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SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation (Boost SAM)
News
2023.07.14: The SPPNet model and training code have been submitted. The paper will be updated later.
2023.08.24: The paper has been accepted by MICCAI-MLMI 2023. The preprint has been available at arXiv.
2023.09.27: Release a New Beta version for users who want to fine-tune the SAM pre-trained image encoder. We add the adapter based on Medical-SAM-Adapter.
Requirements
- pytorch==1.10.0
- pytorch-lightning==1.1.0
- albumentations==0.3.2
- seaborn
- sklearn
Environment
NVIDIA RTX2080Ti Tensor Core GPU, 4-core CPU, and 28GB RAM
Evaluation on MoNuSeg-2018
| Method | mIoU(%) | DSC(%) | Params(M) | FLOPs | FPS |
|---|---|---|---|---|---|
| SAM (Fine-tuned) | 60.18±8.15 | 74.76±7.00 | 635.93 | 2736.63 | 1.39 |
| SPPNet | 66.43±4.32 | 79.77±3.11 | 9.79 | 39.90 | 22.61 |
Dataset
To apply the model on a custom dataset, the data tree should be constructed as:
├── data
├── images
├── image_1.png
├── image_2.png
├── image_n.png
├── masks
├── image_1.npy
├── image_2.npy
├── image_n.npy
Train
python train.py --dataset your/data/path --jsonfile your/json/path --loss dice --batch 16 --lr 0.001 --epoch 50
Evaluation
python eval.py --dataset your/data/path --jsonfile your/json/path --model save_models/model_best.pth --debug True