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https://github.com/xq141839/SPPNet
SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation (Boost SAM)
https://github.com/xq141839/SPPNet
Last synced: about 2 months ago
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SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation (Boost SAM)
- Host: GitHub
- URL: https://github.com/xq141839/SPPNet
- Owner: xq141839
- License: apache-2.0
- Created: 2023-07-13T08:44:15.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-25T05:11:40.000Z (over 1 year ago)
- Last Synced: 2023-10-25T06:22:46.472Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 92.8 KB
- Stars: 14
- Watchers: 2
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Segment-Anything - [code
README
# 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](https://sites.google.com/view/mlmi2023). The preprint has been available at [arXiv](https://arxiv.org/abs/2308.12231).
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](https://github.com/WuJunde/Medical-SAM-Adapter).
## Requirements
1. pytorch==1.10.0
2. pytorch-lightning==1.1.0
3. albumentations==0.3.2
4. seaborn
5. 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
```
## Acknowledgement
The codes are modified from [SAM](https://github.com/facebookresearch/segment-anything) and [MobileSAM](https://github.com/ChaoningZhang/MobileSAM).