MAPPNet: A Multi-Scale Attention Pyramid Pooling Network for Dental Calculus Segmentation
Abstract
:1. Introduction
- An algorithm called MAPPNet is proposed for dental calculus image segmentation. This algorithm effectively captures global information from the images. Accordingly, targets with different scales can be accurately segmented.
- In both the encoder and decoder stages, a multi-scale fusion strategy is employed to build a network with a dual-ended multi-scale structure. This comprehensive approach significantly enhances the model’s capability to acquire contextual features.
- We incorporate channel attention, spatial attention, and combinations of these mechanisms to optimize the effectiveness of the pyramid pooling structure in dental calculus segmentation.
2. Related Work
2.1. Encoder–Decoder Architecture
2.2. Multi-Scale Feature Fusion
2.3. Attention Mechanism
2.4. Medical Segmentation Model
3. Methods
3.1. MAPPNet Architecture
3.2. MSF Module
3.3. APPM
3.4. Loss Function
3.5. Experimental Configuration
3.6. Evaluation Metrics
4. Experiments
4.1. Ablation Experiments
4.2. Comparison with Other Methods
4.3. Generalization Ability
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Items | Methods | IoU (%) | Sen (%) | Spe (%) | PPV (%) | Acc (%) | Params (M) |
---|---|---|---|---|---|---|---|
1 | Baseline | 76.21 | 85.91 | 98.89 | 87.09 | 97.85 | 46.71 |
2 | Baseline+APPM-CA | 77.95 | 87.85 | 98.89 | 87.37 | 98.0 | 47.26 |
3 | Baseline+APPM-SA | 77.32 | 86.66 | 98.95 | 87.77 | 97.96 | 44.64 |
4 | Baseline+APPM-CBAM | 77.70 | 87.64 | 98.88 | 87.27 | 97.98 | 47.26 |
5 | Baseline+APPM | 77.91 | 87.85 | 98.89 | 87.32 | 98.0 | 47.26 |
6 | Baseline+MSF | 80.89 | 89.30 | 99.09 | 89.58 | 98.31 | 27.62 |
7 | Baseline+MSF+APPM-CA | 80.11 | 89.22 | 99.01 | 88.70 | 98.22 | 27.78 |
8 | Baseline+MSF+APPM-SA | 81.20 | 89.23 | 99.14 | 90.02 | 98.34 | 27.62 |
9 | Baseline+MSF+APPM-CBAM | 80.51 | 88.67 | 99.01 | 88.75 | 98.26 | 27.78 |
10 | Baseline+MSF+APPM | 81.46 | 90.20 | 99.06 | 89.37 | 98.35 | 27.78 |
Methods | IoU (%) | Sen (%) | Spe (%) | PPV (%) | Acc (%) | Params (M) |
---|---|---|---|---|---|---|
BiseNetV1 | 41.45 | 56.91 | 96.74 | 60.41 | 93.55 | 56.856 |
CGNet | 53.29 | 65.51 | 98.00 | 74.07 | 95.39 | 0.492 |
ICNet | 57.67 | 70.16 | 98.11 | 76.41 | 95.86 | 47.528 |
MobileNetV3 | 58.28 | 72.55 | 97.86 | 74.77 | 95.83 | 3.282 |
UNext | 59.06 | 72.52 | 98.01 | 76.09 | 95.96 | 1.471 |
BiseNetV2 | 60.44 | 70.55 | 98.54 | 80.84 | 96.29 | 3.341 |
ERFNet | 63.17 | 78.93 | 97.82 | 75.98 | 96.31 | 2.082 |
Fastscnn | 67.68 | 78.74 | 98.57 | 82.82 | 96.98 | 1.398 |
PSPNet | 76.21 | 85.91 | 98.89 | 87.09 | 97.85 | 46.602 |
DMNet | 76.37 | 82.37 | 99.31 | 91.28 | 97.95 | 50.803 |
DeeplabV3+ | 76.87 | 83.14 | 99.29 | 91.07 | 97.99 | 41.216 |
MALUNet | 77.36 | 85.31 | 99.16 | 89.12 | 98.07 | 0.175 |
EGEUNet | 77.86 | 86.12 | 99.08 | 89.01 | 98.16 | 0.053 |
HRNet | 78.57 | 87.46 | 99.01 | 88.55 | 98.09 | 9.636 |
Fastfcn | 79.61 | 88.13 | 99.07 | 89.17 | 98.19 | 66.338 |
ENCNet | 80.06 | 88.85 | 99.04 | 88.99 | 98.22 | 33.510 |
CCNet | 80.41 | 88.80 | 99.09 | 89.49 | 98.26 | 47.453 |
TransUNet | 37.24 | 46.70 | 97.78 | 64.77 | 93.68 | 66.815 |
Segmenter | 39.37 | 54.35 | 96.68 | 58.81 | 93.28 | 6.685 |
