Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (4): 1294-1302.DOI: 10.11772/j.issn.1001-9081.2023050606
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Lijun XU, Hui LI, Zuyang LIU, Kansong CHEN(), Weixuan MA
Received:
2023-05-18
Revised:
2023-08-27
Accepted:
2023-09-01
Online:
2023-10-12
Published:
2024-04-10
Contact:
Kansong CHEN
About author:
XU Lijun, born in 1991, Ph. D., associate professor. Her research interests include computer vision, artificial intelligence, digital twin.Supported by:
通讯作者:
陈侃松
作者简介:
许立君(1991—),女,湖北武汉人,副教授,博士,CCF会员,主要研究方向:计算机视觉、人工智能、数字孪生基金资助:
CLC Number:
Lijun XU, Hui LI, Zuyang LIU, Kansong CHEN, Weixuan MA. 3D-GA-Unet: MRI image segmentation algorithm for glioma based on 3D-Ghost CNN[J]. Journal of Computer Applications, 2024, 44(4): 1294-1302.
许立君, 黎辉, 刘祖阳, 陈侃松, 马为駽. 基于3D‑Ghost卷积神经网络的脑胶质瘤MRI图像分割算法3D‑GA‑Unet[J]. 《计算机应用》唯一官方网站, 2024, 44(4): 1294-1302.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023050606
区域名 | 包含真实区域 | 掩码值 | 颜色 | 说明 |
---|---|---|---|---|
WT | 周围水肿区域(ED) | 2 | 绿色 | 边界包含脑胶质瘤的 所有肿瘤结构 |
ET | 增强肿瘤区域(ET) | 4 | 黄色 | 通过造影剂显示的 肿瘤区域 |
TC | 非增强肿瘤核(NET) 坏死核心(NCR) | 1 | 红色 | 脑胶质瘤的核心 部分,包含主要的 肿瘤结构 |
背景 | 无 | 0 | 黑色 | 图像中脑胶质瘤 区域外的其他区域 |
Tab. 1 Mask and color description for glioma in BraTS2018 dataset
区域名 | 包含真实区域 | 掩码值 | 颜色 | 说明 |
---|---|---|---|---|
WT | 周围水肿区域(ED) | 2 | 绿色 | 边界包含脑胶质瘤的 所有肿瘤结构 |
ET | 增强肿瘤区域(ET) | 4 | 黄色 | 通过造影剂显示的 肿瘤区域 |
TC | 非增强肿瘤核(NET) 坏死核心(NCR) | 1 | 红色 | 脑胶质瘤的核心 部分,包含主要的 肿瘤结构 |
背景 | 无 | 0 | 黑色 | 图像中脑胶质瘤 区域外的其他区域 |
模型 | DSC | PPV | sensitivity | Hausdorff | 参数量/106 | 收敛 时间/h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
WT | TC | ET | WT | TC | ET | WT | TC | ET | WT | TC | ET | |||
U-Net | 0.838 7 | 0.821 5 | 0.768 8 | 0.856 1 | 0.853 3 | 0.774 5 | 0.857 0 | 0.901 4 | 0.829 5 | 2.662 3 | 1.751 1 | 2.846 6 | 16.323 1 | 5.08 |
GA-Unet | 0.841 3 | 0.836 6 | 0.774 2 | 0.872 8 | 0.863 7 | 0.804 1 | 0.861 3 | 0.905 8 | 0.825 8 | 2.596 9 | 1.717 4 | 2.807 6 | 16.323 4 | 5.30 |
3D U-Net | 0.856 2 | 0.851 7 | 0.802 3 | 0.885 1 | 0.867 7 | 0.815 4 | 0.866 9 | 0.914 3 | 0.838 6 | 2.373 9 | 1.615 3 | 2.704 1 | 16.622 9 | 4.94 |
3D-GA-Unet | 0.863 2 | 0.847 3 | 0.803 6 | 0.867 3 | 0.826 9 | 0.983 1 | 0.867 6 | 0.949 2 | 0.831 5 | 2.079 3 | 1.601 2 | 2.573 8 | 16.333 0 | 4.50 |
Tab. 2 Glioma segmentation results on BraTS2018
模型 | DSC | PPV | sensitivity | Hausdorff | 参数量/106 | 收敛 时间/h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
WT | TC | ET | WT | TC | ET | WT | TC | ET | WT | TC | ET | |||
U-Net | 0.838 7 | 0.821 5 | 0.768 8 | 0.856 1 | 0.853 3 | 0.774 5 | 0.857 0 | 0.901 4 | 0.829 5 | 2.662 3 | 1.751 1 | 2.846 6 | 16.323 1 | 5.08 |
GA-Unet | 0.841 3 | 0.836 6 | 0.774 2 | 0.872 8 | 0.863 7 | 0.804 1 | 0.861 3 | 0.905 8 | 0.825 8 | 2.596 9 | 1.717 4 | 2.807 6 | 16.323 4 | 5.30 |
