摘要
脑胶质瘤是起源于脑神经胶质细胞的原发性肿瘤,发病率约占全部颅内肿瘤的45%,其核磁共振图像(MRI)的精准分割有着非常重要的临床意义。文中提出了一种基于多编码器的脑肿瘤自动分割方法,模型采用U型网络结构,扩充单收缩路径为多路径以深度挖掘不同模态语义信息;提出结合空洞卷积的Inception模块作为网络基础卷积层以获取图像多尺度特征;在网络瓶颈层和解码器中引入轻量型通道注意力Efficient Channel Attention(ECA)模块,使得模型更多地关注与分割相关的信息,忽略通道维度的信息冗余,从而进一步提高网络分割的精确率。在Brain Tumor Segmentation Challenge 2018(BraTS 2018)数据集上进行验证,提出的网络在整体肿瘤、肿瘤核心、增强肿瘤3个区域的平均Dice系数分别为0.880,0.784,0.757,结果表明所提算法实现了准确有效的多模态MRI脑肿瘤分割。
Glioma is a primary tumor originating from glial cells in the brain,accounting for about 45%of all intracranial tumors.Accurate segmentation of brain tumor in magnetic resonance imaging(MRI)images is of great clinical significance.In this paper,an automatic brain tumor segmentation method based on multi-encoder architecture is proposed.The model adopts a U-shaped network structure which expands the single contracting path into multiple paths to deeply exploit semantic information of diffe-rent modalities.In order to obtain the multiscale features of images,an inception module combined with dilated convolution is designed as the basic convolutional layer;a lightweight attention mechanism known as efficient channel attention(ECA)block is then introduced into the bottleneck layer and the decoder,so that the model pays more attention to the segmentation-related information and ignores the redundancy of the channel dimension,thereby further improving the segmentation results.Using the Brain Tumor Segmentation Challenge 2018(BraTS 2018)dataset for verification,the proposed model gets average Dice coefficient values of 0.880,0.784,and 0.757 for the whole tumor,tumor core and enhancing tumor respectively.Experiment results show that the proposed method achieves accurate and effective multimodal MRI brain tumor segmentation.
作者
戴天虹
宋洁绮
DAI Tianhong;SONG Jieqi(College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin 150040,China)
出处
《计算机科学》
CSCD
北大核心
2023年第S01期509-514,共6页
Computer Science
基金
中央高校基本科研业务费专项资金(2572019CP17)
黑龙江省自然科学基金(C201414)
哈尔滨市科技创新人才项目(2014RFXXJ086)。
关键词
图像处理
脑肿瘤分割
多编码器
空洞卷积
通道注意力
Image processing
Brain tumor segmentation
Muti-encoder
Dilated convolution
Channel attention mechanism
