[1]高宇飞,马自行,徐 静,等.基于卷积和可变形注意力的脑胶质瘤图像分割[J].郑州大学学报(工学版),2024,45(02):27-32.[doi:10.13705/j.issn.1671-6833.2023.05.007]
　GAO Yufei,MA Zixing,XU Jing,et al.Brain Glioma Image Segmentation Based on Convolution and Deformable Attention[J].Journal of Zhengzhou University (Engineering Science),2024,45(02):27-32.[doi:10.13705/j.issn.1671-6833.2023.05.007]
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基于卷积和可变形注意力的脑胶质瘤图像分割()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年02期
页码:
27-32
栏目:
出版日期:
2024-03-06

文章信息/Info

Title:
Brain Glioma Image Segmentation Based on Convolution and Deformable Attention
作者:
高宇飞;  马自行;  徐 静;  赵国桦;  石 磊
1. 郑州大学 网络空间安全学院, 河南 郑州 450002;2. 嵩山实验室, 河南 郑州 450052;3. 郑州大学 计算机与人工 智能学院, 河南 郑州 450001;4. 郑州大学第一附属医院, 河南 郑州 450003
Author(s):
GAO Yufei;  MA Zixing;  XU Jing;  ZHAO Guohua;  SHI Lei
1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, China; 2. Songshan Laboratory, Zhengzhou 450052, China; 3. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China; 4. The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450003, China
关键词:
深度学习;  脑胶质瘤图像分割;  卷积神经网络;  Transformer;  自注意力机制
Keywords:
deep learning;  brain glioma image segmentation;  CNN;  Transformer;  self-attention mechanism
DOI:
10.13705/j.issn.1671-6833.2023.05.007
文献标志码:
A
摘要:
对于脑胶质瘤图像分割这类密集预测的医学影像分割任务,局部和全局依赖关系都是不可或缺的,针对卷 积神经网络缺乏建立全局依赖关系的能力,且自注意力机制在局部细节上捕捉能力不足等问题,提出了基于卷积 和可变形注意力的脑胶质瘤图像分割方法。 设计了卷积和可变形注意力 Transformer 的串行组合模块,其中卷积用 于提取局部特征,紧随其后的可变形注意力 Transformer 用于捕捉全局依赖关系,建立不同分辨率下局部和全局依 赖关系。 作为一种 CNN-Transformer 混合架构,所提方法不需要任何预训练即可实现精准的脑胶质瘤图像分割。 实验结果表明:所提方法在 BraTS2020 脑胶质图像分割数据集上平均 Dice 系数和平均 95% 豪斯多夫距离分别为 83. 56%和 11. 30 mm,达到了与其他脑胶质瘤图像分割方法相当的分割精度,同时降低了至少 50%的计算开销,有 效提升了脑胶质瘤图像分割的效率。
Abstract:
For medical image segmentation tasks such as glioma image segmentation with dense prediction, both local and global dependencies were indispensable. To address the problems that convolutional neural networks lacked the ability to establish global dependencies and the self-attention mechanism had insufficient ability to capture local details, a convolutional and deformable attention-based method for glioma image segmentation was proposed. A serial combination module of convolution and deformable attention Transformer was designed, in which convolution was used to extract local features and the immediately following deformable attention. Transformer was used to capture global dependencies to the establishment of local and global dependencies at different resolutions. As a hybrid CNN-Transformer architecture, the method could achieve accurate brain glioma image segmentation without any pretraining. Experiments showed that the average dice score and the average 95% Hausdorff distance on the BraTS2020 glioma image segmentation dataset were 83. 56% and 11. 30 mm, respectively, achieving comparable segmentation accuracy compared with other methods, while reducing the computational overhead by at least 50% and effectively improving the efficiency of glioma image segmentation.

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更新日期/Last Update: 2024-03-08