Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (11): 3599-3606.DOI: 10.11772/j.issn.1001-9081.2022111673
• Multimedia computing and computer simulation • Previous Articles Next Articles
Xin ZHAO1(), Qianqian ZHU1, Cong ZHAO2, Jialing WU3
Received:
2022-11-11
Revised:
2023-02-16
Accepted:
2023-02-20
Online:
2023-03-10
Published:
2023-11-10
Contact:
Xin ZHAO
About author:
ZHAO Xin, born in 1974, Ph. D., associate professor. Her research interests include artificial intelligence, digital medical image processing.Supported by:
通讯作者:
赵欣
作者简介:
赵欣(1974—),女,辽宁锦州人,副教授,博士,CCF会员,主要研究方向:人工智能、数字医学图像处理 zhaoxin@dlu.edu.cn基金资助:
CLC Number:
Xin ZHAO, Qianqian ZHU, Cong ZHAO, Jialing WU. Segmentation of breast nodules in ultrasound images based on multi-scale and cross-spatial fusion[J]. Journal of Computer Applications, 2023, 43(11): 3599-3606.
赵欣, 祝倩倩, 赵聪, 吴佳玲. 基于多尺度和跨空间融合的超声乳腺结节分割[J]. 《计算机应用》唯一官方网站, 2023, 43(11): 3599-3606.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022111673
组号 | 方法 | DICE系数 | 召回率 | 精度 | 交并比 |
---|---|---|---|---|---|
1 | UNet | 0.794 | 0.796 | 0.879 | 0.709 |
2 | UNet+SplitB | 0.816 | 0.821 | 0.880 | 0.732 |
UNet+SplitB+ | 0.827 | 0.835 | 0.885 | 0.749 | |
3 | UNet+MFEF | 0.847 | 0.852 | 0.896 | 0.762 |
UNet+SFA | 0.833 | 0.805 | 0.933 | 0.754 | |
UNet+CRF | 0.825 | 0.794 | 0.936 | 0.744 | |
4 | UNet+MFEF+SFA | 0.875 | 0.867 | 0.906 | 0.790 |
UNet+SFA+CRF | 0.854 | 0.828 | 0.928 | 0.778 | |
UNet+MFEF+CRF | 0.858 | 0.839 | 0.930 | 0.784 | |
5 | UNet+MFEF+SFA+CRF | 0.888 | 0.875 | 0.916 | 0.808 |
Tab. 1 Performance comparison of network structure ablation
组号 | 方法 | DICE系数 | 召回率 | 精度 | 交并比 |
---|---|---|---|---|---|
1 | UNet | 0.794 | 0.796 | 0.879 | 0.709 |
2 | UNet+SplitB | 0.816 | 0.821 | 0.880 | 0.732 |
UNet+SplitB+ | 0.827 | 0.835 | 0.885 | 0.749 | |
3 | UNet+MFEF | 0.847 | 0.852 | 0.896 | 0.762 |
UNet+SFA | 0.833 | 0.805 | 0.933 | 0.754 | |
UNet+CRF | 0.825 | 0.794 | 0.936 | 0.744 | |
4 | UNet+MFEF+SFA | 0.875 | 0.867 | 0.906 | 0.790 |
UNet+SFA+CRF | 0.854 | 0.828 | 0.928 | 0.778 | |
UNet+MFEF+CRF | 0.858 | 0.839 | 0.930 | 0.784 | |
5 | UNet+MFEF+SFA+CRF | 0.888 | 0.875 | 0.916 | 0.808 |
方法 | DICE系数 | 召回率 | 精度 | 交并比 |
---|---|---|---|---|
UNet | 0.794 | 0.796 | 0.879 | 0.709 |
AttUNet | 0.817 | 0.809 | 0.902 | 0.724 |
ResUNet++ | 0.849 | 0.858 | 0.867 | 0.756 |
SKUNet | 0.855 | 0.847 | 0.907 | 0.773 |
CF2-Net | 0.856 | 0.852 | 0.882 | — |
ESTAN | 0.820 | 0.840 | — | 0.740 |
FS-UNet | 0.887 | 0.864 | — | 0.804 |
SMU-Net | 0.870 | 0.889 | 0.880 | — |
本文方法 | 0.888 | 0.875 | 0.916 | 0.808 |
Tab. 2 Comparison of different segmentation methods
方法 | DICE系数 | 召回率 | 精度 | 交并比 |
---|---|---|---|---|
UNet | 0.794 | 0.796 | 0.879 | 0.709 |
AttUNet | 0.817 | 0.809 | 0.902 | 0.724 |
ResUNet++ | 0.849 | 0.858 | 0.867 | 0.756 |
SKUNet | 0.855 | 0.847 | 0.907 | 0.773 |
CF2-Net | 0.856 | 0.852 | 0.882 | — |
ESTAN | 0.820 | 0.840 | — | 0.740 |
FS-UNet | 0.887 | 0.864 | — | 0.804 |
SMU-Net | 0.870 | 0.889 | 0.880 | — |
本文方法 | 0.888 | 0.875 | 0.916 | 0.808 |
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