ScribblePrompt

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Official implementation of ScribblePrompt: Fast and Flexible Interactive Segmentation for any Biomedical Image accepted at ECCV 2024

Hallee E. Wong, Marianne Rakic, John Guttag, Adrian V. Dalca

Updates

• (2024-07-01) ScribblePrompt has been accepted to ECCV 2024!
• (2024-06-17) ScribblePrompt won the Bench-to-Bedside Award at the CVPR 2024 DCAMI Workshop!
• (2024-04-16) Released MedScribble -- a diverse dataset of segmentation tasks with scribble annotations
• (2024-04-15) An updated version of the paper is on arXiv!
• (2024-04-14) Added Google Colab Tutorial
• (2024-01-19) Released scribble simulation code
• (2023-12-15) Released model code and weights
• (2023-12-12) Paper and online demo released

Overview

ScribblePrompt is an interactive segmentation tool that enables users to segment unseen structures in medical images using scribbles, clicks, and bounding boxes.

Try ScribblePrompt

• Interactive online demo on Hugging Face Spaces
• See Installation and Getting Started for how to run the Gradio demo locally
• Jupyter notebook colab tutorial

Models

We provide checkpoints for two versions of ScribblePrompt:

• ScribblePrompt-UNet with an efficient fully-convolutional architecture

• ScribblePrompt-SAM based on the Segment Anything Model

Both models have been trained with iterative scribbles, click, and bounding box interactions on a diverse collection of 65 medical imaging datasets with both real and synthetic labels.

MedScribble Dataset

We release MedScribble, a dataset of multi-annotator scribble annotations for diverse biomedical image segmentation tasks, under ./MedScribble. See the readme for more info and ./MedScribble/tutorial.ipynb for a preview of the data.

Installation

You can install scribbleprompt in two ways:

• With pip:

pip install git+https://github.com/halleewong/ScribblePrompt.git

• Manually: cloning it and installing dependencies

git clone https://github.com/halleewong/ScribblePrompt
python -m pip install -r ./ScribblePrompt/requirements.txt
export PYTHONPATH="$PYTHONPATH:$(realpath ./ScribblePrompt)"

The following optional dependencies are necessary for the local demo:

pip install gradio==3.40.1

Getting Started

First, download the model checkpoints to ./checkpoints.

To run an interactive demo locally:

python demos/app.py

To instantiate ScribblePrompt-UNet and make a prediction:

from scribbleprompt import ScribblePromptUNet

sp_unet = ScribblePromptUNet()

mask = sp_unet.predict(
    image,        # (B, 1, H, W) 
    point_coords, # (B, n, 2)
    point_labels, # (B, n)
    scribbles,    # (B, 2, H, W)
    box,          # (B, n, 4)
    mask_input,   # (B, 1, H, W)
) # -> (B, 1, H, W) 

To instantiate ScribblePrompt-SAM and make a prediction:

from scribbleprompt import ScribblePromptSAM

sp_sam = ScribblePromptSAM()

mask, img_features, low_res_logits = sp_sam.predict(
    image,        # (B, 1, H, W) 
    point_coords, # (B, n, 2)
    point_labels, # (B, n)
    scribbles,    # (B, 2, H, W)
    box,          # (B, n, 4)
    mask_input,   # (B, 1, 256, 256)
) # -> (B, 1, H, W), (B, 16, 256, 256), (B, 1, 256, 256)

For best results, image should have spatial dimensions $(H,W) = (128,128)$ and pixel values min-max normalized to the $[0,1]$ range.

For ScribblePrompt-UNet, mask_input should be the logits from the previous prediction. For ScribblePrompt-SAM, mask_input should be low_res_logits from the previous prediction.

To Do

• Release Gradio demo
• Release model code and weights
• Release jupyter notebook tutorial
• Release scribble simulation code
• Release MedScribble dataset
• Release training code
• Release segmentation labels collected using ScribblePrompt

Acknowledgements

• Code for ScribblePrompt-SAM builds on Segment Anything. Thanks to Meta AI for open-sourcing the model.

• We use functions from voxsynth for applying random deformations during scribble simulation

Citation

If you find our work or any of our materials useful, please cite our paper:

@article{wong2024scribbleprompt,
  title={ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image},
  author={Hallee E. Wong and Marianne Rakic and John Guttag and Adrian V. Dalca},
  journal={European Conference on Computer Vision (ECCV)},
  year={2024},
}

License

Code for this project is released under the Apache 2.0 License