Bidirectional Efficient Attention Parallel Network for Segmentation of 3D Medical Imaging
Abstract
:1. Introduction
- A novel Supreme Channel Attention (SCA) mechanism has been designed. By integrating channel information from two types of pooling operations, SCA maximizes the extraction of channel features. It also addresses the issue of errors introduced by spatial gaps between slices in previous attention networks for 3D medical images. This results in a significant performance enhancement for the network.
- A Parallel Spatial Attention mechanism is proposed to synergistically collaborate with Supreme Channel Attention (SCA). In particular, we concatenate together the results after running max pooling and average pooling in parallel and added the previous input block to the feature block that came out later. This aims to extract more detailed spatial information, which can be used to assist the network in making more accurate segmentation predictions.
- The proposed BEAP-Net adopts a configuration with one encoder and three slightly different decoders, constrained by consistency loss to ensure consistent outputs for a given input. This paper designs a cyclic pseudo-labeling scheme that utilizes prediction outputs from three different decoders. These predicted biases are then transformed into auxiliary supervision signals to facilitate model training. We aim for these signals to learn more information by leveraging their differences, thereby achieving performance enhancement.
- This paper conducts detailed ablation studies on various improvements of the network to better understand their impact on network performance.
- The BEAP-Net is evaluated on datasets from two different domains and compared with several state-of-the-art semi-supervised methods. The experiments demonstrate that the proposed approach achieves the most efficient performance with good generalization and robustness.
2. Related Work
3. Methods
3.1. Supreme Channel Attention
3.2. Parallel Spatial Attention
3.3. Triple Consistency Training
4. Experiment
4.1. Implementation Details
4.2. Results and Comparison with SOTA
4.3. Ablation Study
4.3.1. Effectiveness of Supreme Channel Attention
4.3.2. Effectiveness of Parallel Spatial Attention
4.3.3. Ablation Study of Triple Consistency Training
4.4. Robustness and Generalization
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method | Labeled | Metrics | |||
---|---|---|---|---|---|
Dice ↑ | Jaccard ↑ | HD95 ↓ (Voxel) | ASD ↓ (Voxel) | ||
V-Net | 8 (10%) | 79.99 | 68.12 | 21.11 | 5.48 |
V-Net | 16 (20%) | 86.10 | 76.09 | 13.29 | 3.19 |
V-Net | 80 (All) | 91.14 | 83.82 | 5.75 | 1.52 |
DAP [48] | 81.88 | 71.23 | 15.81 | 3.79 | |
UA-MT [43] | 84.23 | 73.48 | 13.84 | 3.35 | |
SASSNet [47] | 87.31 | 77.72 | 9.61 | 2.55 | |
LG-ER-MT [16] | 85.53 | 75.12 | 13.29 | 3.77 | |
DUWM [50] | 8 (10%) | 85.90 | 75.74 | 12.67 | 3.31 |
DTC [7] | 86.57 | 76.55 | 14.47 | 3.74 | |
MC-Net [12] | 87.70 | 78.30 | 9.37 | 2.18 | |
BEAP-Net (Ours) | 88.95 ↑ 1.25 | 80.18 ↑ 1.88 | 8.90 ↓ 0.47 | 1.94 ↓ 0.24 | |
DAP [48] | 87.89 | 78.72 | 9.29 | 2.74 | |
UA-MT [43] | 88.87 | 80.21 | 7.32 | 2.26 | |
SASSNet [47] | 89.54 | 81.24 | 8.24 | 2.20 | |
LG-ER-MT [16] | 89.63 | 81.30 | 7.16 | 2.06 | |
DUWM [50] | 16 (20%) | 89.65 | 81.33 | 7.04 | 2.03 |
DTC [7] | 89.42 | 80.97 | 7.32 | 2.10 | |
BCP [49] | 90.56 | 82.69 | 6.03 | 1.71 | |
MC-Net [12] | 90.34 | 82.50 | 6.01 | 1.75 | |
BEAP-Net (Ours) | 91.52 ↑ 0.96 | 84.41 ↑ 1.72 | 5.08 ↓ 0.93 | 1.39 ↓ 0.32 |
