基于多尺度特征的脑肿瘤分割算法

doi:10.11896/jsjkx.210700217

摘要/Abstract

摘要： 脑肿瘤是除脑血管病外神经系统最常见的疾病,其分割也是医学图像处理领域的一个重要方向。准确地分割出肿瘤区域是治疗脑肿瘤的首要步骤。针对传统的全卷积神经网络多尺度处理能力弱而造成信息丢失的问题,提出了一种基于多尺度特征的全卷积神经网络用于脑肿瘤区域分割。利用空间金字塔池化可以获得多感受野的高级特征,从而捕获上下文多尺度信息,提高模型对不同尺度特征的适应能力;用残差紧密模块代替原有卷积层,可以缓解训练深度网络时的退化问题,提取更多的特征;结合数据增强技术,避免过拟合的同时最大程度地强化了模型的分割性能。在公开的低级神经胶质瘤核磁共振成像数据集上进行大量对比消融实验分析,以Dice系数、Jaccard指数和准确性作为分割性能的主要评价标准,获得了91.8％的Dice系数、85.0％的Jaccard指数和99.5％的准确性。实验结果表明,该方法能有效分割出脑肿瘤区域并具有一定的泛化性,且相比其他网络相比分割效果更好。

关键词: 多尺度特征, 核磁共振成像, 脑肿瘤分割, 全卷积神经网络, 深度学习

Abstract: Brain tumors are the most common diseases of nervous system except cerebrovascular disease,and their segmentation is also an important field in medical image processing.Accurately segmenting the tumor region is the first step in the treatment of brain tumors.Aiming at the problem of information loss caused by the weak multi-scale processing ability of traditional fully convolutional networks,a fully convolutional network based on multi-scale features is proposed.Using spatial pyramid pooling to obtain advanced features of multiple receptive fields,thereby capturing contextual multi-scale information and improving the adaptability to different scale features.Replacing the original convolution layer with the residual compact module can alleviate the degradation problem and extract more features.The data augmentation technology is combined to enhance the segmentation perfor-mance maximally while avoiding over fitting.Through a large number of contrastive ablation experiments on the public low-grade glioma MRI dataset,using Dice coefficient,Jaccard index and accuracy as the main evaluation criteria,91.8% Dice coefficient,85.0% Jaccard index and 99.5% accuracy are obtained.Experimental results show that the proposed method can effectively segment brain tumor regions and have certain generalization,and the segmentation effect is better than other networks.

Key words: Brain tumor segmentation, Deep learning, Fully convolutional network, Magnetic resonance imaging, Multi-scale features

中图分类号:

TP391

孙福权, 崔志清, 邹彭, 张琨. 基于多尺度特征的脑肿瘤分割算法[J]. 计算机科学, 2022, 49(6A): 12-16. https://doi.org/10.11896/jsjkx.210700217

SUN Fu-quan, CUI Zhi-qing, ZOU Peng, ZHANG Kun. Brain Tumor Segmentation Algorithm Based on Multi-scale Features[J]. Computer Science, 2022, 49(6A): 12-16. https://doi.org/10.11896/jsjkx.210700217

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