计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220200108-6.doi: 10.11896/jsjkx.220200108
戴天虹, 宋洁绮
DAI Tianhong, SONG Jieqi
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。
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