Vit | 47.23 | 79.80 | 93.98 | 53.64 | 92.84 | 142 |
Segformer | 55.66 | 64.17 | 98.67 | 80.76 | 95.90 | 3.716 |
Swin | 56.49 | 67.26 | 98.34 | 77.92 | 95.84 | 58.942 |
DPT | 58.71 | 78.59 | 97.04 | 69.89 | 95.56 | 110 |
SwinUNet | 66.94 | 78.91 | 98.13 | 80.74 | 96.18 | 27.145 |
MedT | 67.39 | 79.11 | 98.45 | 82.57 | 96.81 | 1.604 |
MAPPNet | 81.46 | 90.20 | 99.06 | 89.37 | 98.35 | 27.784 |
Methods | ISIC2018 | Kvasir-SEG | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
IoU (%) | Sen (%) | Spe (%) | PPV (%) | Acc (%) | IoU (%) | Sen (%) | Spe (%) | PPV (%) | Acc (%) | |
BiseNetV1 | 67.01 | 86.55 | 87.74 | 74.80 | 87.38 | 69.69 | 76.31 | 97.64 | 88.92 | 93.40 |
CGNet | 67.78 | 76.96 | 94.30 | 85.04 | 89.17 | 84.94 | 94.15 | 97.31 | 89.67 | 96.68 |
ICNet | 69.65 | 81.23 | 93.01 | 83.01 | 89.52 | 85.89 | 91.57 | 98.36 | 92.36 | 97.01 |
MobileNetV3 | 73.42 | 78.84 | 96.89 | 91.43 | 91.55 | 78.19 | 80.76 | 99.19 | 96.10 | 95.53 |
UNext | 74.46 | 84.17 | 94.52 | 86.59 | 91.45 | 83.48 | 88.18 | 98.60 | 93.99 | 96.53 |
BiseNetV2 | 71.22 | 76.57 | 96.84 | 91.06 | 90.84 | 81.96 | 90.67 | 97.36 | 89.50 | 96.03 |
ERFNet | 73.81 | 81.65 | 95.53 | 88.49 | 91.42 | 87.53 | 92.77 | 98.52 | 93.94 | 97.37 |
Fastscnn | 72.39 | 80.23 | 95.44 | 88.11 | 90.94 | 83.32 | 91.48 | 97.57 | 90.33 | 96.36 |
PSPNet | 67.32 | 73.66 | 96.04 | 88.66 | 89.41 | 88.19 | 91.70 | 99.02 | 95.85 | 97.56 |
DMNet | 67.20 | 75.66 | 94.71 | 85.74 | 89.07 | 88.36 | 92.50 | 98.84 | 95.17 | 97.58 |
DeeplabV3+ | 64.70 | 73.02 | 94.59 | 85.02 | 88.20 | 88.46 | 90.70 | 99.37 | 97.28 | 97.65 |
HRNet | 70.30 | 76.36 | 96.38 | 89.87 | 90.45 | 87.34 | 90.37 | 99.14 | 96.29 | 97.40 |
Fastfcn | 65.28 | 70.39 | 96.71 | 89.99 | 88.91 | 88.58 | 91.06 | 99.30 | 97.01 | 97.67 |
ENCNet | 71.04 | 85.91 | 91.19 | 80.40 | 89.63 | 89.36 | 92.37 | 99.17 | 96.49 | 97.82 |
CCNet | 73.36 | 83.82 | 94.00 | 85.46 | 90.99 | 89.73 | 92.41 | 99.26 | 96.87 | 97.90 |
TransUNet | 68.00 | 74.29 | 96.11 | 88.92 | 89.65 | 71.71 | 77.59 | 97.97 | 90.45 | 93.92 |
Segmenter | 67.34 | 84.98 | 88.98 | 76.44 | 87.80 | 38.76 | 46.58 | 94.99 | 69.76 | 85.37 |
Vit | 73.75 | 85.69 | 93.19 | 84.11 | 90.97 | 53.47 | 61.44 | 96.30 | 80.46 | 89.37 |
Segformer | 73.13 | 81.38 | 95.25 | 87.82 | 91.05 | 67.80 | 71.79 | 98.54 | 92.43 | 93.23 |
Swin | 73.75 | 85.20 | 93.47 | 84.58 | 91.02 | 66.40 | 71.46 | 98.11 | 90.36 | 92.82 |
DPT | 71.34 | 83.74 | 92.69 | 82.81 | 90.04 | 76.14 | 82.65 | 97.88 | 90.62 | 94.85 |
MAPPNet | 76.48 | 87.08 | 94.17 | 86.27 | 92.07 | 91.38 | 94.16 | 99.25 | 96.87 | 98.23 |
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Nie, T.; Yao, S.; Wang, D.; Wang, C.; Zhao, Y. MAPPNet: A Multi-Scale Attention Pyramid Pooling Network for Dental Calculus Segmentation. Appl. Sci. 2024, 14, 7273. https://doi.org/10.3390/app14167273
Nie T, Yao S, Wang D, Wang C, Zhao Y. MAPPNet: A Multi-Scale Attention Pyramid Pooling Network for Dental Calculus Segmentation. Applied Sciences. 2024; 14(16):7273. https://doi.org/10.3390/app14167273
Chicago/Turabian StyleNie, Tianyu, Shihong Yao, Di Wang, Conger Wang, and Yishi Zhao. 2024. "MAPPNet: A Multi-Scale Attention Pyramid Pooling Network for Dental Calculus Segmentation" Applied Sciences 14, no. 16: 7273. https://doi.org/10.3390/app14167273