基于3D‑Ghost卷积神经网络的脑胶质瘤MRI图像分割算法3D‑GA‑Unet

3D-GA-Unet:MRI image segmentation algorithm for glioma based on 3D-Ghost CNN

脑胶质瘤是由于大脑和脊髓胶质癌变产生的、最常见的原发性颅脑肿瘤,其中恶性脑胶质瘤占比大且致死率高。利用磁共振成像(MRI)图像对脑胶质瘤定量分割和分级是目前诊治脑胶质瘤的主要方法。为提升脑胶质瘤的分割精度与速度,提出一种基于3D-Ghost卷积神经网络(CNN)的脑胶质瘤MRI图像分割算法:3D-GA-Unet。3DGA-Unet以3D U-Net(3D U-shaped Network)为基础框架,设计基于3D-Ghost CNN模块,利用线性运算增加有用信息输出、减少传统CNN中的冗余特征;添加基于坐标注意力(CA)的模块,利于获取更多于分割精度有利的图像信息。在公共脑胶质瘤数据集BraTS2018进行训练和验证,实验结果表明,3D-GA-Unet脑胶质瘤分割结果的周围水肿区域(WT)、坏死核心区域(TC)和增强肿瘤区域(ET)的平均Dice相似系数(DSC)分别达到0.8632、0.8473和0.8036,平均敏感度分别达到0.8676、0.9492和0.8315。3D-GA-Unet能精准分割脑胶质瘤图像,进一步提升分割效率,对脑胶质瘤的临床诊断有积极的意义。

Gliomas are the most common primary cranial tumors arising from cancerous changes in the glia of the brain and spinal cord,with a high proportion of malignant gliomas and a significant mortality rate.Quantitative segmentation and grading of gliomas based on Magnetic Resonance Imaging(MRI)images is the main method for diagnosis and treatment of gliomas.To improve the segmentation accuracy and speed of glioma,a 3D-Ghost Convolutional Neural Network(CNN)-based MRI image segmentation algorithm for glioma,called 3D-GA-Unet,was proposed.3D-GA-Unet was built based on 3D U-Net(3D U-shaped Network).A 3D-Ghost CNN block was designed to increase the useful output and reduce the redundant features in traditional CNNs by using linear operation.Coordinate Attention(CA)block was added,which helped to obtain more image information that was favorable to the segmentation accuracy.The model was trained and validated on the publicly available glioma dataset BraTS2018.The experimental results show that 3D-GA-Unet achieves average Dice Similarity Coefficients(DSCs)of 0.8632,0.8473 and 0.8036 and average sensitivities of 0.8676,0.9492 and 0.8315 for Whole Tumor(WT),Tumour Core(TC),and Enhanced Tumour(ET)in glioma segmentation results.It is verified that 3D-GA-Unet can accurately segment glioma images and further improve the segmentation efficiency,which is of positive significance for the clinical diagnosis of gliomas.