.12–82.84 mm), 106.3 ± 5.8 mm (range: 86.93–115.08 mm), and 113.9 ± 4.8 mm (range: 97.93–120.22 mm) at radial nerve deviation angles of 0°, –30°, 30°, and 45°, respectively. The average radial nerve deviation angle was –37.7° ± 7.7° and 123.9° ± 19.9° at 50 and 150 mm, respectively. Relative to 0°, the distance between the radial nerve and the ERC at radial nerve deviation angles of –30°, 30°, and 45° showed a significant difference (t = 18.20, p < 0.05; Z = 6.07, p < 0.001; Z = 6.40, p < 0.001, respectively). Conclusions: Pins inserted into the proximal humerus should be about 150 mm from the ERC with a radial nerve deviation angle of 30° anteriorly, and 50 mm from the ERC with a deviation angle of 30°–45° posteriorly. [ABSTRACT FROM AUTHOR]

In recent years, block compressed sampling (BCS) has emerged as a considerable attractive sampling technology for image acquisition. However, the general BCS approaches ignore the information distribution in the same image sub-block, and may lead to unfair allocation of sampling resources. In this paper, we propose a novel compressed sampling scheme by employing the idea of adaptive partition. In the proposed scheme, images are adaptively partitioned based on their saliency information through clustering, and pixels with similar saliency are gathered in the same sub-blocks. Sampling rates for those blocks, in turn, are computed on the basis of their saliency values, respectively. Therefore the sampling resources are allocated with fairer and more equitable sharing by all sub-blocks. Experimental results show that the proposed scheme has better visual effect and obtains higher image reconstruction accuracy than existing ones. [ABSTRACT FROM AUTHOR]

Backgrounds: Traditional Chinese medicine and Western medicine combination (TCM-WMC) increased the complexity of compounds ingested. Objective: To develop a method for screening hepatotoxic compounds in TCM-WMC based on chemical structures using artificial intelligence (AI) methods. Methods: Drug-induced liver injury (DILI) data was collected from the public databases and published literatures. The total dataset formed by DILI data was randomly divided into training set and test set at a ratio of 3:1 approximately. Machine learning models of SGD (Stochastic Gradient Descent), kNN (k-Nearest Neighbor), SVM (Support Vector Machine), NB (Naive Bayes), DT (Decision Tree), RF (Random Forest), ANN (Artificial Neural Network), AdaBoost, LR (Logistic Regression) and one deep learning model (deep belief network, DBN) were adopted to construct models for screening hepatotoxic compounds. Result: Dataset of 2035 hepatotoxic compounds was collected in this research, in which 1505 compounds were as training set and 530 compounds were as test set. Results showed that RF obtained 0.838 of classification accuracy (CA), 0.827 of F1-score, 0.832 of Precision, 0.838 of Recall, 0.814 of area under the curve (AUC) on the training set and 0.767 of CA, 0.731 of F1, 0.739 of Precision, 0.767 of Recall, 0.739 of AUC on the test set, which was better than other eight machine learning methods. The DBN obtained 82.2% accuracy on the test set, which was higher than any other machine learning models on the test set. Conclusion: The DILI AI models were expected to effectively screen hepatotoxic compounds in TCM-WMC. [ABSTRACT FROM AUTHOR]

Retinal blood vessel segmentation in fundus images plays an important role in the early diagnosis and treatment of retinal diseases. In recent years, the segmentation methods based on deep neural networks have attracted the attention of experts and scholars. However, due to the complexity of the distribution of blood vessels in fundus images and the imbalance between blood vessels and background, retinal blood vessel segmentation remains challenging. In this paper, we present a retinal blood vessel segmentation method using deep neural networks. Firstly, we propose a novel deep network architecture named Bridge-net to make use of the context of the retinal blood vessels efficiently. Specifically, the architecture incorporates a recurrent neural network (RNN) into a convolutional neural network (CNN) to deliver the context and then to produce the probability map of the retinal blood vessels. Secondly, we propose a patch-based loss weight mapping by considering the distributions of different types of blood vessels to correct the imbalance, since there are large morphological differences between thick and thin blood vessels. Finally, we evaluate our method on three publicly datasets STARE, DRIVE, and CHASE _ DB1, and compare the results to eighteen state-of-the-art approaches. We also compare our method with some existing approaches on a high-resolution dataset, i.e., HRF. The results show that our method achieves better/comparable performances when compared to the existing approaches. The results on various datasets also verify the effectiveness and stability of the proposed method. • We present a novel automatic method for the segmentation of retinal blood vessels. • We propose Bridge-net by joint learning context-involved and non-context features. • We develop a patch-based loss weight mapping to correct the imbalance of the image. • We evaluate the effectiveness of the proposed method on four public datasets. • The results have verified the effectiveness and stability of the proposed method. [ABSTRACT FROM AUTHOR]

