郭彤宇,王博,刘悦,魏颖(东北大学信息科学与工程学院, 沈阳 110004;东北大学信息科学与工程学院, 沈阳 110004;教育部医学影像计算重点实验室, 沈阳 110004)
目的 卷积神经网络方法可以提取到图像的深层次信息特征，在脑部磁共振图像（MRI）分割领域展现出优秀的性能。但大部分深度学习方法都存在参数量大，边缘分割不准确的问题。为克服上述问题，本文提出一种多通道融合可分离卷积神经网络（MFSCNN）模型分割脑图像。方法 首先，在训练集中增加待分割脑结构及其边缘像素点的权重，强制使网络学习如何分割脑结构边缘部分，从而提升整体脑结构分割的准确率。其次，引入残差单元，以避免梯度弥散，同时使用深度可分离卷积代替原始的卷积层，在不改变网络每个阶段特征通道数的情况下，减少了网络训练的参数数量和训练时间，降低了训练成本。最后，将不同阶段的特征信息合并在一起，进行通道混洗，得到同时包含深浅层次信息的增强信息特征，加入到网络中进行训练，每个阶段的输入特征信息更丰富，学习特征的速度和收敛速度更快，显著地提升了网络的分割性能。结果 在IBSR（internet brain segmentation repositor）数据集上的分割结果表明，MFSCNN的分割性能相对于普通卷积神经网络（CNN）方法要明显提高，且在边缘复杂的部分，分割效果更理想，Dice和IOU（intersection over union）值分别提升了0.9% 6.6%，1.3% 9.7%。在边缘平滑的部分，MFSCNN方法比引入残差块的神经网络模型（ResCNN）和引入局部全连接模块的神经网络模型（DenseCNN）分割效果要好，而且MFSCNN的参数量仅为ResCNN的50%，DenseCNN的28%，在提升分割性能的同时，也降低了运算复杂度，缩短了训练时间。同时，在IBSR、Hammer67n20、LPBA40这3个数据集上，MFSCNN的分割性能比现有的其他主流方法更出色。结论 本文提出的MFSCNN方法，加强了网络特征的信息量，提升了网络模型的训练速度，在不同数据集上均获得更精确的MR脑部图像分割结果。
Multi-channel fusion separable convolution neural networks for brain magnetic resonance image segmentation
Guo Tongyu,Wang Bo,Liu Yue,Wei Ying(College of Information Science and Engineering, Northeastern University, Shenyang 110004, China;College of Information Science and Engineering, Northeastern University, Shenyang 110004, China;Key Laboratory of Medical Imaging Calculation of the Ministry of Education, Shenyang 110004, China)
Objective CNN (convolution neural network) shows excellent performance in the field of brain magnetic resonance image segmentation because of its ability to extract the deep information features of the image. However, the majority of deep learning methods have the problems of too many parameters and inaccurate result of edge segmentation. To overcome these problems, this study proposes a multi-channel fusion separable convolution neural network (MFSCNN). Method First, the weight of the brain structure and its edge pixels are increased in the training set to make the network acquire numerous features of the brain structure and its edge in training. The network is also forced to learn how to segment the edge part of the brain structure to improve the accuracy of the entire brain structure segmentation. Second, the residual unit is introduced to allow the network to transfer the derivative back to the network by jumping connections between the layers of the residual network. While deepening the network, the gradient dispersion can be avoided, which makes up for the lack of information loss in information transmission. The deep separable convolution is used to replace the original convolution layer, and the depth is used to replace the width. Without changing the number of characteristic channels in each stage of the network, the number of network parameters, the number of network training parameters, the training cost, and the training time of the network are reduced. Finally, the feature information of different stages is merged, and the channel is shuffled to obtain the enhanced information features containing deep and shallow information. The features are then placed into the network for training. The input feature information of each stage is richer, the learning feature is faster, and the convergence is faster; so the performance of the brain image segmentation based on the network is obviously improved. Content of main experiment and result For IBSR data sets, the results of MFSCNN are compared with those of ordinary convolutional neural network model (CNN), neural network model with residual unit (ResCNN), and neural network model with local full connection (DenseCNN). The network structure is divided into four stages, and each stage is a specific unit. In training and testing, 75% of the samples are selected as training set and 25% as test set. Dice and IOU (intersection cver union) values are used to measure the accuracy of image segmentation. Dice value can measure the similarity between the segmentation and gold standard results. IOU value reflects the coincidence degree between the segmentation and gold standard results. The results of MFSCNN are significantly higher than those of CNN. In the complex part of the edge, the performance of segmentation is improved obviously. The Dice and IOU are increased by 0.9%6.6% and 1.3%9.7% respectively. In the edge smoothing part, MFSCNN is better than the deep network ResCNN and DenseCNN in terms of the segmentation effect. Moreover, the parameters of MFSCNN are only 50% of ResCNN and 28% of DenseCNN, which not only improves the segmentation performance but also reduces the computational complexity and training time. Comparisons with reviewed research. In the performance on the IBSR, Hammer67n20, and LPBA40, the segmentation results of MFSCNN are better than those of other existing methods. MFSCNN is more prominent in the segmentation of the hippocampus. Compared with commonly used segmentation software FIRST and FreeSurfer, the average Dice values of the putamen and caudate nucleus are increased by 3.4% and 8%, respectively. For the popular methods, the values of Brainsegnet and MSCNN+LC (label consistenay) are increased by 1.6%4.4% and 2.6%2.7%, respectively. Conclusion The proposed MFSCNN method can form a friendly initialization training set for brain structure segmentation by increasing the weight of the interested brain structure and its edge pixels in the training set. When training the network, the deep separable convolution structure is used instead of the original convolution layer, thereby reducing the amount of network training parameters. The feature maps of each stage are merged, and the channels are shuffled to obtain enhanced information features containing deep and shallow information, thereby improving the accuracy of network model segmentation. MFSCNN not only solves the problem of inaccurate segmentation of complex edges of the brain structure by traditional CNN but also improves the inaccurate segmentation of the lateral edges of the brain structure by ResCNN and DenseCNN. In addition, for different data sets, accurate segmentation results of MR brain images can be obtained. Meanings: The regional contrast of MR image is low, and the gray value of each structure is similar. Therefore, fusion information can be extracted directly from MR image by the proposed MFSCNN method and further applied to other MR image segmentation. Although MFSCNN achieves good results for deep brain structure segmentation, the accuracy of segmentation for the discontinuous part of the brain structure still needs to be improved mainly because of the complex and discontinuous types of pixels on the edges of these parts. Therefore, how to extract features that can segment complex edge contours by using deep convolution network is a problem that needs to be studied in the future.