基于多尺度卷积神经网络的磁共振成像脑肿瘤分割研究

Research on the application of brain tumor segmentation of MRI based on multi-scale convolutional neural networks

目的:针对脑肿瘤形状、位置及大小等多变性,提出一种适合磁共振成像(MRI)脑肿瘤分割的卷积神经网络模型的改进方法。方法:将卷积神经网络应用到脑肿瘤分割上,并针对脑肿瘤的特点,提出多尺度卷积神经网络模型(MSCNN),通过多尺度的输入与多尺度下的采样,克服脑肿瘤的个体差异,同时适应脑肿瘤不同图像层之间的大小位置差异,弱化肿瘤边缘与正常组织灰度相近的影响。结果:通过对30例患者的多模态磁共振图像进行分割,得到平均Dice系数为83.11%;平均灵敏度系数为89.48%;平均阳性预测值(PPV)系数为78.91%。结论:MRI脑肿瘤分割的改进方法可使分割精度得到明显提高,多尺度卷积神经网络能自适应脑肿瘤的差异性,并准确有效地分割脑肿瘤。

Objective： In view of these tumors can appear anywhere in the brain and have almost any kind of shape and size, a new segmentation method of MRI based on intelligent convolutional neural network is developed. Methods： The convolutional neural network is applied in brain tumor segmentation, according to the features of the brain tumor, the multi-scale convolutional neural network is proposed and conducted multi-scale input and multi-scale down sampling to overcome the individual differences of brain tumor. At the same time it adapted any kind of size, shape and contrast of the difference layers. Results： Data from 30 patients showed that the proposed algorithm is effective. The average Dice is 83.11%, the average sensitivity coefficientis 89.48%. the average predictive positivity value coefficient is 78.91%. Conclusion： It can improve the segmentation accuracy obviously. The multi-scale convolution neural network can adaptively the differences of brain tumor, have more effective segmentation for more images.