LMD-UNet网络在脑肿瘤多模态MRI图像分割中的应用

doi:10.3969/j.issn.0258-8021.2023.04.006

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Abstract There is a semantic gap among the feature maps corresponding to the codec in the UNet network, and its dual roll integration layer cannot learn multi-scale information, resulting in the loss of some feature information, which affects the MRI image segmentation effect. To solve this problem, this paper proposed a new image segmentation network local residual fusion multi-scale dual branch network LMD-UNet. In the coding process, the network used local feature residuals to fuse dense blocks and multi-scale convolution modules to expand the receptive field of images and optimize the propagation of underlying visual features; and in the decoding process, the network used double branch convolution to generate new high-level semantic features to reconstruct the information lost in the coding path. For segmentation experiments, 335 cases of the public brain tumor dataset BraTs were used, and the segmentation results were compared with U-Net that is currently a mainstream segmentation network. Experimental results showed that the four objective evaluation indexes of LMD-UNet model, precision, dice, 95% HD and recall reached0.933, 0.921, 0.702 and 0.966 respectively. Compared to U-Net, the corresponding indicators increased by 6.3%, 5.7%, 1.8%, and 6.1%, respectively, which indicated that LMD-UNet achieved more precise segmentation of brain tumor images. Meanwhile, the proposed method also showed a good performance in the edge contour segmentation for the detail part, which prospectively provided guarantee for the diagnosis of brain tumor and the surgery.

Key words： image segmentation multi-scale convolution dense block dual branch convolution

Received: 06 June 2022

PACS:

R318

Corresponding Authors: ^*E-mail:cillatan0@nuist.edu.cn

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	Xia Jingming
	Tan Ling
	Liang Ying

Cite this article:

Xia Jingming,Tan Ling,Liang Ying. Application of LMD-UNET Network in Multi-Modal MRI Images Segmentation of Brain Tumors[J]. Chinese Journal of Biomedical Engineering, 2023, 42(4): 431-441.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.04.006 OR http://cjbme.csbme.org/EN/Y2023/V42/I4/431

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