基于空洞卷积的三维并行卷积神经网络脑肿瘤分割  

Three-Dimensional Parallel Convolution Neural Network Brain Tumor Segmentation Based on Dilated Convolution

作　　者：冯博文 吕晓琪[1,2,3] 谷宇 李菁[1] 刘阳 Feng Bowen;LüXiaoqi;Gu Yu;Li Qing;Liu Yang(Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing,School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou,Inner Mongolia 014010,China;School of Information Engineering,Inner Mongolia University of Technology,Hohhot,Inner Mongolia 010051,China;School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China)

机构地区：[1]内蒙古科技大学信息工程学院内蒙古自治区模式识别与智能图像处理重点实验室,内蒙古包头014010 [2]内蒙古工业大学信息工程学院,内蒙古呼和浩特010051 [3]上海大学计算机工程与科学学院,上海200444

出　　处：《激光与光电子学进展》2020年第14期84-93,共10页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61771266,61841204,61179019);内蒙古自治区自然科学基金(2019MS06003,2015MS0604);内蒙古自治区高等学校科学研究项目(NJZY145);包头市科技计划项目(2017C1002,2015C2006-14);教育部“春晖计划”合作科研项目(教外司留[2019]1383号)。

摘　　要：针对分割核磁共振成像(MRI)三维图像中整个肿瘤病灶运算量大、过程繁复的问题,提出了一种基于深度学习的全自动分割算法。在填充锯齿状空洞的卷积通路上构建并行三维卷积神经网络,提取多尺度图像块进行训练,捕获大范围空间信息。利用密集连接的恒等映射特性,将浅层特征叠加到网络末端,在MRI多模态图像中分割出水肿区、增强区、核心区和囊化区。在BraTS 2018数据集中对该模型进行了分割测试,结果表明,该模型分割的全肿瘤区、核心区和增强区的平均Dice系数分别为0.90、0.73和0.71,与已有算法相当,且具有较高的自动化集成度。ing at the problem of large computation and complicated process of segmentation for whole tumor lesion in segmented magnetic resonance imaging(MRI)three-dimensional images,a fully automatic segmentation algorithm based on deep learning is proposed.A dual pathway three-dimensional convolutional neural network model is constructed on the dilated convolution path filled with jagged holes to extract multi-scale image blocks for training and capture large-scale spatial information.The shallow features are superimposed to the end of the network by using the identity mapping feature of dense connection.The swollen area,enhanced area,core area,and cystic area are segmented in the multi-modal MRI image.The model is segmented and tested in the BraTS 2018 dataset.The results show that the average Dice coefficients of the whole tumor area,core area and enhanced tumor area segmented by the model are about 0.90,0.73 and 0.71,respectively,which is equal to the performance of the current algorithms and has a high degree of automation integration.

关 键 词：三维图像处理 脑肿瘤 空洞卷积 密集连接 BraTS 2018数据集 

分 类 号：P391.4[天文地球—地球物理学]