Lung cancer has become one of the life-threatening killers. Lung disease need to be assisted by CT images taken doctor's diagnosis, and the segmented CT image of the lung parenchyma is the first step to help doctor diagnosis. For the problem of accurately segmenting the lung parenchyma, this paper proposes a segmentation method based on the combination of VGG-16 and dilated convolution. First of all, we use the first three parts of VGG-16 network structure to convolution and pooling the input image. Secondly, using multiple sets of dilated convolutions make the network has a large enough receptive field. Finally, the multi-scale convolution features are fused, and each pixel is predicted using MLP to segment the parenchymal region. Experimental results were produced over state of the art on 137 images which key metrics Dice similarity coefficient (DSC) is 0.9867. Experimental results show that this method can effectively segment the lung parenchymal area, and compared to other conventional methods better.
Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules are an early indicator of lung cancer. Therefore, accurate detection and image segmentation of lung nodules is of great significance to the early diagnosis of lung cancer. This paper proposes a CT (Computed Tomography) image lung nodule segmentation method based on 3D-UNet and Res2Net, and establishes a new convolutional neural network called 3D-Res2UNet. 3D-Res2Net has a symmetrical hierarchical connection network with strong multi-scale feature extraction capabilities. It enables the network to express multi-scale features with a finer granularity, while increasing the receptive field of each layer of the network. This structure solves the deep level problem. The network is not prone to gradient disappearance and gradient explosion problems, which improves the accuracy of detection and segmentation. The U-shaped network ensures the size of the feature map while effectively repairing the lost features. The method in this paper was tested on the LUNA16 public dataset, where the dice coefficient index reached 95.30% and the recall rate reached 99.1%, indicating that this method has good performance in lung nodule image segmentation.
The total flavonoids from Hemerocallis citrina baroni are regarded as a green and natural health care product with many beneficial impacts on human health. In this study, ultrasound-synergized electrostatic field extraction (UEE) of the total flavonoids (TF) from H. citrina was investigated. Significant independent variables of the extraction, including the electrostatic field, ultrasonic power, ethanol concentration and extraction time, were optimized using the Box-Behnken (BB) method, and the optimal extraction conditions were obtained by response surface methodology (RSM). The extraction yield using UEE was compared with the yields obtained using only ultrasound extraction (UE) and water bath extraction (WE), using a UV-vis spectrophotometer. The best extraction yield of 1.536% using UEE was achieved under the following optimal conditions: electrostatic field of 7kV, ultrasonic power of 500W, ethanol concentration of 70% and extraction time of 20min. The optimal solid-liquid ratio (1:25g/mL) and extraction temperature (55°C) were determined by single factor experiments. Compared to other extraction methods, UEE not only increases the extraction yield of TF but also exhibits an excellent antioxidant activity in assays of the scavenging capacity for DPPH, hydroxyl and superoxide anion radicals. The availability of the UEE method can be supported by the ultrasonic cavitation effect, which plays the most important role in the UEE method. The electrostatic field can be regarded as a random disturbance for sonication, which can strengthen the cavitation effect and increase the cavitation yield. Moreover, the amount of iodine release in potassium iodide solution well validated the synergetic effect between the ultrasound and electrostatic field.
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