计算机技术与发展2024，Vol.34Issue(9)：47-54,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0161
融合卷积和Transformer的腹部多器官分割网络
Abdominal Multi Organ Segmentation Network Combining Convolution and Transformer
摘要
Abstract
Abdominal multi organ segmentation plays a crucial role in computer-aided diagnosis and has significant research value.However,due to the blurred boundaries of multiple organs in the abdomen,complex backgrounds,and variable shapes and sizes,this task is extremely challenging.To this end,TCMSUnet,a new abdominal multi organ segmentation network that integrates convolution and Transformer is proposed.Firstly,a multi-scale guided fusion module(GFM)was designed in the feature extraction stage,which utilizes the significant semantic information extracted from high-level features to guide low-level features and enhance the semantic consistency of adjacent features,thereby promoting the fusion of features at different scales.Subsequently,a global local enhancement module(GLE)was designed to enhance the model's extraction of global and local contextual information through a combination of dilated con-volution and Transformer blocks,enabling the model to establish long-range dependencies while enhancing local correlations of features.Finally,a multi-stage loss aggregation structure was introduced in the decoder section to accelerate the convergence of the model and optimize its performance.The performance of the model was evaluated on the Synapse dataset,with an average Dice similarity coefficient(DSC)of 81.20%.The experimental results show that the proposed method outperforms multiple comparison networks in overall per-formance and has better segmentation performance for organs with variable shapes and sizes.关键词
医学图像分割/特征融合/多尺度/空洞卷积/Transformer/多器官Key words
medical image segmentation/feature fusion/multi scale/dilated convolutions/Transformer/multiple organs分类
信息技术与安全科学引用本文复制引用
杨萍,陈立伟,王庆凤,周莹..融合卷积和Transformer的腹部多器官分割网络[J].计算机技术与发展,2024,34(9):47-54,8.基金项目
四川省自然科学基金项目(2022NSFSC0940,2022NSFSC0894) （2022NSFSC0940,2022NSFSC0894）
西南科技大学博士基金项目(19zx7143,20zx7137) （19zx7143,20zx7137）
