README.md

# PRISM

[PRISM](https://arxiv.org/abs/2404.15028): A **P**romptable and **R**obust **I**nteractive **S**egmentation **M**odel with Visual Prompts

Placenta application:

[PRISM Lite](https://arxiv.org/abs/2408.05372): A lightweight model for interactive 3D placenta segmentation in ultrasound

Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound Images ([arXiv version](https://arxiv.org/abs/2407.08020))


## News
[07/07/24] Check out the decent performance/version of [PRISM on placenta segmentation in ultrasound images](https://github.com/MedICL-VU/PRISM-placenta).

[05/13/24] Our work is early accepted by MICCAI 2024.

[03/07/24] The [pretrained PRISM](https://drive.google.com/drive/u/1/folders/1B6Df44Gd9PEBGPkE1FwC8Ds4jefCekUB) models and [preprocessed datasets](https://drive.google.com/drive/folders/13uGNb2WQhSQcBQIUhnvYJere1LBYGDsW?usp=sharing) are uploaded.

## TODO


demo (gradio)



## Introduction of PRISM
<img src='figs/framework_v1.png' width='600'>

PRISM is a robust model/method for interactive segmentation in medical imaging. We strive for human-level performance, as a human-in-loop interactive segmentation model with prompts should gradually refine its outcomes until they closely match inter-rater variability. 



## PRISM tumor segmentation examples
Briefly, PRISM produces tumor segmentation with mean Dice values of **93.79 (colon), 94.48 (pancreas), 94.18 (liver), and 96.58 (kidney)**.

  |   |   |
:-------------------------:|:-------------------------:
Iterative correction for colon tumor | ![iterative_colon](figs/iterative_results.png)
Iterative correction for multiple tumors | ![iterative_all](figs/iterative_results_supp.png)
Qualitative results with compared methods | ![qualitative_results](figs/qualitative_results.png)

The quantitative results can be viewed in our [paper](https://arxiv.org/abs/2404.15028).

## Datasets
The anatomical differences among individuals and ambiguous boundaries are present in the datasets.

- Our preprocessed
  
     We used four public [datasets](https://drive.google.com/drive/folders/13uGNb2WQhSQcBQIUhnvYJere1LBYGDsW?usp=sharing) for 3D tumor segmentation in [colon](https://drive.google.com/drive/u/1/folders/1bt17794HCZfmJ2MLh5w0Y_IAJyUj6ti2), [pancreas](https://drive.google.com/drive/u/1/folders/1NncGDG5Cu795WJTmBse-Lm0GrJmtvTdc), [liver](https://drive.google.com/drive/u/1/folders/1vDM2VkNAT5dvFX5XTRhPe6b7zwYWqU_U) and [kidney](https://drive.google.com/drive/u/1/folders/12UDho-JEZHfK1c1laD5dBFNxvJumcoDF). 

- Original
 
     Here are the links for the datasets: [MSD-colon](http://medicaldecathlon.com/), [MSD-pancreas](http://medicaldecathlon.com/), [LiTS2017](https://competitions.codalab.org/competitions/17094) and [KiTS2021](https://kits-challenge.org/kits21/).





## Models
| colon | pancreas | liver | kidney |
|------------------------------|------------------------------|------------------------------|------------------------------|
| [Download](https://drive.google.com/drive/u/1/folders/1nPUC0cCsyA_w-tKkhL_Bw7lesBorGzCl) |[Download](https://drive.google.com/drive/u/1/folders/1JPiF7wtSnbFdl0ZLmFQt1b4H-XH4FDrM)| [Download](https://drive.google.com/drive/u/1/folders/1JAFOca1FxWebzZjRa1lKo1OAv0HXqeh6) |[Download](https://drive.google.com/drive/u/1/folders/1sN0HQLM-LfWB5Kp119YwMsZIfv3VJj7S)|


## Get Started

**Installation**
```
conda create -n prism python=3.9
conda activate prism
sudo install git
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113 # install pytorch
pip install git+https://github.com/facebookresearch/segment-anything.git # install segment anything packages
pip install git+https://github.com/deepmind/surface-distance.git # for normalized surface dice (NSD) evaluation
pip install -r requirements.txt
```


**Train**

```
python train.py --data colon --data_dir your_data_directory --save_name your_save_name --multiple_outputs --dynamic --use_box --refine
```

add "--use_scribble" and "--efficient_scribble" if you want to train with scribbles.

**Train (Distributed Data Parallel)**

the only difference between this and above (train) command is the use of "--ddp".
```
python train.py --data colon --data_dir your_data_directory --save_name your_save_name -multiple_outputs --dynamic --use_box --refine --ddp
```



**Test**

put downloaded pretrained model under the implementation directory
```
python test.py --data colon --data_dir your_data_directory --split test --checkpoint best --save_name prism_pretrain --num_clicks 1 --iter_nums 11 --multiple_outputs --use_box --use_scribble --efficient_scribble --refine --refine_test
```




**FAQ**

if you got the error as AttributeError: module 'cv2' has no attribute 'ximgproc', please check [this](https://stackoverflow.com/questions/57427233/module-cv2-cv2-has-no-attribute-ximgproc) out

DDP mode has lower Dice and more epoch numbers may solve it

On my end, combining trainer and trainer_basic speeds up

training the model without refine module (as we reported in the paper) has better accuracy than with refine but not using it


## License

The model is licensed under the [Apache 2.0 license](LICENSE)


## Acknowledgements
Thanks for the code from: [SAM](https://github.com/facebookresearch/segment-anything), [SAM-Med3D](https://github.com/uni-medical/SAM-Med3D), [ProMISe](https://github.com/MedICL-VU/ProMISe), [ScribblePrompt](https://github.com/halleewong/ScribblePrompt), [nnU-Net](https://github.com/MIC-DKFZ/nnUNet)

If you find this repository useful, please consider citing:
```
@inproceedings{li2024prism,
  title={Prism: A promptable and robust interactive segmentation model with visual prompts},
  author={Li, Hao and Liu, Han and Hu, Dewei and Wang, Jiacheng and Oguz, Ipek},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={389--399},
  year={2024},
  organization={Springer}
}
```
```
@inproceedings{li2024interactive,
  title={Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound Images},
  author={Li, Hao and Oguz, Baris and Arenas, Gabriel and Yao, Xing and Wang, Jiacheng and Pouch, Alison and Byram, Brett and Schwartz, Nadav and Oguz, Ipek},
  booktitle={International Workshop on Advances in Simplifying Medical Ultrasound},
  pages={132--142},
  year={2024},
  organization={Springer}
}
```
Please send an email to hao.li.1@vanderbilt.edu for any questions and always happy to help! :)