结合MRI多模态信息和3D-CNNs特征提取的脑肿瘤分割研究  

Brain Tumor Segmentation Based on MRI Multimodal Information and 3D-CNNs Feature Extraction

作　　者：杨新焕[1] 张勇 YANG Xin-huan;ZHANG Yong(Department of Radiology of Zhengzhou people's Hospital,Zhengzhou 467000,Henan Province,China)

机构地区：[1]郑州人民医院影像科,河南郑州467000 [2]郑州大学附属郑州中心医院放射科,河南郑州467000

出　　处：《中国CT和MRI杂志》2020年第9期4-6,23,共4页Chinese Journal of CT and MRI

基　　金：2016年河南省医学科技攻关计划项目(编号:201602030)。

摘　　要：目的探究结合MRI多模态信息和3D-CNNs特征提取对于脑肿瘤分割的价值。方法分析相比于未加入多模态3D-CNNs特征的方法,并对比2D-CNNs特征方法和3D-CNNs特征方法分割的结果,主要参考dice系数,假阳性率和sensitibity。结果在加入多模态3D-CNNs特征之后,患者的dice系数均有不同程度的提高,sensitibity系数也有改变,假阳性率显著得到改善;加上多模态3D-CNNs特征提取后,dice系数变为(88.26±4.65)%,显著优于多模态2D-CNNs特征提取的(83.67±4.22)%。而多模态2D-CNNs特征提取的运用甚至比单独使用灰度邻域结合haar小波低频系数的分割结果。结论基于多模态3D-CNNs特征提取的MRI脑肿瘤分割准确度高,适应不同患者不同模态之间的多变性和差异性,值得参考。Objective Explore the value of combining MRI multimodal information and 3D-CNNs feature extraction for brain tumor segmentation.Methods Compared with the method of adding multimodal 3D-CNNs,and comparing the results of 2D-CNNs feature method and 3D-CNNs feature method,the main reference is the dice coefficient,false positive rate and sensitibity.Results After adding the multimodal 3D-CNNs features,the patient’s dice coefficient increased to varying degrees,the sensitibity coefficient also changed,and the false positive rate was significantly improved.After the multimodal 3D-CNNs feature extraction,the dice coefficient became (88.26±4.65)%,significantly better than the multimodal 2D-CNNs feature extraction (83.67±4.22)%.The application of multi-modal 2D-CNNs feature extraction is even more than the segmentation result of the haar wavelet low-frequency coefficient combined with the gray neighborhood.Conclusion MRI brain tumor segmentation based on multimodal 3D-CNNs feature extraction has high accuracy and adapts to the variability and difference between different modes of different patients.It is worthy of reference.

关 键 词：3D-CNNS特征提取 MRI多模态信息 脑肿瘤分割 

分 类 号：R739.41[医药卫生—肿瘤] R445.2[医药卫生—临床医学]