摘要
准确的医学图像多器官分割对临床应用和医药发展意义重大。然而,传统基于手工特征设计的图像处理方法难以处理图像中的组织纹理和复杂形态。近年来,随着人工智能的兴起,端到端的深度学习方法展现出在自动化医学图像分析方面的强大潜力。尤其是基于卷积神经网络和Transformer的U-Net系列网络,实现了对医学数据的精确语义分割,更在临床决策和疗效评估中提高了诊断和治疗的准确性。简介目前基于深度学习的医学图像多器官分割算法,重点关注U-Net系列网络的发展及多器官分割在医药领域的应用。
Accurate multi-organ segmentation in medical images holds significant importance for various clinical application and pharmaceutical development.However,traditional image processing methods based on manual feature design struggle with the complex textures and shapes in medical images.In recent years,with the rise of artificial intelligence,end-to-end deep learning approaches have demonstrated powerful potential in automated medical image analysis.Particularly,U-Net series of networks based on convolutional neural networks and Transformers have achieved precise semantic segmentation of medical data,which has significantly enhanced the accuracy of medical diagnosis and treatment in clinical decision-making and efficacy assessment.This paper reviews current deep learning-based algorithms for organ segmentation in medical images,focusing on the development of U-Net family networks and the application of multiorgan segmentation in medical advancements.
作者
吕朋举
王澄
朱建军
陆建
朱海东
滕皋军
LYU Pengju;WANG Cheng;ZHU Jianjun;LU Jia;ZHU Haidong;TENG Gaojun(Hanglok-Tech Co.,Ltd.,Hengqin 519000,China;Department of Interventional and Vascular Surgery,Zhongda Hospital,Southeast University,Nanjing210009,China)
出处
《药学进展》
CAS
2023年第10期751-757,共7页
Progress in Pharmaceutical Sciences
基金
国家自然科学基金重点项目(No.82130060)
重大仪器研制项目(No.81827805)
创新群体(No.61821002)
国家重点研发计划变革性技术项目(No.2018YFA0704100,No.2018YFA0704104)
江苏省影像医学与介入治疗医学创新中心项目(No.CXZX202219)
江苏高校放射医学协同创新中心、南京市科技局生命健康科技专项医工结合项目(No.202205045)。
关键词
医学图像
多器官分割
人工智能
U-Net
深度学习
medical image
multi-organ segmentation
artificial intelligence
U-Net
deep learning