Exploration of a Novel Method for Thyroid Nodule Ultrasound Image Segmentation

Ultrasound examination of thyroid nodules is currently the mainstream method for diagnosing thyroid diseases. Segmentation of thyroid nodule ultrasound images is a cru-cial task in computer-aided diagnosis of thyroid nodules and holds significant importance in the entire diagnostic process. However, due to the complex features and low contrast of thyroid ultrasound images, traditional semantic segmentation algorithms struggle to extract effective features, making it dif-ficult to accurately identify the nodule regions and complete the segmentation task. To address these issues and advance research in thyroid nodule segmentation, this paper proposes a deep learning model for accurate identification and high-quality segmentation of nodule regions in thyroid nodule segmentation. To demonstrate the effectiveness of our method, we collected ultrasound images from clinical examinations at the Second Hospital of Shandong University to serve as our experimental dataset. Evaluations conducted on this dataset show that our method achieves outstanding performance compared to existing mainstream algorithms.