计算机应用与软件2018，Vol.35Issue(4)：220-226,7.DOI:10.3969/j.issn.1000-386x.2018.04.042
基于多通路CNN的多模态MRI神经胶质瘤分割
MULTI-MODALITY MRI GLIOMAS SEGMENTATION BASED ON MULTI-CHANNEL CNN
摘要
Abstract
Convolutional Neural Networks (CNN) tends to lose the global information of magnetic resonance imaging (MRI) brain tumor images due to the limitation of convolution kernel scale.Convolution and pooling process can lead to the loss of some information in the shallow layer of the network,resulting in insufficient segmentation information of brain tumor based on CNN and poor segmentation accuracy.To solve the above problems,a multi-channel CNN model with global access and shallow information in the network was proposed to accomplish the automatic segmentation of multi-modality MRI brain gliomas.The algorithm sliced the 3D multi-modality MRI image into axial slices,and selected the scale of 33 × 33 image blocks on the same slice sequence to get the training set.The training set image block was input into the multi-channel CNN model for training.The test set was input into the trained model,and the brain tumor was correctly segmented from the MRI images of the brain,and was divided into four areas of necrosis,edema,enhancement and non-enhancement.The model was used to assess the quality of the model by evaluating the parameters Dice coefficient,sensitivity coefficient and specificity coefficient.Experimental results show that the proposed method is simple and effectively accomplished the task of brain tumor segmentation.关键词
多模态磁共振成像/神经胶质瘤/浅层信息/全局信息/多通路卷积神经网络/全自动分割Key words
Multi-modality MRI/Gliomas/Shallow information/Global information/Multi-channel CNN/Automatic segmentation分类
信息技术与安全科学引用本文复制引用
朱婷,王瑜,肖洪兵,曹利红..基于多通路CNN的多模态MRI神经胶质瘤分割[J].计算机应用与软件,2018,35(4):220-226,7.基金项目
国家自然科学基金项目(61671028) （61671028）
北京市自然科学基金面上项目(4162018) （4162018）
北京市委组织部“高创计划”青年拔尖人才培养项目(2014000026833ZK14) （2014000026833ZK14）
北京市属高等学校高层次人才引进与培养计划项目(CIT&TCD201504010) （CIT&TCD201504010）
2017年研究生科研能力提升计划项目. （）
