[1]杨萍,陈立伟,王庆凤,等.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(09):47-54.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0161]
　YANG Ping,CHEN Li-wei,WANG Qing-feng,et al.Abdominal Multi Organ Segmentation Network Combining Convolution and Transformer[J].,2024,34(09):47-54.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0161]
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融合卷积和Transformer的腹部多器官分割网络()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年09期
页码:
47-54
栏目:
媒体计算
出版日期:
2024-09-10

文章信息/Info

Title:
Abdominal Multi Organ Segmentation Network Combining Convolution and Transformer
文章编号:
1673-629X(2024)09-0047-08
作者:
杨萍1; 陈立伟1; 王庆凤1; 周莹2
1. 西南科技大学 计算机科学与技术学院,四川 绵阳 621000;2. 绵阳市中心医院 放射科,四川 绵阳 621000
Author(s):
YANG Ping1; CHEN Li-wei1; WANG Qing-feng1; ZHOU Ying2
1. School of Computer Science and Technology,Southwest University of Science and Technology,Mianyang 621000,China;2. Radiology Department,Mianyang Center Hospital,Mianyang 621000,China
关键词:
医学图像分割; 特征融合; 多尺度; 空洞卷积; Transformer; 多器官
Keywords:
medical image segmentation; feature fusion; multi scale; dilated convolutions; Transformer; multiple organs
分类号:
TP391
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0161
摘要:
腹部多器官分割在计算机辅助诊断中起着至关重要的作用,具有重要的研究价值。 但由于腹部多器官边界模糊、背景复杂以及形状大小多变,使这项任务极具挑战性。 为此,提出了一种融合卷积和 Transformer 的腹部多器官分割网络TCMSUnet。 首先,在特征提取阶段设计了多尺度引导融合模块(GFM),利用高层特征提取的显著语义信息来引导低层特征以增强相邻特征的语义一致性,从而促进不同尺度特征的融合;随后设计了全局局部增强模块(GLE),通过空洞卷积和 Transformer 块结合来增强模型对全局局部上下文信息的提取,使模型在建立长距离依赖关系的同时加强特征的局部关联性;最后,在解码器部分引入多阶段损失聚合结构以加快模型的收敛并优化模型的性能。 在 Synapse 数据集上评估了模型的性能,其平均 Dice 相似系数(DSC)为 81. 20% 。 实验结果表明,所提方法整体性能优于多种比较网络,并对形状大小多变的器官有更好的分割效果。
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.

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更新日期/Last Update: 2024-09-10