机构：[1]College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, People’s Republic of China[2]The Affiliated Hospital of Hebei University, Baoding, Hebei 071002, People’s Republic of China河北大学附属医院[3]Hebei University, Baoding, Hebei 071002, People’s Republic of China
Background and Objective. Automatic segmentation of MRI brain tumor area is a key step in the diagnosis and treatment of brain tumor. In recent years, the improved network based on UNet encoding and decoding structure has been widely used in brain tumor segmentation. However, due to continuous convolution and pooling operations, some spatial context information in existing networks will be discontinuous or even missing. It will affect the segmentation accuracy of the model. Therefore, the method proposed in this paper is to alleviate the lack of spatial context information and improve the accuracy of the model. Approach. This paper proposes a context attention module (multiscale contextual attention) to capture and filter out high-level features with spatial context information, which solves the problem of context information loss in feature extraction. The channel attention mechanism is introduced into the decoding structure to realize the fusion of high-level features and low-level features. The standard convolution block in the encoding and decoding structure is replaced by the pre-activated residual block to optimize the network training and improve the network performance. Results. This paper uses two public data sets (BraTs 2017 and BraTs 2019) to evaluate and verify the proposed method. Experimental results show that the proposed method can effectively alleviate the lack of spatial context information, and the segmentation performance is better than other existing methods. Significance. The method improves the segmentation performance of the model. It will assist doctors in making accurate diagnosis and provide reference basis for tumor resection. As a result, the proposed method will reduce the operation risk of patients and the postoperative recurrence rate.
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2023]版：
大类|3 区医学
小类|3 区工程：生物医学3 区核医学
最新[2025]版：
大类|3 区医学
小类|3 区工程：生物医学3 区核医学
JCR分区：
出版当年[2022]版：
Q2RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGINGQ3ENGINEERING, BIOMEDICAL
最新[2023]版：
Q1RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGINGQ2ENGINEERING, BIOMEDICAL
第一作者机构：[1]College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, People’s Republic of China
通讯作者：
推荐引用方式(GB/T 7714)：
Cao Tianyi,Wang Guanglei,Ren Lili,et al.Brain tumor magnetic resonance image segmentation by a multiscale contextual attention module combined with a deep residual UNet (MCA-ResUNet)[J].PHYSICS IN MEDICINE AND BIOLOGY.2022,67(9):doi:10.1088/1361-6560/ac5e5c.
APA：
Cao, Tianyi,Wang, Guanglei,Ren, Lili,Li, Yan&Wang, Hongrui.(2022).Brain tumor magnetic resonance image segmentation by a multiscale contextual attention module combined with a deep residual UNet (MCA-ResUNet).PHYSICS IN MEDICINE AND BIOLOGY,67,(9)
MLA：
Cao, Tianyi,et al."Brain tumor magnetic resonance image segmentation by a multiscale contextual attention module combined with a deep residual UNet (MCA-ResUNet)".PHYSICS IN MEDICINE AND BIOLOGY 67..9(2022)
