Coronary artery segmentation based on Transformer and convolutional neural networks dual parallel branch encoder neural network

被引:0
|
作者
Pan, Dan [1 ]
Luo, Genqiang [1 ,2 ]
Zeng, An [3 ]
机构
[1] School of Electronics and Information Engineering, Guangdong University of Technology and Education, Guangzhou,510665, China
[2] School of Computer and Information Engineering, Guangdong Songshan Polytechnic, Guangdong, Shaoguan,512126, China
[3] School of Computers, Guangdong University of Technology, Guangzhou,510006, China
来源
Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering | 2024年 / 41卷 / 06期
关键词
Angiocardiography - Blood vessels - Computerized tomography - Convolutional neural networks - Deep neural networks - Heart - Image coding - Image segmentation - Network coding;
D O I
10.7507/1001-5515.202403058
中图分类号
学科分类号
摘要
Manual segmentation of coronary arteries in computed tomography angiography (CTA) images is inefficient, and existing deep learning segmentation models often exhibit low accuracy on coronary artery images. Inspired by the Transformer architecture, this paper proposes a novel segmentation model, the double parallel encoder u-net with transformers (DUNETR). This network employed a dual-encoder design integrating Transformers and convolutional neural networks (CNNs). The Transformer encoder transformed three-dimensional (3D) coronary artery data into a one-dimensional (1D) sequential problem, effectively capturing global multi-scale feature information. Meanwhile, the CNN encoder extracted local features of the 3D coronary arteries. The complementary features extracted by the two encoders were fused through the noise reduction feature fusion (NRFF) module and passed to the decoder. Experimental results on a public dataset demonstrated that the proposed DUNETR model achieved a Dice similarity coefficient of 81.19% and a recall rate of 80.18%, representing improvements of 0.49% and 0.46%, respectively, over the next best model in comparative experiments. These results surpassed those of other conventional deep learning methods. The integration of Transformers and CNNs as dual encoders enables the extraction of rich feature information, significantly enhancing the effectiveness of 3D coronary artery segmentation. Additionally, this model provides a novel approach for segmenting other vascular structures. © 2024 West China Hospital, Sichuan Institute of Biomedical Engineering. All rights reserved.
引用
收藏
页码:1195 / 1203
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