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) has had a huge impact on the lives and safety of people around the world. Chest CT is considered an effective tool for the diagnosis and follow-up of COVID-19. For faster examination, automatic COVID-19 diagnostic techniques using deep learning on CT images have received increasing attention. However, the number and category of existing datasets for COVID-19 diagnosis that can be used for training are limited, and the number of initial COVID-19 samples is much smaller than the normal's, which leads to the problem of class imbalance. It makes the classification algorithms difficult to learn the discriminative boundaries since the data of some classes are rich while others are scarce. Therefore, training robust deep neural networks with imbalanced data is a fundamental challenging but important task in the diagnosis of COVID-19. In this paper, we create a challenging clinical dataset (named COVID19-Diag) with category diversity and propose a novel imbalanced data classification method using deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) for COVID-19 diagnosis. The loss function considers both the effects of data overlap between CT slices and possible noisy labels in clinical datasets on a multi-scale, deep supervised network framework by integrating the effective number of samples and a weighting regularization item. The learning process jointly and automatically optimizes all parameters over the deep supervised network, making our model generally applicable to a wide range of datasets. Extensive experiments are conducted on COVID19-Diag and three public COVID-19 diagnosis datasets. The results show that our DSN-SAAL outperforms the state-of-the-art methods and is effective for the diagnosis of COVID-19 in varying degrees of data imbalance. [ABSTRACT FROM AUTHOR]

Background: Osteoporosis (OP) is a systemic bone disease characterized by decreased bone mass, destruction of the bone tissue microstructure, increased bone brittleness and an increased risk of fracture. OP has a high incidence rate and long disease course and is associated with serious complications. Yigu decoction (YGD) is a compound prescription in traditional Chinese medicine that is used to treat OP. However, its mechanism in OP is not clear. This study used a tandem mass tag (TMT)quantitative proteomics method to explore the potential bone-protective mechanism of YGD in an osteoporotic rat model. Materials and methods: A rat model of OP was established by ovariectomy. Eighteen 12-week-old specific-pathogen-free female Wistar rats weighing 220 ± 10 g were selected. The eighteen rats were randomly divided into 3 groups (n = 6 in each group): the normal, model and YGD groups. The right femurs from each group were subjected to quantitative biological analysis. TMT quantitative proteomics was used to analyze the proteins extracted from the bone tissue of rats in the model and YGD groups, and the differentially expressed proteins after intervention with YGD were identified as biologically relevant proteins of interest. Functional annotation correlation analysis was also performed to explore the biological function and mechanism of YGD. Result: Compared with the model group, the YGD group showed significant upregulation of 26 proteins (FC > 1.2, P < 0.05) and significant downregulation of 39 proteins (FC < 0.833, P < 0.05). Four important targets involved in OP and 5 important signaling pathways involved in bone metabolism were identified. Conclusions: YGD can significantly increase the bone mineral density (BMD) of osteoporotic rats and may play a therapeutic role by regulating target proteins involved in multiple signaling pathways. Therefore, these results improve the understanding of the OP mechanism and provide an experimental basis for the clinical application of YGD in OP treatment. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional features have been applied to discriminative correlation filters-based methods, which have achieved impressive performance in tracking. Most of them utilize hierarchical features from a certain layer. However, this is not always sufficient to learn target appearance changes and to suppress the background interference in complicated interfering factors (e.g., deformation, fast motion, low resolution, and rotations). In this paper, we propose an adaptive multi-branch correlation filter tracking method, by constructing multi-branch models and using an adaptive selection strategy to improve the accuracy and robustness of visual tracking. Specially, the multi-branch models are introduced to tolerate temporal changes of the object, which can serve different circumstances. In addition, the adaptive selection strategy incorporates both foreground and background information to learn background suppression. To further improve the tracking performance, we propose a measurement method to handle tracking failures from unreliable samples. Extensive experiments on OTB-2013, OTB-2015, and VOT-2016 datasets show that the proposed tracker has comparable performance compared to state-of-the-art tracking methods. Especially, on the OTB-2015, our method significantly improves the baseline with a gain of 5.5% in overlap precision. [ABSTRACT FROM AUTHOR]

• We propose an improved dataset for research on image privacy. • We build a knowledge graph that represents the relevance between the objects and image privacy. • Based on the graph, we deal with the traditional image classification problem with the novel graph-based neural network. • The introduction of the knowledge graph not only makes the model more explainable but also makes better use of the information of objects provided by the images. • Our method relies on the intrinsic relevance in the dataset rather than extra knowledge, thus is applicable for other tasks. With the development of social media and smartphones, people share their daily lives via a large number of images, but the convince also raises a problem of privacy leakage. Therefore, effective methods are needed to infer the privacy risk of images and identify images that may disclose privacy. Several works have tried to solve this problem with deep learning models. However, we know little about how the models infer the privacy label of an image, thus it is not easy to understand why the image may disclose privacy. Inspired by recent research on graph neural networks, we introduce prior knowledge to the deep models to make the inference more explainable. We propose the Graph-based neural networks for Image Privacy (GIP) to infer the privacy risk of images. The GIP mainly focuses on objects in an image, and the knowledge graph is extracted from the objects in the dataset without reliance on extra knowledge. Experimental results show that the GIP achieves higher performance compared with the object-based methods and comparable performance even compared with the multi-modal fusion method. The results show that the introduction of the knowledge graph not only makes the deep model more explainable but also makes better use of the information of objects provided by the images. Combing the knowledge graph with deep learning is a promising way to help protect image privacy that is worth exploring. [ABSTRACT FROM AUTHOR]

