Ultrasound Image Segmentation Method for Thyroid Nodules Using ASPP Fusion Features

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.

Keywords

• Thyroid nodule
• medical image segmentation
• atrous spatial pyramid pooling
• dilated convolution
• ultrasound image