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山东大学学报 (医学版) ›› 2020, Vol. 58 ›› Issue (8): 81-87.doi: 10.6040/j.issn.1671-7554.0.2020.0598

• 脑科学与类脑智能研究专题 • 上一篇    下一篇

基于机器学习的脑胶质瘤多模态影像分析

吴强1,2,*(),何泽鲲1,刘琚1,2,崔晓萌1,孙双1,石伟1   

  1. 1. 山东大学信息科学与工程学院，山东 青岛 266237
    2. 山东大学脑与类脑科学研究院，山东 济南250012
  • 收稿日期:2020-04-15 出版日期:2020-08-07 发布日期:2020-08-07
  • 通讯作者: 吴强 E-mail:wuqiang@sdu.edu.cn
  • 作者简介:吴强，工学博士，硕士研究生导师，现任山东大学信息科学与工程学院副教授、脑与类脑科学研究院副院长、中国计算机学会人工智能与模式识别专业委员会通讯委员、山东省人工智能学会理事。主要研究兴趣包括大数据环境下的机器学习理论与应用、医学影像处理、生物医学信号处理、仿脑计算理论。近年来在国际期刊和会议上共发表SCI/EI论文20余篇，申请发明专利10余项，授权3项，先后主持和参与国家自然科学基金、科技部重点研发计划及省部级课题10余项。曾担任多个国际重要期刊和会议论文的评阅人，国家自然科学基金通讯评审专家
  • 基金资助:
    山东大学基本科研业务费专项资金(2017JC013);山东省重大创新工程(2017CXGC1504)

A research on multi-modal MRI analysis based on machine learning for brain glioma

Qiang WU1,2,*(),Zekun HE1,Ju LIU1,2,Xiaomeng CUI1,Shuang SUN1,Wei SHI1   

  1. 1. School of Information Science and Engineering, Shandong University, Qingdao 266237, Shandong, China
    2. Institute of Brain and Brain-Inspired Science, Shandong University, Jinan 250012, Shandong, China
  • Received:2020-04-15 Online:2020-08-07 Published:2020-08-07
  • Contact: Qiang WU E-mail:wuqiang@sdu.edu.cn

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摘要：

脑部胶质瘤是临床中常见的一种原发性脑肿瘤，具有复发率高、死亡率高以及治愈率低的特点。常规临床诊断主要依靠计算机断层扫描(CT)和磁共振成像(MRI)检查技术进行鉴别。随着成像技术和机器学习方法的不断发展，多模态影像智能分析技术已经逐步成为研究热点，在脑胶质瘤的病灶分割测量、肿瘤分级、预后生存周期预测和基因型辨别等方面具有重要的应用前景。本文重点介绍基于机器学习和多模态影像在脑胶质瘤临床辅助诊断和预后评估中的应用进展。

关键词: 脑部胶质瘤, 机器学习, 多模态磁共振影像, 影像病灶分割, 生存周期预测, 基因型预测

Abstract:

Brain glioma, a common primary brain tumor, has characteristics of high recurrence rate, high death rate and low cure rate. Conventional clinical diagnosis mainly depends on CT and MRI. With the development of imaging technology and machine learning methods, multi-modal image intelligent analysis technology has gradually become a research hotspot, which has an important application prospect in brain glioma lesion segmentation and measurement, tumor classification, overall survival prediction and genotype identification. This paper updates the application of machine learning and multi-modal imaging in the clinical diagnosis and prognosis of brain glioma.

Key words: Brain Glioma, Machine Learning, Multi-modal MRI, Image lesion segmentation, Survival prediction, Genotype prediction

中图分类号: 

  • R574

图1

U-Net网络结构示意图[13]"

图2

多特征提取网络结构示意图[14]"

图3

多特征提取单元、注意力单元、跨层连接等结构示意图[14]"

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