3D U-Net | 0.856 2 | 0.851 7 | 0.802 3 | 0.885 1 | 0.867 7 | 0.815 4 | 0.866 9 | 0.914 3 | 0.838 6 | 2.373 9 | 1.615 3 | 2.704 1 | 16.622 9 | 4.94 |
3D-GA-Unet | 0.863 2 | 0.847 3 | 0.803 6 | 0.867 3 | 0.826 9 | 0.983 1 | 0.867 6 | 0.949 2 | 0.831 5 | 2.079 3 | 1.601 2 | 2.573 8 | 16.333 0 | 4.50 |
模型 | DSC | PPV | sensitivity | Hausdorff | 参数量/106 | 收敛 迭代 次数 | 收敛 时间/h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
WT | TC | ET | WT | TC | ET | WT | TC | ET | WT | TC | ET | ||||
3D U-Net | 0.856 2 | 0.851 7 | 0.802 3 | 0.885 1 | 0.867 7 | 0.815 4 | 0.866 9 | 0.914 3 | 0.838 6 | 2.373 9 | 1.615 3 | 2.704 1 | 16.322 9 | 84 | 4.94 |
3D U-Net+Ghost | 0.857 3 | 0.840 3 | 0.793 2 | 0.863 4 | 0.868 0 | 0.834 1 | 0.862 0 | 0.915 8 | 0.843 2 | 2.341 2 | 1.605 1 | 2.664 2 | 16.322 7 | 82 | 4.92 |
3D U-Net+Attention | 0.858 2 | 0.853 2 | 0.801 6 | 0.875 2 | 0.841 2 | 0.842 4 | 0.861 4 | 0.924 3 | 0.821 3 | 2.213 4 | 1.612 1 | 2.584 1 | 16.334 6 | 79 | 4.81 |
3D-GA-Unet | 0.863 2 | 0.847 3 | 0.803 6 | 0.867 3 | 0.826 9 | 0.983 1 | 0.867 6 | 0.949 2 | 0.831 5 | 2.079 3 | 1.601 2 | 2.573 8 | 16.333 0 | 69 | 4.50 |
Tab. 3 Performance analysis of ablation experiment
模型 | DSC | PPV | sensitivity | Hausdorff | 参数量/106 | 收敛 迭代 次数 | 收敛 时间/h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
WT | TC | ET | WT | TC | ET | WT | TC | ET | WT | TC | ET | ||||
3D U-Net | 0.856 2 | 0.851 7 | 0.802 3 | 0.885 1 | 0.867 7 | 0.815 4 | 0.866 9 | 0.914 3 | 0.838 6 | 2.373 9 | 1.615 3 | 2.704 1 | 16.322 9 | 84 | 4.94 |
3D U-Net+Ghost | 0.857 3 | 0.840 3 | 0.793 2 | 0.863 4 | 0.868 0 | 0.834 1 | 0.862 0 | 0.915 8 | 0.843 2 | 2.341 2 | 1.605 1 | 2.664 2 | 16.322 7 | 82 | 4.92 |
3D U-Net+Attention | 0.858 2 | 0.853 2 | 0.801 6 | 0.875 2 | 0.841 2 | 0.842 4 | 0.861 4 | 0.924 3 | 0.821 3 | 2.213 4 | 1.612 1 | 2.584 1 | 16.334 6 | 79 | 4.81 |
3D-GA-Unet | 0.863 2 | 0.847 3 | 0.803 6 | 0.867 3 | 0.826 9 | 0.983 1 | 0.867 6 | 0.949 2 | 0.831 5 | 2.079 3 | 1.601 2 | 2.573 8 | 16.333 0 | 69 | 4.50 |
模型 | DSC | Hausdorff | ||||
---|---|---|---|---|---|---|
WT | TC | ET | WT | TC | ET | |
文献[ | 0.873 0 | 0.783 0 | 0.754 0 | 5.900 0 | 8.030 0 | 4.530 0 |
AFPNet[ | 0.865 8 | 0.768 8 | 0.744 3 | — | — | — |
Task Structure[ | 0.896 0 | 0.824 0 | 0.782 0 | — | — | — |
3D-GA-Unet | 0.863 2 | 0.847 3 | 0.803 6 | 2.079 3 | 1.601 2 | 2.573 8 |
Tab.4 Comparison results of 3D-GA-Unet and advanced segmentation models
模型 | DSC | Hausdorff | ||||
---|---|---|---|---|---|---|
WT | TC | ET | WT | TC | ET | |
文献[ | 0.873 0 | 0.783 0 | 0.754 0 | 5.900 0 | 8.030 0 | 4.530 0 |
AFPNet[ | 0.865 8 | 0.768 8 | 0.744 3 | — | — | — |
Task Structure[ | 0.896 0 | 0.824 0 | 0.782 0 | — | — | — |
3D-GA-Unet | 0.863 2 | 0.847 3 | 0.803 6 | 2.079 3 | 1.601 2 | 2.573 8 |
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