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Feature-prompting GBMSeg: One-Shot Reference Guided Training-Free Prompt Engineering for Glomerular Basement Membrane Segmentation

<br>Xueyu Liu, Guangze Shi, Rui Wang, Yexin Lai, Jianan Zhang, Lele Sun, Quan Yang, Yongfei Wu*, Weixia Han, Ming Li, and Wen Zheng<br> ¹Taiyuan University of Technology,   ²The Second Affiliated Hospital of Shanxi Medical University，  ³Shanxi Provincial People's Hospital

🚀🚀This work has been accepted by MICCAI2024!🚀🚀

We present GBMSeg, a training-free framework that automates the segmentation and measurement of the glomerular basement membrane (GBM) in TEM using only one-shot reference images. GBMSeg leverages the robust feature matching capabilities of pretrained foundation models (PFMs) to generate initial prompts, designs novel prompting engineering for optimized prompting methods, and utilizes a class-agnostic segmentation model to obtain the final segmentation result.

Usage

Setup

• Cuda 12.0
• Python 3.9.18
• PyTorch 2.0.0

Datasets

../                          # parent directory
├── ./data                   # data path
│   ├── reference_images      # the one-shot reference image
│   ├── reference_masks       # the one-shot reference mask
│   ├── target_images        # testing images

Usage

python main.py

##  Citation


If you find this project useful in your research, please consider citing:


```BibTeX
@article{liu2024feature,
  title={Feature-prompting GBMSeg: One-Shot Reference Guided Training-Free Prompt Engineering for Glomerular Basement Membrane Segmentation},
  author={Liu, Xueyu and Shi, Guangze and Wang, Rui and Lai, Yexin and Zhang, Jianan and Sun, Lele and Yang, Quan and Wu, Yongfei and Li, MIng and Han, Weixia and others},
  journal={arXiv preprint arXiv:2406.16271},
  year={2024}
}

Acknowledgement

Thanks DINOv2, SAM. for serving as building blocks of GBMSeg.