Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture

Abstract

Abstract Background Identifying thyroid nodules’ boundaries is crucial for making an accurate clinical assessment. However, manual segmentation is time-consuming. This paper utilized U-Net and its improved methods to automatically segment thyroid nodules and glands. Methods The 5822 ultrasound images used in the experiment came from two centers, 4658 images were used as the training dataset, and 1164 images were used as the independent mixed test dataset finally. Based on U-Net, deformable-pyramid split-attention residual U-Net (DSRU-Net) by introducing ResNeSt block, atrous spatial pyramid pooling, and deformable convolution v3 was proposed. This method combined context information and extracts features of interest better, and had advantages in segmenting nodules and glands of different shapes and sizes. Results DSRU-Net obtained 85.8% mean Intersection over Union, 92.5% mean dice coefficient and 94.1% nodule dice coefficient, which were increased by 1.8%, 1.3% and 1.9% compared with U-Net. Conclusions Our method is more capable of identifying and segmenting glands and nodules than the original method, as shown by the results of correlational studies.

Keywords

• Convolutional neural network
• Deep learning
• Ultrasound images
• Semantic segmentation
• Thyroid nodule
• U-Net