A Virtual Staining Method Based on Self-Supervised GAN for Fourier Ptychographic Microscopy Colorful Imaging
Abstract
:1. Introduction
- We adopt the unpaired learning architecture of CUT to treat the virtual staining of the FPM pathology images as a transformation from a single-channel image to a color image. Since CUT cannot accurately identify the light-colored part of the image and will produce unnecessary artifacts, we design the ECR block in the generator to extract cross-channel interaction information and improve the network performance.
- CUT cannot effectively capture the edges and details of the image. We introduce the content-consistency loss based on multi-scale structural similarity (MS-SSIM) to avoid feature distortion between input and output images and enhance the high-frequency information of images.
2. Related Work
2.1. Color-Based FPM Imaging
2.2. Image-to-Image Translation
2.3. Contrastive Learning
2.4. Attention Mechanism
3. Proposed Method
3.1. Network Architecture
3.2. Efficient Channel Residual Block
3.3. Loss Functions
3.3.1. PatchNCE Loss
3.3.2. Content-Consistency Loss
3.3.3. Adversarial Loss
3.3.4. Identity Loss
3.4. Overall Objective
4. Experiments
4.1. Datasets
4.2. Training Settings
4.3. Comparison Results with Unsupervised Deep Learning Methods
4.3.1. Visual Comparisons
4.3.2. Quantitative Examinations
4.4. Comparative Experiments under Noisy Conditions
4.5. Ablation Experiment
4.6. Comparison Results with Classical Virtual Staining Methods
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Method | FID↓ | LPIPS↓ | SSIM↑ | PSNR↑ |
---|---|---|---|---|
CUT [14] | 58.7077 | 0.1832 | 0.9078 | 20.1739 |
CycleGAN [10] | 63.1189 | 0.1688 | 0.9095 | 19.4760 |
UNIT [21] | 81.0382 | 0.1709 | 0.8884 | 19.1821 |
GCGAN [22] | 76.1018 | 0.1649 | 0.8934 | 18.3975 |
DCLGAN [23] | 95.7042 | 0.2131 | 0.8722 | 17.5598 |
Ours | 49.3686 | 0.1334 | 0.9241 | 20.4459 |
Method | FID↓ | LPIPS↓ | SSIM↑ | PSNR↑ |
---|---|---|---|---|
CUT [14] | 67.6780 | 0.1832 | 0.9066 | 20.0794 |
CycleGAN [10] | 72.8258 | 0.1649 | 0.9093 | 19.4901 |
UNIT [21] | 90.3650 | 0.1699 | 0.8884 | 19.3234 |
GCGAN [22] | 83.3363 | 0.1692 | 0.8875 | 18.2488 |
DCLGAN [23] | 104.5146 | 0.2109 | 0.8716 | 17.6097 |
Ours | 56.4709 | 0.1327 | 0.9220 | 20.3231 |
Noise | FID↓ | LPIPS↓ | SSIM↑ | PSNR↑ |
---|---|---|---|---|
6 × 10−4 | 56.4709 | 0.1327 | 0.9220 | 20.3231 |
6 × 10−3 | 56.4854 | 0.1343 | 0.9214 | 20.3286 |
6 × 10−2 | 56.5298 | 0.1378 | 0.9203 | 20.4288 |
Method | FID↓ | LPIPS↓ | SSIM↑ | PSNR↑ |
---|---|---|---|---|
Ours w/o Loss | 67.8005 | 0.1866 | 0.9086 | 20.3208 |
Ours w/o ECR | 57.3747 | 0.1372 | 0.9180 | 19.8297 |
Ours | 49.3686 | 0.1334 | 0.9241 | 20.4459 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wang, Y.; Guan, N.; Li, J.; Wang, X. A Virtual Staining Method Based on Self-Supervised GAN for Fourier Ptychographic Microscopy Colorful Imaging. Appl. Sci. 2024, 14, 1662. https://doi.org/10.3390/app14041662
Wang Y, Guan N, Li J, Wang X. A Virtual Staining Method Based on Self-Supervised GAN for Fourier Ptychographic Microscopy Colorful Imaging. Applied Sciences. 2024; 14(4):1662. https://doi.org/10.3390/app14041662
Chicago/Turabian StyleWang, Yan, Nan Guan, Jie Li, and **aoli Wang. 2024. "A Virtual Staining Method Based on Self-Supervised GAN for Fourier Ptychographic Microscopy Colorful Imaging" Applied Sciences 14, no. 4: 1662. https://doi.org/10.3390/app14041662