多参数磁共振成像(mpMRI)在前列腺癌(PCa)的无创诊断中发挥着越来越重要的作用,为了深入研究卷积神经网络在该领域的发展.首先,通过关键词prostate cancer、neural network、deep learning、image analysis,在PubMed和Web of Science数据库中进行系统的文献检索,包括卷积神经网络(CNN)出现以来的几次重大突破和近5年CNN在mpMRI应用中已发表的文献.然后,从模型的构建块出发解释CNN的设计原理,总结CNN在前列腺mpMRI诊断中的相关应用.最后,讨论了所用方法目前的局限性和未来发展前景,为医学图像分割人员提供参考,以促进CNN在前列腺mpMRI的应用发展.
Application Progress on Convolutional Neural Network in Prostate Multiparameter MRI
Multi parameter magnetic resonance imaging(mpMRI)is playing an increasingly important role in non-invasive diagnosis of pros-tate cancer(PCa),in order to further investigate the development of convolutional neural networks in this field.Firstly,a systematic literature search was conducted in PubMed and Web of Science databases using keywords such as state cancer,neural network,deep learning,and im-age analysis,including several major breakthroughs since the emergence of convolutional neural networks(CNN)and literature published in the past five years on the application of CNN in mpMRI.Then,starting from the building blocks of the model,explain the design principles of CNN and summarize the relevant applications of CNN in prostate MPMRI diagnosis.Finally,the current limitations and future development prospects of the methods used were discussed,providing reference for medical image segmentation personnel to promote the application and development of CNN in prostate MPMRI.
deep learningconvolutional neural networkprostate cancermultiparametric magnetic resonance imagingtarget detectionimage segmentation
万方数据
维普
