摘要
甲状腺结节作为一种常见的内分泌疾病,其早期检测和准确诊断对于预防甲状腺癌至关重要。由于甲状腺结节的形态和边界高度异质化,对其精准识别与分类仍面临较大挑战。传统的诊断方法依赖医生经验,存在误诊和漏诊的风险。随着计算机辅助诊断技术的迅速发展,将深度学习算法应用于甲状腺结节图像处理已展现出极大潜力。本文综述了基于深度学习的甲状腺结节计算机辅助诊断技术的最新研究进展,重点回顾了深度学习在图像预处理、分割与分类中的应用,并分析了当前技术的优势、局限性及未来发展方向。本文旨在揭示深度学习在甲状腺结节诊断中的潜力,并为进一步的临床应用选择可行的技术路径奠定基础。
Thyroid nodules are a common endocrine disorder,and their early detection and accurate diagnosis are crucial for the prevention of thyroid cancer.However,the highly heterogeneous morphology and boundaries of thyroid nodules pose significant challenges to their precise identification and classification.Traditional diagnostic approaches rely heavily on physicians’experience,which increases the risk of misdiagnosis and missed diagnoses.With the rapid advancement of computer-aided diagnosis(CAD)technologies,applying deep learning algorithms to the analysis of thyroid nodule ultrasound images has shown great potential.This paper reviews the latest research progress on deep learning-based CAD methods for thyroid nodules,with a focus on their applications in image preprocessing,segmentation and classification.The advantages and limitations of current techniques are analyzed,and potential future directions are discussed.This review aims to highlight the potential of deep learning in thyroid nodule diagnosis and to provide a foundation for selecting feasible pathways for future clinical applications.
作者
周鑫源
邱敏
商江峰(综述)
魏国辉(审校)
ZHOU Xinyuan;Qiu Min;SHANG Jiangfeng;WEI Guohui(School of Medical Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan 250355,P.R.China;Department of Thyroid Surgery,Affiliated Hosipital of Jining Medical University,Jining,Shandong 272007,P.R.China;Department of Thyroid and Breast Surgery,Changshu Hospital Affiliated to Suzhou University(Changshu NO.1 People's Hospital),Suzhou,Jiangsu 215500,P.R.China)
出处
《生物医学工程学杂志》
北大核心
2025年第5期1069-1075,共7页
Journal of Biomedical Engineering
基金
国家自然科学基金(61702087)
山东省自然科学基金面上项目(ZR2022MH203)
山东省研究生优质教育教学资源项目(SDYKC2023044)
山东中医药大学教育教学研究课题(实验教学专项)(SYJX2022013)。
