Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
Abstract
:Featured Application
Abstract
1. Introduction
2. Materials and Methods
2.1. Experimental Setup
2.2. Deep Learning Models
2.3. Training Methodology
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter | Repetition Time (ms) | Echo Time (ms) | Flip Angle (Degrees) | Signal Averages | Signal-to-Noise Ratio |
---|---|---|---|---|---|
T2w TSE | 3091 | 100 | 90 | 3 | 1 |
Layer (Type) | Output Shape | Parameters Number |
---|---|---|
input_1 (InputLayer) | (None, 512, 512, 1) | 0 |
conv2d_1 (Conv2D) | (None, 256, 256, 15) | 150 |
Sensitivity | PPV | DSC | VOE | VD | |
---|---|---|---|---|---|
ENet | |||||
Mean | 93.06% | 89.25% | 90.89% | 16.50% | 4.53% |
±std | 6.37% | 3.94% | 3.87% | 5.86% | 9.43% |
±CI (95%) | 1.36% | 0.84% | 0.82% | 1.24% | 2.00% |
UNet | |||||
Mean | 88.89% | 91.89% | 90.14% | 17.66% | 3.16% |
± std | 7.61% | 3.31% | 4.69% | 6.91% | 9.36% |
±CI (95%) | 1.62% | 0.70% | 1.00% | 1.47% | 1.99% |
ERFNet | |||||
Mean | 89.93% | 85.44% | 87.18% | 22.18% | 5.70% |
±std | 10.92% | 5.43% | 6.44% | 9.61% | 14.72% |
±CI (95%) | 2.32% | 1.16% | 1.37% | 2.04% | 3.13% |
ANOVA | F Value | F Critic Value | p-Value |
---|---|---|---|
ENet vs. ERFNet | 20.70407668 | 3.897407169 | 0.000010236 |
ERFNet vs. UNet | 11.69135829 | 3.897407169 | 0.000788084 |
ENet vs. UNet | 1.301554482 | 3.897407169 | 0.255553164 |
Model Name | Number of Parameters | Size on Disk | Inference Times/Dataset | ||
---|---|---|---|---|---|
Trainable | Non-Trainable | CPU | GPU | ||
ENet | 362,992 | 8352 | 5.8 MB | 6.17 s | 1.07 s |
ERFNet | 2,056,440 | 0 | 25.3 MB | 8.59 s | 1.03 s |
UNet | 5,403,874 | 0 | 65.0 MB | 42.02 s | 1.57 s |
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Comelli, A.; Dahiya, N.; Stefano, A.; Vernuccio, F.; Portoghese, M.; Cutaia, G.; Bruno, A.; Salvaggio, G.; Yezzi, A. Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging. Appl. Sci. 2021, 11, 782. https://doi.org/10.3390/app11020782
Comelli A, Dahiya N, Stefano A, Vernuccio F, Portoghese M, Cutaia G, Bruno A, Salvaggio G, Yezzi A. Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging. Applied Sciences. 2021; 11(2):782. https://doi.org/10.3390/app11020782
Chicago/Turabian StyleComelli, Albert, Navdeep Dahiya, Alessandro Stefano, Federica Vernuccio, Marzia Portoghese, Giuseppe Cutaia, Alberto Bruno, Giuseppe Salvaggio, and Anthony Yezzi. 2021. "Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging" Applied Sciences 11, no. 2: 782. https://doi.org/10.3390/app11020782
APA StyleComelli, A., Dahiya, N., Stefano, A., Vernuccio, F., Portoghese, M., Cutaia, G., Bruno, A., Salvaggio, G., & Yezzi, A. (2021). Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging. Applied Sciences, 11(2), 782. https://doi.org/10.3390/app11020782