Automatic Segmentation Algorithm of Liver Tumor Based on Feature Fusion

Fig. 5. Comparison before and after pretreatment. (a) Transverse plane; (b) sagittal plane; (c) coronal plane; (d) HU distribution before pretreatment; (e) HU distribution after pretreatment

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Fig. 6. Segmentation results of different methods. (a) Raw image; (b) Ground truth; (c) proposed method; (d) U-Net; (e) U-Net+deeply-supervised net; (f) U-Net+deeply-supervised net+spatial attention

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Fig. 7. Comparison between proposed method and One-stage

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Table 1. Segmentation results at different input sizes
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Table 1. Segmentation results at different input sizes
Input solution Voxel spacing /mm Slice Stride DSC
128×128×32 3 10 5 0.913
128×128×32 3 15 3 0.921
256×256×32 3 15 3 0.944
256×256×48 2 15 3 0.957

Table 2. Comparison of segmentation results of various network structures

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Table 2. Comparison of segmentation results of various network structures

Model	Liver			Tumor
Model	DSC	VOE	RVD	DSC	VOE	RVD
U-Net	0.939	0.112	0.007	0.547	0.411	-0.070
U-Net+deeply-supervised net	0.952	0.089	0.001	0.589	0.390	-0.104
U-Net+deeply-supervised net+spatial attention	0.955	0.084	-0.006	0.643	0.375	-0.091
U-Net+deeply-supervised net+FF	0.957	0.081	0.003	0.676	0.341	-0.064

Table 3. Comparison between proposed method and One-stage
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Table 3. Comparison between proposed method and One-stage
Model Liver Tumor
DSC VOE RVD DSC VOE RVD
Proposed 0.957 0.081 0.003 0.676 0.341 -0.064
One-stage 0.944 0.114 0.017 0.585 0.380 -0.081

Table 4. Comparison of different segmentation methods

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Table 4. Comparison of different segmentation methods

Model	Liver			Tumor
Model	DSC	VOE	RVD	DSC	VOE	RVD
Bi, et al	0.959	--	--	0.500	--	--
MEDDIIR	0.950	0.094	0.047	0.658	0.380	-0.12
Kaluva, et al^[6]	0.912	0.150	-0.008	0.492	0.411	19.70
Jin, et al^[22]	0.961	0.074	0.002	0.595	0.389	-0.152
Chen, et al^[23]	--	--	--	0.650	--	--
Jiang, et al^[23]	0.953	--	--	0.668	0.135	0.012
Our method	0.957	0.081	0.003	0.676	0.341	-0.064

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Yiming Liu, Zhiyong Xiao. Automatic Segmentation Algorithm of Liver Tumor Based on Feature Fusion[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1417001

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