Feature-prompting GBMSeg: One Shot Reference Guided Training-Free Feature Matching for Glomerular Basement Membrane Segmentation and Quantification
Xueyu Liu, Guangze Shi, Rui Wang, Yexin Lai, Jianan Zhang, Lele Sun, Quan Yang, Yongfei Wu*, Weixia Han, Ming Li, and Wen Zheng
1Taiyuan University of Technology,
2The Second Affiliated Hospital of Shanxi Medical University,
3Shanxi Provincial People's Hospital
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.
- Cuda 12.0
- Python 3.9.18
- PyTorch 2.0.0
../ # parent directory
├── ./data # data path
│ ├── reference_image # the one-shot reference image
│ ├── reference_mask # the one-shot reference mask
│ ├── target_image # testing images
cd GBMSeg/feature-matching
python generate_prompt.py
cd GBMSeg/tools
python automatic_prompt_engineering.py
mkdir GBMSeg/results
cd GBMSeg/segmenting-anything
python segment.py
If you find this project useful in your research, please consider citing:
@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}
}
Thanks DINOv2, SAM. for serving as building blocks of GBMSeg.