基于机器学习的超声造影分析综述  

Contrast-Enhanced Ultrasound Analysis Based on Machine Learning:A Survey

作　　者：万鹏[1] 刘晗 赵俊勇 薛海燕 刘春蕊 邵伟 孔文韬 张道强[1] WAN Peng;LIU Han;ZHAO Junyong;XUE Haiyan;LIU Chunrui;SHAO Wei;KONG Wentao;ZHANG Daoqiang(College of Computer Science and Technology,Nanjing University of Aeronautics&Astronautics,Nanjing 211106,China;Department of Ultrasound,Nanjing Drum Tower Hospital,Nanjing 210008,China)

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京211106 [2]南京鼓楼医院超声诊断科,南京210008

出　　处：《数据采集与处理》2023年第4期741-758,共18页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(62136004,62276130);江苏省重点研发计划(BE2022842)。

摘　　要：超声造影(Contrast-enhanced ultrasound,CEUS)通过外周静脉注入超声造影剂,显著增强来自肿瘤微血管的血流信号,便于临床医生以实时、动态的方式评估肿瘤血管生成、周边浸润等,广泛应用于多器官病变诊断、预后评估和治疗方案规划等方面。近年来,以深度学习为代表的机器学习方法快速发展,为动态超声造影智能分析带来新的机遇。深度学习方法很大程度上拓宽了超声造影临床应用范围,提高了其诊疗效能。但与常规超声影像类似,超声造影仍然存在斑点噪声、呼吸运动干扰和标准化程度低等问题,使得动态灌注时间、空间信息挖掘面临挑战。本文系统性回顾了近年来超声造影智能分析相关工作,涵盖良恶性鉴别、恶性分级、疗效预测和诊疗方案选择等方面应用,总结了当前影像组学及深度学习方法在超声造影分析领域的最新进展,并指出当前研究的局限性和未来发展方向。Contrast⁃enhanced ultrasound(CEUS)is a powerful diagnostic tool that enhances blood flow signals from tumor micro⁃vessels through the peripheral venous injection of ultrasound contrast agents.This enables clinical physicians to dynamically evaluate tumor angiogenesis in real⁃time.CEUS imaging is widely used for the diagnosis,postoperative evaluation,and treatment planning of multiple organs.In recent years,deep learning techniques have made considerable progress,offering new opportunities for the intelligent analysis of dynamic CEUS.Deep learning methods have widened the scope of clinical applications largely,improving its efficacy of diagnosis and treatment.However,similar to the traditional ultrasound imaging,CEUS is faced with the challenges of interference from speckle noise,respiratory motion,and low standardization,making the analysis of spatial⁃temporal information of dynamic perfusion become difficult.This paper systematically reviews recent research on the intelligent analysis of CEUS,covering clinical applications such as benign⁃malignant differentiation,malignant grading,therapeutic prediction,and the selection of diagnosis and treatment plans.We summarize the latest advances of radiomic and deep learning methods in the area of CEUS imaging analysis,and highlight the limitations of current research and future directions for development.

关 键 词：超声造影 深度学习 影像组学 疾病诊断 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]