Method | Labeled | Metrics | |||
---|---|---|---|---|---|
Dice ↑ | Jaccard ↑ | HD95 ↓ (Voxel) | ASD ↓ (Voxel) | ||
V-Net | 8 (10%) | 82.72 | 71.73 | 13.33 | 3.27 |
V-Net | 16 (20%) | 86.10 | 76.09 | 13.29 | 3.19 |
V-Net | 80 (All) | 91.47 | 84.35 | 5.49 | 1.52 |
V-Net+SE | 8 (10%) | 86.04 | 76.06 | 14.25 | 3.51 |
V-Net+SE | 16 (20%) | 90.72 | 82.74 | 7.66 | 2.36 |
V-Net+SCA | 8 (10%) | 90.69 | 82.56 | 7.78 | 2.42 |
V-Net+SCA | 16 (20%) | 91.33 | 83.79 | 5.78 | 1.57 |
Method | Labeled | Metrics | |||
---|---|---|---|---|---|
Dice↑ | Jaccard ↑ | HD95 ↓ (Voxel) | ASD ↓ (Voxel) | ||
V-Net | 8 (10%) | 82.72 | 71.73 | 13.33 | 3.27 |
V-Net | 16 (20%) | 86.10 | 76.09 | 13.29 | 3.19 |
V-Net | 80 (All) | 91.47 | 84.35 | 5.49 | 1.52 |
V-Net+SA | 8 (10%) | 85.06 | 75.03 | 14.35 | 3.91 |
V-Net+SA | 16 (20%) | 90.23 | 81.76 | 7.66 | 2.54 |
V-Net+PSA | 8 (10%) | 90.56 | 81.47 | 7.98 | 2.62 |
V-Net+PSA | 16 (20%) | 91.16 | 83.34 | 5.88 | 1.89 |
Method | Labeled | Metrics | |||
---|---|---|---|---|---|
Dice ↑ | Jaccard ↑ | HD95 ↓ (Voxel) | ASD ↓ (Voxel) | ||
V-Net | 8 (10%) | 82.72 | 71.73 | 13.33 | 3.27 |
V2d-Net | 8 (10%) | 85.78 | 75.40 | 14.45 | 3.84 |
V3-Net | 8 (10%) | 85.56 | 76.69 | 13.41 | 3.93 |
V3d-Net | 8 (10%) | 87.55 | 78.24 | 9.95 | 2.18 |
V2d-Net | 16 (20%) | 88.96 | 80.37 | 7.63 | 2.33 |
V3-Net | 16 (20%) | 86.64 | 79.69 | 12.07 | 2.75 |
V3d-Net | 16 (20%) | 90.79 | 83.21 | 5.79 | 1.97 |
Method | Labeled | Metrics | |||
---|---|---|---|---|---|
Dice ↑ | Jaccard ↑ | HD95 ↓ (Voxel) | ASD ↓ (Voxel) | ||
V-Net | 6 (10%) | 55.06 | 40.48 | 32.80 | 12.67 |
V-Net | 12 (20%) | 69.65 | 55.18 | 20.19 | 6.31 |
V-Net | 62 (All) | 83.01 | 71.35 | 5.18 | 1.19 |
UA-MT [43] | 6 (10%) | 68.70 | 54.65 | 13.89 | 3.23 |
SASSNet [47] | 6 (10%) | 66.52 | 52.23 | 17.11 | 2.25 |
DTC [7] | 6 (10%) | 66.27 | 52.07 | 15.00 | 4.43 |
MC-Net [12] | 6 (10%) | 68.94 | 54.74 | 16.28 | 3.16 |
BEAP-Net (Ours) | 6 (10%) | 68.99 ↑ 0.05 | 54.86 ↑ 0.12 | 14.02 ↑ 0.13 | 2.09 ↓ 0.16 |
UA-MT [43] | 12 (20%) | 76.77 | 63.77 | 11.41 | 2.79 |
SASSNet [47] | 12 (20%) | 77.12 | 64.24 | 8.93 | 1.41 |
DTC [7] | 12 (20%) | 78.27 | 64.75 | 8.37 | 2.27 |
MC-Net [12] | 12 (20%) | 79.05 | 65.82 | 10.29 | 2.71 |
BEAP-Net (Ours) | 12 (20%) | 79.11 ↑ 0.06 | 65.94 ↑ 0.12 | 8.50 ↑ 0.13 | 1.57 ↓ 0.16 |
Method | Labeled | Metrics | |||
---|---|---|---|---|---|
Dice ↑ | Jaccard ↑ | HD95 ↓ (Voxel) | ASD ↓ (Voxel) | ||
V-Net | 80 (All) | 91.47 | 84.36 | 5.48 | 1.51 |
BEAP-Net | 8 (10%) | 88.95 | 80.18 | 8.90 | 1.94 |
try1 | 8 (10%) | 87.56 | 78.69 | 10.41 | 2.18 |
try2 | 8 (10%) | 85.55 | 76.24 | 12.95 | 3.93 |
BEAP-Net | 16 (20%) | 91.52 | 84.41 | 5.08 | 1.39 |
try1 | 16 (20%) | 91.34 | 84.40 | 5.37 | 1.48 |
try2 | 16 (20%) | 91.27 | 83.99 | 5.75 | 1.73 |
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Wang, D.; Xv, T.; Liu, J.; Li, J.; Yang, L.; Guo, J. Bidirectional Efficient Attention Parallel Network for Segmentation of 3D Medical Imaging. Electronics 2024, 13, 3086. https://doi.org/10.3390/electronics13153086
Wang D, Xv T, Liu J, Li J, Yang L, Guo J. Bidirectional Efficient Attention Parallel Network for Segmentation of 3D Medical Imaging. Electronics. 2024; 13(15):3086. https://doi.org/10.3390/electronics13153086
Chicago/Turabian StyleWang, Dongsheng, Tiezhen Xv, Jiehui Liu, Jianshen Li, Lijie Yang, and Jinxi Guo. 2024. "Bidirectional Efficient Attention Parallel Network for Segmentation of 3D Medical Imaging" Electronics 13, no. 15: 3086. https://doi.org/10.3390/electronics13153086