Objective

2

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

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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

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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.