Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (12): 3891-3899.DOI: 10.11772/j.issn.1001-9081.2021101737
• Multimedia computing and computer simulation • Previous Articles Next Articles
Hong LI, Junying ZOU(), Xicheng TAN, Guiyang LI
Received:
2021-10-09
Revised:
2022-01-04
Accepted:
2022-01-24
Online:
2022-04-08
Published:
2022-12-10
Contact:
Junying ZOU
About author:
LI Hong, born in 1995, M. S. candidate. His research interests include machine learning, medical image segmentation.Supported by:
通讯作者:
邹俊颖
作者简介:
李鸿(1995—),男,四川眉山人,硕士研究生,主要研究方向:机器学习、医学图像分割基金资助:
CLC Number:
Hong LI, Junying ZOU, Xicheng TAN, Guiyang LI. Multi-attention fusion network for medical image segmentation[J]. Journal of Computer Applications, 2022, 42(12): 3891-3899.
李鸿, 邹俊颖, 谭茜成, 李贵洋. 面向医学图像分割的多注意力融合网络[J]. 《计算机应用》唯一官方网站, 2022, 42(12): 3891-3899.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021101737
模型 | DSC↑ | HD↓ | 器官分割精度 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
主动脉 | 胆囊 | 肾脏(L) | 肾脏(R) | 肝脏 | 胰腺 | 脾 | 胃 | |||
V-Net[ | 68.81 | — | 75.34 | 51.87 | 77.10 | 80.75 | 87.84 | 40.05 | 80.56 | 56.98 |
DARR[ | 69.77 | — | 74.74 | 53.77 | 72.31 | 73.24 | 94.08 | 54.18 | 89.90 | 45.96 |
R50 UNet[ | 74.68 | 36.87 | 84.18 | 62.84 | 80.60 | 79.19 | 93.74 | 56.90 | 85.87 | 74.16 |
UNet[ | 76.60 | 35.02 | 87.80 | 67.25 | 80.47 | 73.31 | 93.36 | 53.78 | 84.87 | 71.93 |
R50 Att-UNet[ | 75.57 | 36.97 | 85.92 | 63.91 | 79.20 | 72.71 | 93.56 | 49.37 | 87.19 | 74.95 |
Att-UNet[ | 77.75 | 32.59 | 88.05 | 67.73 | 82.23 | 75.59 | 93.38 | 55.69 | 85.56 | 73.81 |
R50 Vit[ | 71.29 | 32.87 | 73.73 | 55.13 | 75.80 | 72.20 | 91.51 | 45.99 | 81.99 | 73.95 |
TransUNet[ | 77.48 | 31.69 | 87.23 | 63.13 | 81.87 | 77.02 | 94.08 | 55.86 | 85.08 | 75.62 |
MFUNetfffm | 79.45 | 29.35 | 87.61 | 63.58 | 83.23 | 80.65 | 94.41 | 61.36 | 87.86 | 76.89 |
MFUNetdca | 78.05 | 29.75 | 87.37 | 63.59 | 81.83 | 78.35 | 93.43 | 56.77 | 85.98 | 77.10 |
MFUNet | 81.06 | 28.05 | 88.40 | 65.13 | 84.63 | 81.88 | 94.71 | 63.95 | 89.22 | 80.60 |
Tab.1 Segmentation accuracies of different models on Synapse multi-organ segmentation dataset
模型 | DSC↑ | HD↓ | 器官分割精度 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
主动脉 | 胆囊 | 肾脏(L) | 肾脏(R) | 肝脏 | 胰腺 | 脾 | 胃 | |||
V-Net[ | 68.81 | — | 75.34 | 51.87 | 77.10 | 80.75 | 87.84 | 40.05 | 80.56 | 56.98 |
DARR[ | 69.77 | — | 74.74 | 53.77 | 72.31 | 73.24 | 94.08 | 54.18 | 89.90 | 45.96 |
R50 UNet[ | 74.68 | 36.87 | 84.18 | 62.84 | 80.60 | 79.19 | 93.74 | 56.90 | 85.87 | 74.16 |
UNet[ | 76.60 | 35.02 | 87.80 | 67.25 | 80.47 | 73.31 | 93.36 | 53.78 | 84.87 | 71.93 |
R50 Att-UNet[ | 75.57 | 36.97 | 85.92 | 63.91 | 79.20 | 72.71 | 93.56 | 49.37 | 87.19 | 74.95 |
