Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 6
1905-1910

Vessel wall image segmentation based on dense connection and adaptive weighted loss

Gao Hongxia1
Gao Wei2
1. School of Software, Henan University of Engineering, Zhengzhou 451191, China
2. Institute of Sciences, Information Engineering Univer-sity, Zhengzhou 450001, China
DOI: 10.19734/j.issn.1001-3695.2021.09.0440

Abstract

Aiming at the traditional deep neural network is difficult to extract effective features from vessel wall image segmentation, this paper proposed a vascular wall image segmentation method combining dense connection and adaptive weighted loss. Firstly, it constructed a densely connected segmentation network to learn more boundary and contour representations to promote feature reuse and fusion, and then designed an improved adaptive weight loss and boundary compactness loss constraint trai-ning network. It used the adaptive weighted loss to automatically adjust the loss ratio of different regions to guide the network to learn in the best direction. At the same time, it introduced the boundary compactness loss constraint to make full use of the boundary information and improve the segmentation accuracy of the blood vessel wall image. Finally, this paper performed vali-dation experiments using the MERGE blood vessel wall dataset containing 2 544 MRI. The results show that the proposed improved method can effectively extract the feature information of the vessel wall image, segmentation Dice reaches 93.65% and 95.81% in the segmentation of the lumen contour and the outer wall contour, respectively. The ablation experiment also fully proves the effectiveness of the various module, which can better achieve high-precision image segmentation.

Foundation Support

河南省高等学校青年骨干教师培养计划资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.09.0440
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 6
Section: Technology of Graphic & Image
Pages: 1905-1910
Serial Number: 1001-3695(2022)06-053-1905-06

Publish History

[2021-12-14] Accepted Paper
[2022-06-05] Printed Article

Cite This Article

高红霞, 郜伟. 融合密集连接与自适应加权损失的血管壁图像分割 [J]. 计算机应用研究, 2022, 39 (6): 1905-1910. (Gao Hongxia, Gao Wei. Vessel wall image segmentation based on dense connection and adaptive weighted loss [J]. Application Research of Computers, 2022, 39 (6): 1905-1910. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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