摘要
基于深度学习的脑图像分割算法是目前的一个研究热点。本文首先对脑图像分割的意义以及相关算法内容进行系统阐述,突出了基于深度学习的脑图像分割算法的优势。然后,本文从针对脑图像存在的问题所提出的基于深度学习的脑图像分割算法、先验知识引导的基于深度学习的脑图像分割算法和基于通用深度学习模型的脑图像分割算法三个方面,介绍近年来流行的基于深度学习的脑图像分割算法,以便相关领域的科研工作者更系统地了解目前的研究进展。最后,本文为基于深度学习的脑图像分割算法的进一步研究提供了一些建议。
Brain image segmentation algorithm based on deep learning is a research hotspot at present.In this paper,firstly,the significance of brain image segmentation and the content of related brain image segmentation algorithm are systematically described,highlighting the advantages of brain image segmentation algorithms based on deep learning.Then,this paper introduces current brain image segmentation algorithms based on deep learning from three aspects:the brain image segmentation algorithms based on problems existent to brain image,the brain image segmentation algorithms based on prior knowledge guidance and the application of general deep learning models in brain image segmentation,so as to enable researchers in relevant fields to understand current research progress more systematically.Finally,this paper provides a general direction for the further research of brain image segmentation algorithm based on deep learning.
作者
王玉丽(综述)
赵子健(审校)
WANG Yuli;ZHAO Zijian(School of Control Science and Engineering,Shandong University,Jinan 250061,P.R.China)
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2020年第4期721-729,735,共10页
Journal of Biomedical Engineering
基金
国家重点研发计划资助项目(2019YFB1301900)。
关键词
深度学习
卷积神经网络
脑图像分割
先验知识
通用深度学习模型
deep learning
convolutional neural network
brain image segmentation
prior knowledge
general deep learning models