Att-UNet[ | 77.75 | 32.59 | 88.05 | 67.73 | 82.23 | 75.59 | 93.38 | 55.69 | 85.56 | 73.81 |
R50 Vit[ | 71.29 | 32.87 | 73.73 | 55.13 | 75.80 | 72.20 | 91.51 | 45.99 | 81.99 | 73.95 |
TransUNet[ | 77.48 | 31.69 | 87.23 | 63.13 | 81.87 | 77.02 | 94.08 | 55.86 | 85.08 | 75.62 |
MFUNetfffm | 79.45 | 29.35 | 87.61 | 63.58 | 83.23 | 80.65 | 94.41 | 61.36 | 87.86 | 76.89 |
MFUNetdca | 78.05 | 29.75 | 87.37 | 63.59 | 81.83 | 78.35 | 93.43 | 56.77 | 85.98 | 77.10 |
MFUNet | 81.06 | 28.05 | 88.40 | 65.13 | 84.63 | 81.88 | 94.71 | 63.95 | 89.22 | 80.60 |
模型 | DSC | 器官分割精度 | ||
---|---|---|---|---|
右心室 | 心肌 | 左心室 | ||
R50 U-Net | 87.55 | 87.10 | 80.63 | 94.92 |
R50 Att-UNet | 86.75 | 87.58 | 79.20 | 93.47 |
R50 ViT | 87.57 | 86.07 | 81.88 | 94.75 |
TransUNet | 89.71 | 88.86 | 84.53 | 95.73 |
MFUNet | 90.91 | 90.29 | 88.01 | 94.42 |
Tab.2 Segmentation accuracies of different models on ACDC heart segmentation dataset
模型 | DSC | 器官分割精度 | ||
---|---|---|---|---|
右心室 | 心肌 | 左心室 | ||
R50 U-Net | 87.55 | 87.10 | 80.63 | 94.92 |
R50 Att-UNet | 86.75 | 87.58 | 79.20 | 93.47 |
R50 ViT | 87.57 | 86.07 | 81.88 | 94.75 |
TransUNet | 89.71 | 88.86 | 84.53 | 95.73 |
MFUNet | 90.91 | 90.29 | 88.01 | 94.42 |
模型 | 跳跃连接 | DSC | 器官分割精度 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
主动脉 | 胆囊 | 肾脏(L) | 肾脏(R) | 肝脏 | 胰腺 | 脾 | 胃 | |||
TransUNet | 1 | 76.18 | 83.31 | 60.57 | 79.94 | 77.39 | 92.80 | 53.46 | 85.59 | 76.34 |
2 | 76.74 | 86.72 | 60.79 | 81.07 | 76.08 | 93.78 | 55.94 | 84.76 | 75.55 | |
3 | 77.48 | 87.23 | 63.13 | 81.87 | 77.02 | 94.08 | 55.86 | 85.08 | 75.62 | |
MFUNet | 1 | 77.67 | 84.54 | 62.63 | 80.41 | 77.42 | 94.23 | 59.49 | 86.78 | 80.15 |
2 | 80.05 | 88.00 | 63.90 | 83.02 | 81.66 | 94.55 | 62.67 | 87.08 | 79.52 | |
3 | 81.06 | 88.40 | 65.13 | 84.63 | 81.88 | 94.71 | 63.95 | 89.22 | 80.60 |
Tab.3 Ablation experiment for number of skip connections
模型 | 跳跃连接 | DSC | 器官分割精度 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
主动脉 | 胆囊 | 肾脏(L) | 肾脏(R) | 肝脏 | 胰腺 | 脾 | 胃 | |||
TransUNet | 1 | 76.18 | 83.31 | 60.57 | 79.94 | 77.39 | 92.80 | 53.46 | 85.59 | 76.34 |
2 | 76.74 | 86.72 | 60.79 | 81.07 | 76.08 | 93.78 | 55.94 | 84.76 | 75.55 | |
3 | 77.48 | 87.23 | 63.13 | 81.87 | 77.02 | 94.08 | 55.86 | 85.08 | 75.62 | |
MFUNet | 1 | 77.67 | 84.54 | 62.63 | 80.41 | 77.42 | 94.23 | 59.49 | 86.78 | 80.15 |
2 | 80.05 | 88.00 | 63.90 | 83.02 | 81.66 | 94.55 | 62.67 | 87.08 | 79.52 | |
3 | 81.06 | 88.40 | 65.13 | 84.63 | 81.88 | 94.71 | 63.95 | 89.22 | 80.60 |
模型 | 模型规模 | DSC | 器官分割精度 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
主动脉 | 胆囊 | 肾脏(L) | 肾脏(R) | 肝脏 | 胰腺 | 脾 | 胃 | |||
