Diversified and Personalized Multi-rater Medical Image Segmentation

by Yicheng Wu*+, Xiangde Luo+, Zhe Xu, Xiaoqing Guo, Lie Ju, Zongyuan Ge, Wenjun Liao and Jianfei Cai.

News

<06.18.2024> We release the NPC170 dataset in the MMIS-2024 Grand Challenge;

<05.04.2024> The paper is selected as a Poster (Highlight, top 15%) in CVPR 2024;

<19.03.2024> We released the codes;

<27.02.2024> The paper is accepted by CVPR 2024;

Introduction

This repository is for our paper: "Diversified and Personalized Multi-rater Medical Image Segmentation", the video introduction can be found at YouTube platform.

Here, we study the inherent annotation ambiguity problem in medical image segmentation and use two datasets for the model evaluation (the public LIDC-IDRI and our in-house NPC-170 datasets).

For the LIDC-IDRI dataset, we use its pre-processed version as MedicalMatting. For the NPC-170 dataset, we now open the MMIS-2024 challenge in ACM MM 2024. Please check it for more details.

Requirements

This repository is based on PyTorch 2.0.1+cu118 and Python 3.11.4; All experiments in our paper were conducted on a single NVIDIA GeForce 3090 GPU.

Usage

1. Clone this repo.;

git clone https://github.com/ycwu1997/D-Persona.git

2. Put the data into "./dataset";

3. First-stage training;

cd ./D-Persona/code
# e.g., the LIDC-IDRI dataset
python train_dp.py --stage 1 --val_num 10 --gpu 0

4. Put the first-stage weights into the "../code/";

cp ../models/[YOUR_MODEL_PATH]/DPersona1_LIDC_[IDX]_best.pth ../code/

6. Second-stage training;

python train_dp.py --stage 2 --val_num 100 --gpu 0

6. Test the model;

# e.g., first-stage performance on the LIDC-IDRI dataset
Python evaluate_dp.py --stage 1 --save_path ../models/[YOUR_MODEL_PATH] --test_num 50
# e.g., second-stage performance
Python evaluate_dp.py --stage 2 --save_path ../models/[YOUR_MODEL_PATH] --test_num 500

Citation

If our D-Persona model is useful for your research, please consider citing:

    @InProceedings{Wu_2024_CVPR,
        author    = {Wu, Yicheng and Luo, Xiangde and Xu, Zhe and Guo, Xiaoqing and Ju, Lie and Ge, Zongyuan and Liao, Wenjun and Cai, Jianfei},
        title     = {Diversified and Personalized Multi-rater Medical Image Segmentation},
        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
        month     = {June},
        year      = {2024},
        pages     = {11470-11479}
    }

Acknowledgements:

Our code is adapted from Pionono, MedicalMatting, and Prob. U-Net. Thanks for these authors for their valuable works and hope our model can promote the relevant research as well.

Questions

If any questions, feel free to contact me at 'ycwueli@gmail.com'