Abstract Lung cancer is one of the leading causes of cancer-related death worldwide. Early diagnosis can effectively reduce the mortality, and computer-aided diagnosis (CAD) as an important way to assist doctors has developed rapidly. In particular, automated pulmonary nodule detection in computed tomography (CT) images is crucial to CAD. It is a challenging task to quickly locate the exact positions of lung nodules. In this paper, a novel automated pulmonary nodule detection framework with 2D convolutional neural network (CNN) is proposed to assist the CT reading process. Firstly, we adjust the structure of Faster R-CNN with two region proposal networks and a deconvolutional layer to detect nodule candidates, and then three models are trained for three kinds of slices for later result fusion. Secondly, a boosting architecture based on 2D CNN is designed for false positive reduction, which is a classifier to distinguish true nodules from the candidates. The misclassified samples are still kept for retraining a model which boosts the sensitivity for pulmonary nodule detection. Finally, the results of these networks are fused to vote out the final classification results. Extensive experiments are conducted on LUNA16, and the sensitivity of nodule candidate detection achieves 86.42%. For the false positive reduction, the sensitivity reaches 73.4% and 74.4% at 1/8 and 1/4 FPs/scan, respectively. It illustrates that the proposed method can obviously achieve accurate pulmonary nodule detection. [ABSTRACT FROM AUTHOR]

Accurate skin lesion segmentation in dermoscopic images is crucial to the early diagnosis of skin cancers. However, it remains a challenging task due to fuzzy lesion boundaries, irregular lesion shapes, and the existence of various interference factors. In this paper, a novel Attention Synergy Network (AS-Net) is developed to enhance the discriminative ability for skin lesion segmentation by combining both spatial and channel attention mechanisms. The spatial attention path captures lesion-related features in the spatial dimension while the channel attention path selectively emphasizes discriminative features in the channel dimension. The synergy module is designed to optimally integrate both spatial and channel information, and a weighted binary cross-entropy loss function is introduced to emphasize the foreground lesion region. Comprehensive experiments indicate that our proposed model achieves the state-of-the-art performance with the highest overall score in the ISIC2017 challenge, and outperforms several popular deep neural networks on both ISIC2018 and PH2 datasets. • Proposes AS-Net to enhance the discriminative ability for skin lesion segmentation. • Combines spatial and channel attentions for feature learning of skin lesions. • A weighted loss function is presented to emphasize the skin lesion region. • Our model outperforms state-of-the-art methods on three publicly available datasets. [ABSTRACT FROM AUTHOR]

• We propose an efficient 3D residual neural network for brain tumor segmentation. • We propose a fusion loss function based on Dice and Cross-entropy. • We introduce a concise but effective post-processing method. • The evaluation is performed on the BRATS 2018 dataset. • The results demonstrate that our method outperforms the state-of-the-art approaches. Brain tumors are the most aggressive and mortal cancers, which lead to short life expectancy. A reliable and efficient automatic or semi-automatic segmentation method is significant for clinical practice. In recent years, deep learning-based methods achieve great success in brain tumor segmentation. However, due to the limitation of parameters and computational complexity, there is still much room for improvement in these methods. In this paper, we propose an efficient 3D residual neural network (ERV-Net) for brain tumor segmentation, which has less computational complexity and GPU memory consumption. In ERV-Net, a computation-efficient network, 3D ShuffleNetV2, is firstly utilized as encoder to reduce GPU memory and improve the efficiency of ERV-Net, and then the decoder with residual blocks (Res-decoder) is introduced to avoid degradation. Furthermore, a fusion loss function, which is composed of Dice loss and Cross-entropy loss, is developed to solve the problems of network convergence and data imbalance. Moreover, a concise and effective post-processing method is proposed to refine the coarse segmentation result of ERV-Net. The experimental results on the dataset of multimodal brain tumor segmentation challenge 2018 (BRATS 2018) demonstrate that ERV-Net achieves the best performance with Dice of 81.8%, 91.21% and 86.62% and Hausdorff distance of 2.70 mm, 3.88 mm and 6.79 mm for enhancing tumor, whole tumor and tumor core, respectively. Besides, ERV-Net also achieves high efficiency compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]