Deep Learning Realizes Photoacoustic Imaging Artifact Removal
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Framework
2.2. Deep Learning Algorithm for Photoacoustic Image Segmentation
2.3. Deep Learning Algorithm for Photoacoustic Image Artifact Removal
3. Results
3.1. Experimental Setup
3.2. Evaluation Metrics
3.3. Brain Tumor Dataset
3.3.1. The Experimental Results and Analysis of the Removal of Artifacts
3.3.2. Photoacoustic Image Segmentation Experimental Results and Analysis
3.4. Photoacoustic Detection Experiment
3.4.1. Data Acquisition
3.4.2. The Experimental Results and Analysis of the Removal of Artifacts
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Parameter |
---|---|
CPU | Intel(R) Core(TM) i7-10700 |
RAM | 32 GB |
GPU | NVIDIA Quadro P2200 |
Programming language | Python 3.10 |
Deep learning framework | PyTorch 2.2.1, CUDA 12.1 |
Dependency library | Numpy 1.26.4, tqdm 4.66.2, tensorboard 2.16.2, opencv-python 4.9.0.80 |
Network | Main Hyperparameters | Specific Values |
---|---|---|
YOLOv8 | Batch size | 16 |
Image size | 640 | |
Optimiser | Adam | |
Momentum | 0.937 | |
Initial learning rate | 0.01 | |
epoch | 300 | |
Pix2Pix | Batch_size | 1 |
Momentum | 0.5 | |
Optimiser | Adam | |
Loss function | cGAN | |
Initial learning rate | 0.0002 | |
epoch | 200 |
TR | Gaussian Filter | Cyclegan | Pix2Pix | YOLOv8 | YOLOv8-Cyclegan | YOLOv8-Pix2Pix | |
---|---|---|---|---|---|---|---|
PSNR | 28.918 | 28.821 | 29.998 | 30.421 | 29.113 | 29.568 | 31.459 |
SSIM | 0.566 | 0.526 | 0.571 | 0.612 | 0.491 | 0.513 | 0.650 |
Precision | Recall | Accuracy | IoU | Mean BFScore | Dice | ||
---|---|---|---|---|---|---|---|
Artifact Image | All | 0.915 | 0.852 | 0.961 | 0.773 | 0.863 | 0.864 |
Brain tumor | 0.843 | 0.704 | 0.970 | 0.621 | 0.766 | 0.766 | |
Brain | 0.986 | 1 | 0.952 | 0.924 | 0.961 | 0.961 | |
Artifact-free Image | All | 0.955 | 0.932 | 0.986 | 0.946 | 0.972 | 0.973 |
Brain tumor | 0.918 | 0.864 | 0.994 | 0.928 | 0.963 | 0.963 | |
Brain | 0.992 | 1 | 0.978 | 0.964 | 0.982 | 0.982 |
Phantom I | Phantom II | Phantom III | Phantom IV | ||
---|---|---|---|---|---|
Artifact Image | PSNR | 74.089 | 78.531 | 71.162 | 54.164 |
SSIM | 0.993 | 0.995 | 0.985 | 0.646 | |
Artifact-free Image | PSNR | 79.988 | 78.933 | 74.514 | 73.616 |
SSIM | 0.998 | 0.997 | 0.996 | 0.992 |
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He, R.; Chen, Y.; Jiang, Y.; Lei, Y.; Yan, S.; Zhang, J.; Cao, H. Deep Learning Realizes Photoacoustic Imaging Artifact Removal. Appl. Sci. 2024, 14, 5161. https://doi.org/10.3390/app14125161
He R, Chen Y, Jiang Y, Lei Y, Yan S, Zhang J, Cao H. Deep Learning Realizes Photoacoustic Imaging Artifact Removal. Applied Sciences. 2024; 14(12):5161. https://doi.org/10.3390/app14125161
Chicago/Turabian StyleHe, Ruonan, Yi Chen, Yufei Jiang, Yuyang Lei, Shengxian Yan, Jing Zhang, and Hui Cao. 2024. "Deep Learning Realizes Photoacoustic Imaging Artifact Removal" Applied Sciences 14, no. 12: 5161. https://doi.org/10.3390/app14125161
APA StyleHe, R., Chen, Y., Jiang, Y., Lei, Y., Yan, S., Zhang, J., & Cao, H. (2024). Deep Learning Realizes Photoacoustic Imaging Artifact Removal. Applied Sciences, 14(12), 5161. https://doi.org/10.3390/app14125161