Development of Deep Learning with RDA U-Net Network for Bladder Cancer Segmentation
Abstract
:Simple Summary
Abstract
1. Introduction
- Improve the speed and accuracy of identifying organs and lesions without contrast agent injection.
- Compared with Attention U-Net and Attention Dense-U-Net, RDA U-Net convolution reduces the calculation time by about 40%.
- With a mixture of hospital and open-source image data, RDA U-Net can identify various abdominal organs accurately.
2. Method
2.1. Data Set-Up and Image Pre-Processing
2.2. Data Augmentation
- Size scaling: For zooming in and out of the image, this research uses the original size of the image and zooms to 120% of the original size for training.
- Rotation: The image is randomly translated and rotated at the specified angle. This research uses a rotation translation of plus or minus 15 degrees.
- Shifting: The pixel moves the image horizontally and vertically. This research moves the image on the x and y axes by plus or minus 10.
- Shearing: The random amount of the image is clipped according to the set angle range. The set value in this research is between plus and minus 5 degrees.
- Horizontal flip: The image is flipped based on the horizontal direction. In this paper, 50% of the images are converted.
2.3. Model Structure
2.4. Encoder
2.5. Decoder
2.6. Training Data
3. Evaluation Metrics
4. Result and Discussion
4.1. Parameter Setting of Experimental Learning Rate
4.2. Comparison of Bladder Training Time and Convolution Parameters
4.3. Residual-Dense Attention U-Net Model Convergence Curve
4.4. Bladder and Lesion Segmentation Results
4.5. Analysis of the Results of Bladder Cancer
5. Conclusions
- By changing the HU value, we made the picture clearer without a contrast agent. Through experiments, we confirmed that the training speed of our model was much lower than that of other models.
- Through experiments, it was confirmed that RDA U-NET reduced the computing time by about 40% compared with the proposed strategy.
- With the data from Kaohsiung Medical University and Open-Source Imaging (TCGA), the accuracy of RDA U-Net in bladder organ identification and lesion identification reached 96% and 93%, respectively.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method | Parameter | Bladder Tumor-Training Time (s) | Bladder-Training Time (s) |
---|---|---|---|
RDA U-Net | 13,053,861 | 41 | 42 |
Attention U-Net [32] | 35,238,293 | 56 | 57 |
Attention Dense-U-Net [38] | 14,374,021 | 59 | 60 |
Attention Res-U Net [39] | 12,981,573 | 40 | 40 |
Method | ACC | DSC | IoU | AVGDIST |
---|---|---|---|---|
RDA U-Net | 0.9656 | 0.9745 | 0.9505 | 0.0269 |
Attention U-Net [32] | 0.9610 | 0.9771 | 0.9553 | 0.0245 |
Attention Dense-U-Net [38] | 0.9721 | 0.9811 | 0.9631 | 0.0187 |
Attention Res-U-Net [39] | 0.9614 | 0.9717 | 0.9452 | 0.0291 |
Method | ACC | DSC | IoU | AVGDIST |
---|---|---|---|---|
RDA U-Net | 0.9394 | 0.8895 | 0.8024 | 0.1279 |
Attention U-Net [32] | 0.9330 | 0.8993 | 0.8184 | 0.1141 |
Attention Dense-U-Net [38] | 0.9258 | 0.8797 | 0.7869 | 0.1406 |
Attention Res-U-Net [39] | 0.8928 | 0.8322 | 0.7150 | 0.2080 |
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Share and Cite
Lee, M.-C.; Wang, S.-Y.; Pan, C.-T.; Chien, M.-Y.; Li, W.-M.; Xu, J.-H.; Luo, C.-H.; Shiue, Y.-L. Development of Deep Learning with RDA U-Net Network for Bladder Cancer Segmentation. Cancers 2023, 15, 1343. https://doi.org/10.3390/cancers15041343
Lee M-C, Wang S-Y, Pan C-T, Chien M-Y, Li W-M, Xu J-H, Luo C-H, Shiue Y-L. Development of Deep Learning with RDA U-Net Network for Bladder Cancer Segmentation. Cancers. 2023; 15(4):1343. https://doi.org/10.3390/cancers15041343
Chicago/Turabian StyleLee, Ming-Chan, Shao-Yu Wang, Cheng-Tang Pan, Ming-Yi Chien, Wei-Ming Li, Jin-Hao Xu, Chi-Hung Luo, and Yow-Ling Shiue. 2023. "Development of Deep Learning with RDA U-Net Network for Bladder Cancer Segmentation" Cancers 15, no. 4: 1343. https://doi.org/10.3390/cancers15041343