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摘要
Objective Lung cancer threatens human health severely. It is one of the malignant tumors with the fastest increase in morbidity and mortality and the greatest threat to the life of the population. In the past 50 years, many countries have addressed that the incidence and mortality of lung cancer have increased significantly. The incidence and mortality of lung cancer rank the first among all the malignant tumors. Recent ultrasound elastography technology has been gradually applied to diagnose the benign and malignant bronchial lymph nodes to aid the degree analysis of lung cancer. Ultrasonic elastography provides more information than conventional two-dimensional ultrasound via the evaluation of lesion toughness. Color Doppler energy imaging superimposes the color coding system on the conventional ultrasound image. In general, the hardness of the diseased lymph node is relatively large, and the degree of deformation is small after being squeezed, which is represented as blue color in the elastic image. The normal lymph node is relatively soft, which is represented as red or green colors in the elastic image. Bronchial ultrasound elastography is generated through the squeezing deformation issues of the lymph nodes in related to the record of heartbeat, breathing movement and the pulsation of blood vessels around the lungs. In bronchial ultrasound elastic images, the precise positioning of the lymph node area is of great significance to the diagnosis accuracy of the disease. However, this kind of task is time-consuming and laborious due to its manual segmentation in clinical. We carried out the deep learning based automatic segmentation method of mediastinal lymph nodes in bronchial ultrasound elastic images via U-Net-type architectures. Method A dataset consisting of 205 bronchial ultrasound elastic images and corresponding segmentation labels is collected. The lymph nodes of each image are manually segmented and labeled. Based on this dataset, six classic deep network models based on U-Net are tested. The U-Net has an encoder-decoder structure. The encoder aims to capture more advanced semantic features and reduce the spatial dimension of the feature map gradually, while the decoder is used to restore spatial details and dimensions. We design a new U-Net-based bronchial ultrasound elastic image segmentation method based on the integration of context extractor and attention mechanism. To avoid gradient explosion and disappearance, the encoder is the ResNet-34 pretrained on ImageNet with no average pooling layer and the fully connected layer. The context extractor is used to extract high-level semantic information further from the output of the encoder while preserving as much spatial information as possible. The attention mechanism aims to select features that are more important to the current task. The prediction result of the segmentation network is the probability value of the pixel classification, so a binarization operation is performed by setting the threshold to 0. 5. That is, the pixels are assigned to 0 if the probability value is less than 0. 5 and otherwise is 1. In this way, the segmented binary image is obtained. Result To verify the performance of different networks, a five-fold cross validation evaluation is conducted on the dataset. That is, we divide the dataset into five equal parts in random, and four of them is as the training set each time and the remaining one is as the testing set. The preprocessing operations are related to data cropping, data augmentation and normalized operation on the training set. The input images and the ground-truth segmentation maps are resized to 320 × 320 pixels. Data augmentation approaches include random vertical flip and random angle rotation. The Adam optimizer is selected and the learning rate is set to 0. 000 1. The batch size is set to 8. The number of epoch is 150. The GPU used in the experiment is GeForce RTX 2080Ti. The segmentation task is implemented using python3. 7 under the Ubuntu16. 04. 1 operating system and the core framework is pytorch1. 7. 1. The results of Dice coefficient, sensitivity and specificity of U-Net network lymph node segmentation are 0. 820 7, 85. 08% and 96. 82%, respectively. On this basis, the segmentation performances of other modified versions of U-Net are all improved to a certain extent. Among them, our Dice coefficient, sensitivity and specificity are 0. 845 1, 87. 92% and 97. 04% of each, which are 0. 024 4, 2. 84% and 0. 22% higher than the baseline U-Net, respectively. Compared to the other methods, the Dice coefficient and the sensitivity achieve the first place, while the specificity ranks the second. Conclusion Our analyses demonstrate that deep learning models represented by U-Net have great potential in the segmentation of mediastinal lymph nodes in bronchial ultrasound elastic images. Fused by the context extractor and attention mechanism, the integrated U-Net network can improve the segmentation accuracy to a certain extent. In addition, the illustrated dataset can promote the research of lymph node segmentation in bronchial ultrasound elastic images. Our method can be used for the segmentation of lymph nodes in bronchial ultrasound elastography images. It has potentials for the segmentation of more medical imaging organs and tumors as well. However, due to the relatively small scale of the dataset, there is still large room for further improvement on the segmentation performance, although the data augmentation approaches have been performed. To improve the segmentation accuracy further, it is required to increase the scale of the dataset in consistency. ? 2022 Journal of Image and Graphics.
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