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

Usage

Train and predict segmentation models:
- 00_01_sam_binary_masking.py - Generates coarse masks using the SAM model.
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.
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.
Domain Adaptation:
- 05_01_domain_adaptation.py - Applies domain adaptation, including noise and contrast augmentation.
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.
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:

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}, 
}