A Multi-Scale Lightweight Brain Glioma Image Segmentation Network

doi:10.12141/j.issn.1000-565X.220042

Abstract

Abstract:

Manual segmentation of brain tumor areas in magnetic resonance imaging (MRI) images is time-consuming and laborious, and it can be easily influenced by individual subjectivity. To reliably and efficiently segment brain tumors semi-automatically or automatically is particularly important for medically assisted diagnosis. In recent years, convolutional neural network-based methods for automatic segmentation of brain tumor images have made great progress, but the existing methods still cannot effectively fuse features in terms of large-scale contours and small-scale texture details of tumor images, and the rich global background information is ignored during trai-ning. In view of these problems, this paper proposed a multi-scale lightweight brain tumor image segmentation network MSL-Net. First, the base convolution in the U-Net network was replaced with an improved hierarchical decoupled convolution to expand the perceptual field while efficiently exploring multi-scale multi-view spatial information. Then, a bidirectional feature pyramid network structure was introduced at the skipping connection to fuse multi-scale features, and a hybrid loss function combining the generalized Dice loss function and the Focal loss function was used to improve segmentation accuracy and accelerate convergence in the case of pixel count imba-lance between tumor and non-tumor regions. Experimental results on the BraTS 2019 dataset show that the Dice similarity coefficients of the proposed MSL-Net network in the overall tumor region, core tumor region and enhanced tumor region are 0.900 3, 0.830 6 and 0.777 0, respectively, and the number of parameters and computation (floating-point operations per second) are 3.9×10⁵ and 3.16×10¹⁰, respectively. Compared with the current state-of-the-art methods, the method proposed in the paper achieves high segmentation accuracy while achieving light weight.

Key words: brain tumor, image segmentation, dilated convolution, feature fusion, hybrid loss function, multi-scale, artificial intelligence

CLC Number:

TP391

YANG Jinsheng, CHEN Hongpeng, GUAN Xin, et al. A Multi-Scale Lightweight Brain Glioma Image Segmentation Network[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 132-141.

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网络结构	D_D，ET	D_D，WT	D_D，TC	D_H，ET	D_H，WT	D_H，TC
HDC	0.773 6	0.893 0	0.818 3	4.188 0	6.744 1	7.977 2
HDFU	0.771 9	0.893 8	0.818 7	3.764 4	7.359 6	7.267 3
HDFU+DHDFU	0.775 1	0.893 5	0.824 7	3.890 2	6.351 2	6.438 3
HDFU+DHDFU+DBiFPN	0.777 0	0.900 3	0.830 6	3.874 3	4.864 3	6.691 3

网络模型	D_D，ET	D_D，WT	D_D，TC
模型1	0.777 0	0.900 3	0.830 6
模型2	0.775 3	0.897 6	0.829 2
模型3	0.774 8	0.897 7	0.826 3

网络模型	D_D，ET	D_D，WT	D_D，TC
模型1	0.777 0	0.900 3	0.830 6
模型4	0.774 1	0.898 8	0.828 1
模型5	0.771 2	0.895 7	0.819 0

网络模型	D_D，ET	D_D，WT	D_D，TC
模型1	0.777 0	0.900 3	0.830 6
模型6	0.7745	0.893 7	0.826 1
模型7	0.777 5	0.898 8	0.830 2

损失函数	D_D，ET	D_D，WT	D_D，TC	D_H，ET	D_H，WT	D_H，TC
Focal 损失	0.775 9	0.898 2	0.825 9	3.872 4	5.630 7	6.576 5
广义Dice损失	0.776 9	0.899 3	0.829 0	3.930 1	4.409 3	6.814 5
混合损失	0.777 0	0.900 3	0.830 6	3.874 3	4.864 3	6.691 3