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# EMcopilot: Your label-free copilot for automated electron microscopy image analysis
## Training and Code Details
For detailed training procedures and source code, please refer to our GitHub repository:
[EMcopilot GitHub Repository](https://github.com/WenhaoYuan337/EMcopilot)
## Usage
1. **Train and predict segmentation models**:
* `00_01_sam_binary_masking.py` - Generates coarse masks using the SAM model.
3. **Generate and analyze synthetic masks**:
* `02_01_sam_mask_analysis.py` - Analyzes SAM mask properties and extracts morphology prior.
* `02_02_random_mask_generate.py` - Generates synthetic masks by augmenting existing masks.
5. **Generate and evaluate images**:
* `03_01_p2p_train.py` - Trains a Pix2Pix model for mask-to-EMimage translation.
* `04_01_pix2pix_predict.py` - Runs inference using the trained Pix2Pix model.
6. **Domain Adaptation**:
* `05_01_domain_adaptation.py` - Applies domain adaptation, including noise and contrast augmentation.
7. **UNet++ Training and Inference**:
* `06_01_unet++_train.py` - Trains a CBAM-enhanced UNet++ model for segmentation.
* `07_01_unet++_predict.py` - Performs inference using the trained UNet++ model.
8. **Analyze DM4 microscopy data**:
* `08_01_in_situ_analysis.py` - Analyze HAADF-STEM images of supported nanoparticles in real time.
## Installation
Install required packages using:
```bash
pip install -r requirements.txt
```
## Citation
If you find our code or data useful in your research, please cite our paper:
```
@misc{yuan2024deepgenerativemodelsassistedautomated,
title={Deep Generative Models-Assisted Automated Labeling for Electron Microscopy Images Segmentation},
author={Wenhao Yuan and Bingqing Yao and Shengdong Tan and Fengqi You and Qian He},
year={2024},
eprint={2407.19544},
archivePrefix={arXiv},
primaryClass={cond-mat.mtrl-sci},
url={https://arxiv.org/abs/2407.19544},
}
```
Copyright | 2025 Qian's Lab@NUS
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