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山东大学学报 (医学版) ›› 2020, Vol. 1 ›› Issue (8): 42-49, 73.doi: 10.6040/j.issn.1671-7554.0.2020.0391

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基于深度学习的脑肿瘤图像分割

刘琚1,2,*(),吴强1,2,于璐跃1,林枫茗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:2020-03-20 出版日期:2020-08-07 发布日期:2020-08-07
  • 通讯作者: 刘琚 E-mail:juliu@sdu.edu.cn
  • 作者简介:刘琚，工学博士，教授、博士研究生导师。现任中国电子学会高级会员、IEEE高级会员，《电子与信息学报》《数据采集与处理》和《山东大学学报(理学版)》等编委，《International Journal of Digital Crime and Forensics》等副编辑；曾获批教育部“新世纪优秀人才支持计划”、济南市优秀创新团队领军人物和海信数字多媒体技术国家重点实验室特聘教授。曾作为访问教授或高级研究学者在西班牙、美国、英国、德国和日本等国的不同大学进行通信信号处理和医学图像处理等方面的合作研究。主要研究方向包括“智能信号处理理论与应用”、“无线通信中空时信号处理技术”、“多媒体通信与网络传输技术”等。先后主持承担了国家重点研发计划、国家自然科学基金、高等学校科技创新工程重大项目培育资金项目和山东省重大科技创新工程项目等课题。获省部级科技进步奖4项、授权发明专利20余项、学术专著2部，在国内外核心期刊或重要学术会议上发表学术论文200余篇
  • 基金资助:
    山东省自主创新及成果转化专项计划(2015ZDXX0801A01);山东省重点研发计划(2017CXGC1504);山东大学自主创新基金(自然科学专项);山东大学自主创新基金(2015QY001-05)

Brain tumor image segmentation based on deep learning techniques

Ju LIU1,2,*(),Qiang WU1,2,Luyue YU1,Fengming LIN1   

  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:2020-03-20 Online:2020-08-07 Published:2020-08-07
  • Contact: Ju LIU E-mail:juliu@sdu.edu.cn

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摘要：

人工智能技术在计算机视觉与深度学习领域的应用逐渐增多，自动驾驶、无人机、医学临床诊疗等行业都需要基于深度学习的图像分割技术做支撑。本文对近年来脑肿瘤图像分割方法进行综述：首先介绍了图像分割的传统方法和基于深度学习的方法，然后概述了目前几种典型的针对脑肿瘤图像分割方法，描述其主要进展与可借鉴之处，总结了我们在基于深度学习的脑肿瘤图像分割方面的研究结果，并与典型方法的性能进行对比，最后讨论未来研究方向及面临的挑战。

关键词: 人工智能, 深度学习, 图像分割, 脑肿瘤图像, 神经网络

Abstract:

Artificial intelligence technology is widely applied in the field of computer vision and deep learning. Image segmentation technology based on deep learning is essential in industries such as autonomous driving, drones, and clinical diagnosis and treatment. This paper reviews the methods of brain tumor image segmentation, including the traditional methods of image segmentation and methods based on deep learning and some typical methods. The paper also compares our research advances with the typical methods and discusses future research directions and challenges.

Key words: Artificial intelligence, Deep learning, Image segmentation, Brain tumor image, Neural network

中图分类号: 

  • R574

图1

空洞卷积结构[13]"

图2

特征金字塔结构[15]"

图3

DANet的模块结构[17]"

图4

使用生成对抗结构的分割网络[24]"

图5

ShuffleSeg网络架构[28]"

图6

U-Net用于脑瘤数据分割"

图7

Attention R2U-Net的网络结构[33]"

图8

网络结构可视化示意图[35]"

表1

与BRATS2018测试集的现状比较[40]"

BraTS2018Dice
WT TC ET
VGG 0.872 4 0.693 3 0.659 8
DUNet 0.863 1 0.688 4 0.620 5
FCNN 0.868 7 0.653 7 0.582 8
HPUNet 0.893 0 0.810 8 0.755 8
baseline(HPUNet+ResNet) 0.892 5 0.781 1 0.746 3
baseline+FCU 0.895 4 0.809 1 0.778 3
baseline+FCU+MIMU 0.895 6 0.831 3 0.806 7
baseline+FCU+MIMU+SIMU 0.901 9 0.837 4 0.817 6
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