SAU-Net: Multiorgan Image Segmentation Method Improved Using Squeeze Attention Mechanism

Fig. 6. Comparison of visualization for SAU-Net segmentation results. (a) The cross-sectional view of prediction; (b) the cross-sectional view of ground truth; (c) the cross-sectional view of overlap between prediction and ground truth; (d) the 3D view of overlap between prediction and ground truth

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Table 1. Source and distribution of dataset
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Table 1. Source and distribution of dataset
Type Source Number
Training dataset MICCAI 2015 training dataset 38
Head and Neck Cetuximab collections 46
Public dataset of the Quebec Institute of Canada 177
Test dataset MICCAI 2015 test dataset 10

Table 2. Comparison of DSC with different models

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Table 2. Comparison of DSC with different models

Organ	Baseline	Baseline+Res	Baseline+SE	SAU-Net
Brainstem	0.773	0.834	0.857	0.884
Chiasm	0.484	0.495	0.522	0.554
Mandible	0.830	0.858	0.917	0.932
Optic.L	0.662	0.692	0.712	0.738
Optic.R	0.628	0.683	0.709	0.729
Paro.L	0.787	0.818	0.863	0.887
Paro.R	0.768	0.824	0.859	0.881
Subm.L	0.723	0.743	0.798	0.812
Subm.R	0.732	0.738	0.805	0.820
Average	0.707	0.743	0.782	0.804

Table 3. Comparison of FPR and FNR with different models

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Table 3. Comparison of FPR and FNR with different models

Organ	Baseline		Baseline+Res		Baseline+SE		SAU-Net
Organ	FPR	FNR	FPR	FNR	FPR	FNR	FPR	FNR
Brainstem	0.117	0.138	0.110	0.124	0.072	0.101	0.063	0.091
Chiasm	0.105	0.461	0.091	0.457	0.081	0.347	0.074	0.292
Mandible	0.127	0.119	0.127	0.093	0.097	0.058	0.106	0.047
Optic.L	0.118	0.428	0.106	0.415	0.092	0.327	0.034	0.295
Optic.R	0.121	0.437	0.126	0.397	0.114	0.294	0.108	0.313
Paro.L	0.137	0.154	0.120	0.139	0.088	0.131	0.076	0.092
Paro.R	0.141	0.161	0.131	0.148	0.112	0.129	0.090	0.123
Subm.L	0.115	0.146	0.109	0.139	0.092	0.122	0.084	0.117
Subm.R	0.129	0.152	0.116	0.142	0.098	0.118	0.101	0.071
Average	0.123	0.244	0.115	0.228	0.094	0.181	0.082	0.160

Table 4. Comparison of DSC score of different methods

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Table 4. Comparison of DSC score of different methods

Organs	Ref.［13］	Ref.［14］	Ref.［2］	AnatomyNet^［17］	FocusNet^［19］	FocusNetv2^［21］	SAU-Net
Brainstem	0.880	-	0.903	0.867	0.875	0.882	0.884
Chiasm	0.557	0.580	-	0.532	0.596	0.713	0.554
Mandible	0.930	-	0.944	0.925	0.935	0.947	0.932
Optic.L	0.634	0.720	-	0.721	0.735	0.790	0.738
Optic.R	0.639	0.700	-	0.706	0.744	0.817	0.729
Paro.L	0.827	-	0.823	0.881	0.863	0.898	0.887
Paro.R	0.814	-	0.829	0.874	0.879	0.881	0.881
Subm.L	0.723	-	-	0.814	0.798	0.840	0.812
Subm.R	0.723	-	-	0.813	0.801	0.838	0.820
Average	0.749	-	-	0.793	0.803	0.845	0.804

Table 5. Comparison of 95HD score of different methods

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Table 5. Comparison of 95HD score of different methods

Organs	Ref.［13］	Ref.［14］	AnatomyNet^［17］	FocusNet^［19］	FocusNetv2^［21］	SAU-Net
Brainstem	4.59	-	6.42 $\pm$ 2.38	2.14 $\pm$ 0.6	2.32 $\pm$ 0.7	2.02 $\pm$ 0.8
Chiasm	2.78	2.81 $\pm$ 1.6	5.76 $\pm$ 2.49	3.16 $\pm$ 1.3	2.25 $\pm$ 0.8	2.65 $\pm$ 1.3
Mandible	1.97	-	6.28 $\pm$ 2.21	1.18 $\pm$ 0.3	1.08 $\pm$ 0.4	2.12 $\pm$ 0.7
Optic.L	2.26	2.33 $\pm$ 0.8	4.85 $\pm$ 2.32	3.76 $\pm$ 2.9	1.92 $\pm$ 0.8	2.14 $\pm$ 1.8
Optic.R	3.15	2.13 $\pm$ 1.0	4.77 $\pm$ 4.27	2.65 $\pm$ 1.5	2.17 $\pm$ 0.7	2.32 $\pm$ 1.4
Paro.L	5.11	-	9.31 $\pm$ 3.32	2.52 $\pm$ 1.0	1.81 $\pm$ 0.4	2.54 $\pm$ 1.1
Paro.R	6.13	-	10.08 $\pm$ 5.09	2.07 $\pm$ 0.8	2.43 $\pm$ 2.0	2.32 $\pm$ 1.7
Subm.L	5.35	-	7.01 $\pm$ 4.44	2.67 $\pm$ 1.3	2.84 $\pm$ 1.2	2.27 $\pm$ 1.6
Subm.R	5.44	-	6.02 $\pm$ 1.08	3.41 $\pm$ 1.4	2.74 $\pm$ 1.2	2.92 $\pm$ 1.8
Average	4.14	-	6.30	2.62	2.17	2.37

Table 6. Comparison of parameters and inference time of different methods
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Table 6. Comparison of parameters and inference time of different methods
Models Metrics
Parameters /million Time /s
AnatomyNet 0.73 0.68
FocusNetv2 2.02 1.88
SAU-Net 0.79 0.51

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Guogang Cao, Hongdong Mao, Shu Zhang, Ying Chen, Cuixia Dai. SAU-Net: Multiorgan Image Segmentation Method Improved Using Squeeze Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0417003

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