In order to segment thyroid nodules more accurately， this paper proposes an improved fully convolutional network （FCN） segmentation model. Compared with FCN， the atrous spatial pyramid pooling （ASPP） module and the multi-layer feature transfer （FT） module are added. The decoder module in LinkNet model is used for up-sampling， and the VGG16 backbone network is used for feature extraction down-sampling. The experiment uses 17 413 ultrasound thyroid nodule images from Stanford AIMI shared data set for training， verification and testing， respectively. Experimental results show that compared with other segmentation models， the proposed model achieves 79.7%， 87.6% and 98.42% in mean intersection over union （mIoU）， Dice similarity coefficient and F₁ score respectively， achieving better segmentation effect and effectively improving the segmentation accuracy of thyroid nodules.