Abstract
Ultrasound imaging technology plays an important role to assist doctors in diagnosing thyroid nodules. The tissue structure around the thyroid is very complex, which makes it difficult to segment and extract the ultrasound image of thyroid nodules accurately. For address this problem, this paper proposes a model algorithm for thyroid nodule ultrasound image segmentation using ASPP fusion features. First, spatial pyramid pooling and depthwise separable convolution are combined in order to solve the problem that the size of the mapping feature will change in the process of better capturing the context information. Besides, Atrous Spatial Pyramid Pooling (ASPP) is proposed to achieve the purpose of processing input image channel and spatial information separately. In order to appropriately reduce the dimension and size of feature images, a $1\times 1$ convolution operation is performed before each convolution calculation, and the model size is optimized. In the decoding stage, decoder module appropriately adjusts the feature map with a relatively low resolution previously from decoder module, and sets the output channel number of two convolutions to the same value. All features have the same dimension by adjustment, and features can be fused by element-wise summation. Finally, Dice Similarity Coefficient (DSC), Prevent Match (PM) and Correspondence Patio (CR) are used as evaluation criteria to compare with other model algorithms. The experimental results show that the proposed model can significantly improve the segmentation effect of ultrasound images for thyroid nodules compared with traditional models.
Highlights
In recent years, artificial intelligence technology and medical imaging have become more and more closely integrated [1]–[3]
Using Tversky loss as a loss function can effectively improve the performance of segmentation. It can be seen from the above results that Dice Similarity Coefficient (DSC), Prevent Match (PM) and Correspondence Patio (CR) calculated by the model algorithm proposed in this paper are 0.9961, 0.9931 and 0.9874 respectively
This paper proposes an ultrasound image segmentation model algorithm for thyroid nodules based on Atrous Spatial Pyramid Pooling (ASPP) fusion features
Summary
Artificial intelligence technology and medical imaging have become more and more closely integrated [1]–[3]. Y. Wu et al.: Ultrasound Image Segmentation Method for Thyroid Nodules Using ASPP Fusion Features of images, and reduced false edges by obtained high-order harmonics [15]. This model make up for the defect that ordinary dilated convolution will fail when the expansion rate increases to a certain extent, and can gradually increase the receptive field of each layer of dilated convolution This model had certain defects in the application of ultrasound image segmentation, such as the unsmooth edges of segmentation [21]. The main contribution of this paper is: 1) Based on the DenseNet-121 network structure model and combined with the Atrous Spatial Pyramid Pooling (ASPP), and proposes a new segmentation model for ultrasound images of thyroid nodules. The experimental results show that the segmentation model method proposed in this paper greatly improves the segmentation effect of ultrasound images of thyroid nodules.
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