Multimodal Brain Tumor MR Image Segmentation Network Fused with Attention Mechanism

被引：0
|
作者：
Wu X. [1 ]
Yang Q. [1 ]
Tang C. [1 ]
Sun J. [1 ]
机构：
[1] School of Computer Science & Technology, Henan Polytechnic University, Jiaozuo
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2023年 / 35卷 / 09期
关键词：
attention mechanism; brain tumor segmentation; multimodal images; spatial channel attention; triple attention;
D O I：
10.3724/SP.J.1089.2023.19694
中图分类号：
学科分类号：
摘要：
For the traditional multimodal MR image segmentation methods, the correlation between different modal features, the global and local features were not fully considered, which leads to the reduction of segmentation accuracy. To solve such problem, a multimodal brain tumor MR image segmentation method was proposed based on the attention mechanism. Firstly, a triple attention module was proposed to enhance the correlation between the modal features and to accurately judge the position and boundary information of the region of interest. Secondly, the spatial and channel attention module was designed to capture the global and local features of the space and channel, and enhance the learning ability of tumor tissue structure information. The experimental results on the public datasets BraTs18 and BraTs19 show that the method achieves 90.62%, 87.89%, 90.08% and 2.2583 in the Dice coefficient, precision, sensitivity and Hausdorff distance when segmenting the whole tumor, respectively, which are better than similar methods in comparison. © 2023 Institute of Computing Technology. All rights reserved.
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页码：1429 / 1438
页数：9
相关论文
共 25 条
  • [11] Zhou C H, Ding C X, Lu Z T, Et al., One-pass multi-task convolutional neural networks for efficient brain tumor segmentation, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 637-645, (2018)
  • [12] Huang Z, Xu H, Su S, Et al., A computer-aided diagnosis system for brain magnetic resonance imaging images using a novel differential feature neural network, Computers in Biology and Medicine, 121, (2020)
  • [13] Zhang D W, Huang G H, Zhang Q, Et al., Cross-modality deep feature learning for brain tumor segmentation, Pattern Recognition, 110, (2021)
  • [14] Huang Z, Zhao Y W, Liu Y H, Et al., GCAUNet: a group cross-channel attention residual UNet for slice based brain tumor segmentation, Biomedical Signal Processing and Control, 70, (2021)
  • [15] Zhang J X, Jiang Z K, Jing D, Et al., Attention gate ResU-net for automatic MRI brain tumor segmentation, IEEE Access, 8, pp. 58533-58545, (2020)
  • [16] Luo Kaikai, Wang Ting, Ye Fangfang, U-Net segmentation model of brain tumor MR image based on attention mechanism and multi-view fusion, Journal of Image and Graphics, 26, 9, pp. 2208-2218, (2021)
  • [17] Kaku A, Hegde C V, Huang J, Et al., DARTS: denseunet-based automatic rapid tool for brain segmentation
  • [18] Zhuang J T, Yang J L, Gu L, Et al., ShelfNet for fast semantic segmentation, Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 847-856, (2019)
  • [19] Sudre C H, Li W Q, Vercauteren T, Et al., Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations, Proceedings of Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 240-248, (2017)
  • [20] Lin T Y, Goyal P, Girshick R, Et al., Focal loss for dense object detection, Proceedings of the 16th IEEE International Conference on Computer Vision, pp. 2999-3007, (2017)
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