3D-SPRNet: Segmentation Model of Gallbladder Cancer Based on Parallel Decoder and Double Attention Mechanism

doi:10.3969/j.issn.1006-2475.2023.12.011

Abstract

Abstract: Abstract: The segmentation of cancerous part of gallbladder CT based on deep learning could be used as a diagnostic reference for clinicians. In existing methods， two-dimensional image slices that lack spatial context information are universally adopted as input. Meanwhile， the boundary segmentation is not accurate enough because of lacking the refinement of the cancer boundary region. In order to increase the accuracy of boundary segmentation and guarantee the continuity of spatial information， a 3D-SPRNet segmentation model for gallbladder carcinoma is proposed. A parallel decoder is used to extract and decode multi-scale advanced features. Channel attention is used to help network emphasize feature extraction information. Reverse attention is used to focus on the unpredicted region and gradually refine the cancer boundary. The CT images of 304 patients with gallbladder cancer from Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine are selected for the experiment. The MIoU， IoU and Dice coefficients obtained are 0.85， 0.70 and 0.83， respectively， which are better than those of most mainstream segmentation networks. The effectiveness of each module has been verified by ablation experiment. The experimental results show that the network model proposed in this paper can improve the problem of rough segmentation boundaries and increase the segmentation accuracy of gallbladder carcinoma.

Key words: Key words: computed tomography, gallbladder cancer, channel attention mechanism, parallel decoder, reverse attention mechanism

CLC Number:

TP391

ZHANG Hao-yang, YIN Zi-ming, LE Jun-yi, SHEN Da-cong, SHU Yi-jun, YANG Zi-yi, . 3D-SPRNet: Segmentation Model of Gallbladder Cancer Based on Parallel Decoder and Double Attention Mechanism[J]. Computer and Modernization, 2023, 0(12): 59-66.

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