Awesome

One-Prompt to Segment All Meical Image

One-Prompt to Segment All Medical Images, or say One-Prompt, combines the strengths of one-shot and interactive methods. In the inference stage, with just one prompted sample, it can adeptly handle the unseen task in a single forward pass.

This method is elaborated in the paper One-Prompt to Segment All Medical Images.

A Quick Overview

Requirement

Install the environment:

conda env create -f environment.yml

conda activate oneprompt

Dataset

Download the open-source datasets

We collected 78 open-source datasets for training and testing the model. The datasets and their download links are in here.

Download the prompts

The prompts corresponding to the datasets can be downloaded here. Each prompt is saved a json message with the format {DATASET_NAME, SAMPLE_INDEX, PROMPT_TYPE, PROMPT_CONTENT}

Train

run python train.py -net oneprompt -mod one_adpt -exp_name basic_exp -b 64 -dataset oneprompt -data_path *../data* -baseline 'unet'

Test Examples

Melanoma Segmentation from Skin Images (2D)

1. Download ISIC dataset part 1 from https://challenge.isic-archive.com/data/. Then put the csv files in "./data/isic" under your data path. Your dataset folder under "your_data_path" should be like:

ISIC/

 ISBI2016_ISIC_Part1_Test_Data/...
 
 ISBI2016_ISIC_Part1_Training_Data/...
 
 ISBI2016_ISIC_Part1_Test_GroundTruth.csv
 
 ISBI2016_ISIC_Part1_Training_GroundTruth.csv

2. run: python val.py -net oneprompt -mod one_adpt -exp_name One-ISIC -weights *weight_path* -b 1 -dataset isic -data_path ../dataset/isic -vis 10 -baseline 'unet' change "data_path" and "exp_name" for your own useage. you can change "exp_name" to anything you want.

You can descrease the image size or batch size b if out of memory.

3. Evaluation: The code can automatically evaluate the model on the test set during traing, set "--val_freq" to control how many epoches you want to evaluate once. You can also run val.py for the independent evaluation.

4. Result Visualization: You can set "--vis" parameter to control how many epoches you want to see the results in the training or evaluation process.

In default, everything will be saved at ./logs/

REFUGE: Optic-disc Segmentation from Fundus Images (2D)

REFUGE dataset contains 1200 fundus images with optic disc/cup segmentations and clinical glaucoma labels.

1. Dowaload the dataset manually from here, or using command lines:

git lfs install

git clone git@hf.co:datasets/realslimman/REFUGE-MultiRater

unzip and put the dataset to the target folder

unzip ./REFUGE-MultiRater.zip

mv REFUGE-MultiRater ./data

2. For training the adapter, run: python val.py -net oneprompt -mod one_adpt -exp_name One-REFUGE -weights *weight_path* -b 1 -baseline 'unet' -dataset REFUGE -data_path ./data/REFUGE-MultiRater you can change "exp_name" to anything you want.

You can descrease the image size or batch size b if out of memory.

Run on your own dataset

It is simple to run omeprompt on the other datasets. Just write another dataset class following which in ./dataset.py. You only need to make sure you return a dict with

 {
             'image': A tensor saving images with size [C,H,W] for 2D image, size [C, H, W, D] for 3D data.
             D is the depth of 3D volume, C is the channel of a scan/frame, which is commonly 1 for CT, MRI, US data. 
             If processing, say like a colorful surgical video, D could the number of time frames, and C will be 3 for a RGB frame.

             'label': The target masks. Same size with the images except the resolutions (H and W).

             'p_label': The prompt label to decide positive/negative prompt. To simplify, you can always set 1 if don't need the negative prompt function.

             'pt': The prompt. e.g., a click prompt should be [x of click, y of click], one click for each scan/frame if using 3d data.

             'image_meta_dict': Optional. if you want save/visulize the result, you should put the name of the image in it with the key ['filename_or_obj'].

             ...(others as you want)
 }

Cite

@InProceedings{Wu_2024_CVPR,
    author    = {Wu, Junde and Xu, Min},
    title     = {One-Prompt to Segment All Medical Images},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {11302-11312}
}