Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141030(2020)
Magnetic Resonance Brain Tumor Image Segmentation Based on Attention U-Net
Figures & Tables(9)
Fig. 1. Network structure. (a) FCN; (b) U-Net; (c) Res-U-Net; (d) proposed method
Fig. 2. Convolution module of the network structure. (a) Residure modules; (b) dense module; (c) residure-dense module
Fig. 3. Aattention module of SE-Net. (a) SE-Net attention unit; (b) proposed attention unit
Fig. 4. Experimental data. (a) TI image; (b) T1ce image; (c) T2 image; (d) FLAIR image; (E) ground truth
Fig. 5. Experimental results of the four models at three different levels of LGG and HGG. (a) LGG; (b) HGG
Fig. 6. Comparison of network loss changes under different epoch weighting factors
Table 1. Results of the four models%
View tableTable 1. Results of the four models%
Model Dice score Sensitivity Specificity WT TC ET WT TC ET WT TC ET FCN 83.16 73.34 63.13 88.75 77.25 71.36 99.83 99.82 99.95 U-Net 84.16 75.23 65.13 89.15 78.15 73.26 99.74 99.80 99.94 Res-U-Net 87.10 74.97 78.32 89.85 79.32 77.42 99.96 99.93 99.97 Proposed 90.56 79.82 78.61 90.25 80.29 78.95 99.98 99.95 99.98
Table 2. Results of adding different blocks to the U-NET structure%
View tableTable 2. Results of adding different blocks to the U-NET structure%
Model Dice score Sensitivity Specificity WT TC ET WT TC ET WT TC ET U-Net 84.16 75.23 65.13 89.15 78.15 73.26 99.74 99.80 99.94 RDB+U-Net 87.25 76.20 77.23 87.53 79.42 77.02 99.85 98.90 99.13 TRB+RDB+U-Net 87.38 75.97 77.42 89.52 78.58 77.45 98.86 98.92 99.21 A1+TRB+RDB+U-Net 89.05 78.53 78.46 89.97 79.92 78.51 99.32 99.91 99.42 A2+TRB+RDB+U-Net 88.82 77.25 78.35 89.51 79.36 78.01 99.06 99.88 99.35 A3+TRB+RDB+U-Net 88.23 76.82 77.32 88.97 78.86 77.81 99.85 99.81 99.29 Proposed 90.56 79.82 78.61 90.25 80.29 78.95 99.98 99.95 99.98
Table 3. Dice comparison between proposed method and other advanced segmentation methods
View tableTable 3. Dice comparison between proposed method and other advanced segmentation methods
Method Dice_WT Dice_ET Dice_TC Dong et al.[ 7 ]0.8600 0.6500 0.8600 Isensee et al.[ 21 ]0.8580 0.6470 0.7750 Puch et al.[ 22 ]0.8970 0.7520 0.7970 Proposed 0.9056 0.7861 0.7982
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Lingmei Ai, Tiandong Li, Fuyuan Liao, Kangzhen Shi. Magnetic Resonance Brain Tumor Image Segmentation Based on Attention U-Net[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141030
Paper Information
Category: Image Processing
Received: Nov. 6, 2019
Accepted: Dec. 31, 2019
Published Online: Jul. 28, 2020
The Author Email: Lingmei Ai (almsac@163.com), Tiandong Li (litiandong@snnu.edu.cn)
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