Qi Fengyuan, Qiu Min, Wei Guohui
College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, P. R. China.
Department of Thyroid Surgery, Affiliated Hospital of Jining Medical University, Jining, Shandong 272007, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Oct 25;40(5):1027-1032. doi: 10.7507/1001-5515.202302049.
In recent years, the incidence of thyroid diseases has increased significantly and ultrasound examination is the first choice for the diagnosis of thyroid diseases. At the same time, the level of medical image analysis based on deep learning has been rapidly improved. Ultrasonic image analysis has made a series of milestone breakthroughs, and deep learning algorithms have shown strong performance in the field of medical image segmentation and classification. This article first elaborates on the application of deep learning algorithms in thyroid ultrasound image segmentation, feature extraction, and classification differentiation. Secondly, it summarizes the algorithms for deep learning processing multimodal ultrasound images. Finally, it points out the problems in thyroid ultrasound image diagnosis at the current stage and looks forward to future development directions. This study can promote the application of deep learning in clinical ultrasound image diagnosis of thyroid, and provide reference for doctors to diagnose thyroid disease.
近年来，甲状腺疾病的发病率显著上升，超声检查是甲状腺疾病诊断的首选方法。与此同时，基于深度学习的医学图像分析水平得到了快速提高。超声图像分析取得了一系列具有里程碑意义的突破，深度学习算法在医学图像分割和分类领域表现出强大的性能。本文首先阐述了深度学习算法在甲状腺超声图像分割、特征提取和分类鉴别中的应用。其次，总结了深度学习处理多模态超声图像的算法。最后，指出了现阶段甲状腺超声图像诊断中存在的问题，并展望了未来的发展方向。本研究可促进深度学习在甲状腺临床超声图像诊断中的应用，为医生诊断甲状腺疾病提供参考。
