Research on thyroid nodule segmentation algorithm based on improved U-Net model

School of Public Health, Hubei University of Medicine. Shiyan 442000. China

^* Corresponding author: yongyu@hbmu.edu.cn

Abstract

This research proposes an image segmentation model based on an improved U-Net network (rcU-Net) in response to the phenomena of misinterpretation and missed diagnosis in the process of artificial diagnosis and screening due to the variability of thyroid nodule size and unclear edges in ultrasound images. This paper uses the TN3K dataset as the experimental dataset. The superiority of the proposed model is validated through comparative experiments and ablation experiments. Experimental results show that the proposed model achieves an accuracy of 95.61%, an AUC of 90.67%, a specificity of 98.12%, and a Dice coefficient of 75.69% on the thyroid ultrasound image dataset. The deep learning model proposed in this paper performs well in the segmentation task of thyroid nodule ultrasound images, providing new solutions for the identification of small nodules, nodule edge segmentation, and noise interference in thyroid nodule segmentation tasks.