计算机与现代化Issue(12)：59-66,81,9.DOI:10.3969/j.issn.1006-2475.2023.12.011
3D-SPRNet:一种基于并行解码器和双注意力机制的胆囊癌分割模型
3D-SPRNet:Segmentation Model of Gallbladder Cancer Based on Parallel Decoder and Double Attention Mechanism
摘要
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 re-gion.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 can-cer 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 seg-mentation accuracy of gallbladder carcinoma.关键词
计算机断层扫描/胆囊癌/通道注意力机制/并行解码器/反向注意力机制Key words
computed tomography/gallbladder cancer/channel attention mechanism/parallel decoder/reverse attention mechanism分类
信息技术与安全科学引用本文复制引用
张浩洋,尹梓名,乐珺怡,沈达聪,束翌俊,杨自逸,孔祥勇,龚伟..3D-SPRNet:一种基于并行解码器和双注意力机制的胆囊癌分割模型[J].计算机与现代化,2023,(12):59-66,81,9.基金项目
上海市市级科技重大专项项目(2021SHZDZX) （2021SHZDZX）
国家重点研发计划项目(2022YFC3601101) （2022YFC3601101）