TransUNet | base | 77.48 | 87.23 | 63.13 | 81.87 | 77.02 | 94.08 | 55.86 | 85.08 | 75.62 |
large | 78.52 | 87.42 | 63.92 | 82.17 | 80.19 | 94.47 | 57.64 | 87.42 | 74.90 | |
MFUNet | base | 81.06 | 88.40 | 65.13 | 84.63 | 81.88 | 94.71 | 63.95 | 89.22 | 80.60 |
large | 81.46 | 88.21 | 66.67 | 85.00 | 81.84 | 94.68 | 64.87 | 90.08 | 81.46 |
Tab.4 Ablation experiment for model scale
模型 | 模型规模 | DSC | 器官分割精度 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
主动脉 | 胆囊 | 肾脏(L) | 肾脏(R) | 肝脏 | 胰腺 | 脾 | 胃 | |||
TransUNet | base | 77.48 | 87.23 | 63.13 | 81.87 | 77.02 | 94.08 | 55.86 | 85.08 | 75.62 |
large | 78.52 | 87.42 | 63.92 | 82.17 | 80.19 | 94.47 | 57.64 | 87.42 | 74.90 | |
MFUNet | base | 81.06 | 88.40 | 65.13 | 84.63 | 81.88 | 94.71 | 63.95 | 89.22 | 80.60 |
large | 81.46 | 88.21 | 66.67 | 85.00 | 81.84 | 94.68 | 64.87 | 90.08 | 81.46 |
模型 | 输入分辨率 | DSC | 器官分割精度 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
主动脉 | 胆囊 | 肾脏(L) | 肾脏(R) | 肝脏 | 胰腺 | 脾 | 胃 | |||
TransUNet | 224×224 | 77.48 | 87.23 | 63.13 | 81.87 | 77.02 | 94.08 | 55.86 | 85.08 | 75.62 |
384×384 | 80.51 | 88.67 | 67.62 | 82.80 | 76.25 | 94.87 | 65.57 | 87.57 | 80.69 | |
MFUNet | 224×224 | 81.06 | 88.40 | 65.13 | 84.63 | 81.88 | 94.71 | 63.95 | 89.22 | 80.60 |
384×384 | 81.73 | 89.91 | 65.80 | 81.98 | 78.99 | 95.43 | 68.15 | 91.10 | 82.48 |
Tab.5 Ablation experiment for input resolution
模型 | 输入分辨率 | DSC | 器官分割精度 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
主动脉 | 胆囊 | 肾脏(L) | 肾脏(R) | 肝脏 | 胰腺 | 脾 | 胃 | |||
TransUNet | 224×224 | 77.48 | 87.23 | 63.13 | 81.87 | 77.02 | 94.08 | 55.86 | 85.08 | 75.62 |
384×384 | 80.51 | 88.67 | 67.62 | 82.80 | 76.25 | 94.87 | 65.57 | 87.57 | 80.69 | |
MFUNet | 224×224 | 81.06 | 88.40 | 65.13 | 84.63 | 81.88 | 94.71 | 63.95 | 89.22 | 80.60 |
384×384 | 81.73 | 89.91 | 65.80 | 81.98 | 78.99 | 95.43 | 68.15 | 91.10 | 82.48 |
卷积分组数 | DSC | 器官分割精度 | |||||||
---|---|---|---|---|---|---|---|---|---|
主动脉 | 胆囊 | 肾脏(L) | 肾脏(R) | 肝脏 | 胰腺 | 脾 | 胃 | ||
1 | 80.75 | 87.80 | 64.18 | 84.13 | 79.41 | 94.69 | 64.78 | 90.30 | 80.72 |
48 | 80.53 | 88.00 | 62.64 | 84.12 | 80.53 | 94.75 | 63.22 | 89.92 | 80.70 |
768 | 81.06 | 88.40 | 65.13 | 84.63 | 81.88 | 94.71 | 63.95 | 89.22 | 80.60 |
Tab.6 Ablation experiment for number of convolution groups in MFUNet
卷积分组数 | DSC | 器官分割精度 | |||||||
---|---|---|---|---|---|---|---|---|---|
主动脉 | 胆囊 | 肾脏(L) | 肾脏(R) | 肝脏 | 胰腺 | 脾 | 胃 | ||
1 | 80.75 | 87.80 | 64.18 | 84.13 | 79.41 | 94.69 | 64.78 | 90.30 | 80.72 |
48 | 80.53 | 88.00 | 62.64 | 84.12 | 80.53 | 94.75 | 63.22 | 89.92 | 80.70 |
768 | 81.06 | 88.40 | 65.13 | 84.63 | 81.88 | 94.71 | 63.95 | 89.22 | 80.60 |
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