Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Prostate MRI Images
2.2. Image Preprocessing
2.3. Synthetic Image Generation
2.4. Deep Learning Image Segmentation
2.5. Quality Control Study
3. Results
3.1. Deep Learning Image Segmentation
3.2. CNN Quality Control and Applicability Validation
3.3. Round 1 Quality Control Test
3.4. Round 2 Quality Control Test
3.5. Radiologist Quality Control Check
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Round 1 | Round 2 | |||||
---|---|---|---|---|---|---|
Amount Conventional | 25 | 26 | ||||
Amount Synthetic | 35 | 34 | ||||
Experience Level | 10 Years | 1 Year | No Experience | 10 Years | 1 Year | No Experience |
% Correct | 62 | 55 | 53 | 67 | 58 | 50 |
% FP | 46 | 46 | 54 | 25 | 35 | 47 |
% FN | 33 | 44 | 42 | 40 | 47 | 54 |
% Concordance | 67 | 57 | 42 | 80 | 60 | 30 |
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Xu, I.R.L.; Van Booven, D.J.; Goberdhan, S.; Breto, A.; Porto, J.; Alhusseini, M.; Algohary, A.; Stoyanova, R.; Punnen, S.; Mahne, A.; et al. Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images. J. Pers. Med. 2023, 13, 547. https://doi.org/10.3390/jpm13030547
Xu IRL, Van Booven DJ, Goberdhan S, Breto A, Porto J, Alhusseini M, Algohary A, Stoyanova R, Punnen S, Mahne A, et al. Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images. Journal of Personalized Medicine. 2023; 13(3):547. https://doi.org/10.3390/jpm13030547
Chicago/Turabian StyleXu, Isaac R. L., Derek J. Van Booven, Sankalp Goberdhan, Adrian Breto, Joao Porto, Mohammad Alhusseini, Ahmad Algohary, Radka Stoyanova, Sanoj Punnen, Anton Mahne, and et al. 2023. "Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images" Journal of Personalized Medicine 13, no. 3: 547. https://doi.org/10.3390/jpm13030547