脑肿瘤分割算法研究

doi:10.3969/j.issn.0258-8021.2022.03.005

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Abstract Due to the complexity of medical imaging and the high heterogeneity of the surface of gliomas, image segmentation of human brain gliomas is one of the most challenging tasks in medical image analysis. This paper aimed to improve the UNet++ medical image segmentation network, the improved network can fuse coarse-grained semantics and fine-grained semantics at full scale. Experiments were performed on 335 images obtained from the public BraTS brain tumor segmentation data set, using 2D and 3D comparative segmentation experiments to comprehensively evaluate the segmentation performance of the improved network and compare the segmentation results with the results of UNet, UNet++, and UNet3+ medical image segmentation networks. Among the four indicators of Dice similarity coefficient (DSC), 95% Hausdorff surface distance (HSD95),sensitivity, and positive predictive value (PPV), 2D contrast segmentation achieved the mean values of the indicators of 83.70%, 1.7, 88.40%, 84.96% respectively; the mean values of the 3D contrast segmentation reached 90.79%, 0.242, 91.23%, 91.06% respectively. Compared with the segmentation result indicators of the other three networks, in the 2D comparison experiment, DSC increased by 1.82% on average, HSD95 decreased by 0.35 on average, sensitivity increased by 2.13% on average, and PPV increased by 0.80% on average; in the 3D comparison experiment, DSC increased by 2.78% on average, HSD95 decreased by 0.076 on average, Sensitivity increased by 3.81% on average, and PPV increased by 0.68% on average. It was shown that the proposed algorithm made the segmentation result of glioma and the gold standard overlap more in the region, and completed the segmentation of glioma better. It is expected to help neurosurgeons to more precisely separate brain tumors and tissues around the brain and achieve rapid computer diagnosis and treatment.

Key words： medical image segmentation full-scale feature fusion skip connection glioma

Received: 11 May 2021

PACS:

R318

Corresponding Authors: ^*E-mail: cxd0808@imu.edu.cn

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	Articles by authors
	Zhang Shiqang
	Shi Lei
	Cheng Xiaodong

Cite this article:

Zhang Shiqang,Shi Lei,Cheng Xiaodong. Research on Brain Glioma Segmentation Algorithm[J]. Chinese Journal of Biomedical Engineering, 2022, 41(3): 290-300.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2022.03.005 OR http://cjbme.csbme.org/EN/Y2022/V41/I3/290

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