双重降维通道注意力门控U-Net的胰腺CT分割

doi:10.3969/j.issn.0258-8021.2023.03.004

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Abstract Segmentation and reconstruction of pancreatic 3D model from CT images is of great significance for assisting diagnosis of the disease. Due to the small proportion of pancreas and the difficulty to distinguish pancreas from the surrounding tissues, existing methods are not accurate and stable enough. Herein we proposed a dual dimension reduction (DDR) and channel attention gate (CAG) U-shaped network. The DDR was added to the coding path to strengthen the effective information extraction in the shallow feature space, and the CAG was embedded in the skip connection to filter redundant feature information from channel level. On the public data set of pancreas segmentation published by NIH (including 82 CT samples), we evaluated the segmentation performance by dice similarity coefficient (DSC), recall (R) and precision (P), and evaluated 3D reconstruction through vertex distance error (VDE). DSC,R and P indexes reached (82.35±5.76)%, (81.07±8.50)%, and (84.04±5.40)% respectively, and VDE decreased to 1.27±0.90, which was better than that obtained from U-Net, Attention-Unet and other methods in the experiment. The experimental results showed that the method we proposed improved the segmentation performance of pancreatic CT images, and the 3D reconstruction model reflected the actual situation of individual pancreas.

Key words： image segmentation 3D reconstruction pancreas feature dimension reduction attention

Received: 16 December 2021

PACS:

R318

Corresponding Authors: ^* E-mail: yuuuuan@163.com

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	Articles by authors
	Ji Jianbing
	Chen Shu
	Yang Yuanyuan

Cite this article:

Ji Jianbing,Chen Shu,Yang Yuanyuan. Dual Dimension Reduction and Channel Attention Gate U-Shaped Network for Pancreatic CT Segmentation[J]. Chinese Journal of Biomedical Engineering, 2023, 42(3): 281-288.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.03.004 OR http://cjbme.csbme.org/EN/Y2023/V42/I3/281

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