Requirements

torch 2.0.1, mmcv, mmdet

Dataset

1. download Kumar, CPM-17 and PanNuke datasets.

2. run extract_data.py for data pre-processing.

   datasets
   ├── cpm17
   │   ├── extract_data.py
   │   ├── test
   │   └── train
   ├── cpm17_test_files.npy
   ├── cpm17_train_files.npy
   ├── kumar
   │   ├── extract_data.py
   │   ├── images
   │   └── labels
   ├── kumar_test_files.npy
   ├── kumar_train_files.npy
   ├── pannuke
   │   ├── extract_data.py
   │   ├── Fold 1
   │   ├── Fold 2
   │   ├── Fold 3
   │   ├── Images
   │   └── Masks
   ├── pannuke123_test_files.npy
   ├── pannuke123_train_files.npy
   ├── pannuke123_val_files.npy
   ├── pannuke213_test_files.npy
   ├── pannuke213_train_files.npy
   ├── pannuke213_val_files.npy
   ├── pannuke321_test_files.npy
   ├── pannuke321_train_files.npy
   └── pannuke321_val_files.npy

Training

1. Train the prompter

   cd prompter
   python main.py --config dpa_pannuke123.py --output_dir dpa_pannuke123
   # python main.py --config dpa_pannuke213.py --output_dir dpa_pannuke123
   # python main.py --config dpa_pannuke321.py --output_dir dpa_pannuke123

2. Use the trained prompter to generate nuclei prompts for the validation and test sets.

   python predict_prompts.py --config dpa_pannuke123.py --resume checkpoint/dpa_pannuke123/best.pth
   # python predict_prompts.py --config dpa_pannuke213.py --resume checkpoint/dpa_pannuke213/best.pth
   # python predict_prompts.py --config dpa_pannuke321.py --resume checkpoint/dpa_pannuke321/best.pth

3. Download SAM's pre-trained weights into segmentor/pretrained and train the segmentor.

   cd segmentor
   torchrun --nproc_per_node=4 main.py --config pannuke123_b.py --output_dir pannuke123_b
   # torchrun --nproc_per_node=4 main.py --config pannuke213_b.py --output_dir pannuke213_b
   # torchrun --nproc_per_node=4 main.py --config pannuke321_b.py --output_dir pannuke321_b

Evaluation

see test.sh

Checkpoints

	Kumar	CPM-17	PanNuke123	PanNuke213	PanNuke321
Prompter	OneDrive	OneDrive	OneDrive	OneDrive	OneDrive
Segmentor-B	OneDrive	OneDrive	OneDrive	OneDrive	OneDrive
Segmentor-L	OneDrive	OneDrive	OneDrive	OneDrive	OneDrive
Segmentor-H	OneDrive	OneDrive	OneDrive	OneDrive	OneDrive

Contact

If you have any questions or concerns, feel free to report issues or directly contact us (Zhongyi Shui [email protected]).

Citation

If you find this code useful for your research, please cite us using the following BibTeX entry.

@article{shui2023unleashing,
  title={Unleashing the Power of Prompt-driven Nucleus Instance Segmentation},
  author={Shui, Zhongyi and Zhang, Yunlong and Yao, Kai and Zhu, Chenglu and Sun, Yuxuan and Yang, Lin},
  journal={arXiv preprint arXiv:2311.15939},
  year={2023}
}