Journal of South China University of Technology(Natural Science) >
A Multi-Scale Lightweight Brain Glioma Image Segmentation Network
Received date: 2022-01-25
Online published: 2022-05-04
Supported by
the National Natural Science Foundation of China(62071323)
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.
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), 2022 , 50(12) : 132 -141 . DOI: 10.12141/j.issn.1000-565X.220042
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