多模态融合的深度学习脑肿瘤检测方法

针对目前传统方法脑肿瘤检测准确率低的问题, 提出一种基于深度学习的三维脑肿瘤检测方法.首先将不同模态的脑肿瘤磁共振成像影像进行融合, 获取不同模态下的脑肿瘤病灶三维空间特征; 然后在卷积层和池化层之间增加实列归一化层, 提高网络的收敛速度, 缓解过拟合的问题; 并对损失函数进行改进, 采用加权损失函数加强对病灶区域的特征学习; 最后结合后处理方法解决假阳脑肿瘤病灶多的问题.实验结果表明：提出的脑肿瘤检测方法可有效进行肿瘤病灶定位; 相关性系数、敏感性和特异性三种评价指标分别达到了0.926 7、0.928 1和0.997 7, 与二维检测网络相比, 提高了4.6%、3.96%和0.04%, 较初始的单模态脑肿瘤检测方法提升了13.2%、10.42%和0.12%.

Abstract

Aiming at the low accuracy of traditional brain tumor detection, a three-dimensional brain tumor detection method based on deep learning was proposed. Firstly, the magnetic resonance images of different modal brain tumors were fused to obtain the three-dimensional features of brain tumor focus under different modalities. Then, an instance normalization layer was added between the convolution layer and the pooling layer to improve the convergence speed of the network and relieve the problem of overfitting. And the loss function was improved, the weighted loss function was used to enhance the feature learning of the focus area. Finally, the problem of more focuses in the false positive brain tumor was solved combining with the post-processing method. The experimental results show that the proposed brain tumor detection method can effectively detect the tumor focuses. The Dice coefficient, sensitivity and specificity of the three evaluation indexes reach 0.926 7, 0.928 1 and 0.997 7 respectively. The three indicators improve 4.6%, 3.96% and 0.04% compared with the 2D detection network, and improve 13.2%, 10.42% and 0.12% compared with the initial single modal brain.

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姚红革, 沈新霞, 李宇, 喻钧, 雷松泽. 多模态融合的深度学习脑肿瘤检测方法[J]. 光子学报, 2019, 48(7): 0717001. YAO Hong-ge, SHEN Xin-xia, LI Yu, YU Jun, LEI Song-ze. Multi-modal Fusion Brain Tumor Detection Method Based on Deep Learning[J]. ACTA PHOTONICA SINICA, 2019, 48(7): 0717001.

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