DiffOSeg-Code

👋This repository contains the official pytorch implementation of our MICCAI 2025 paper "DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model".

Updates

[2025.08.27]🔥 Our work has been shortlisted for the MICCAI 2025 Best Paper and Young Scientist Awards, ranking among the top 25 of 1014 accepted papers (from 3447 submissions) !
[2025.06.18]📩 Our work has been accepted by MICCAI 2025 !
[2024.10.23]🥈 We won 2nd place on both tasks of MMIS-2024@ACM MM 2024 !

Method

In this study, we propose DiffOSeg, a two-stage diffusion-based framework, which aims to simultaneously achieve both consensus-driven (combining all experts' opinions) and preference-driven (reflecting experts' individual assessments) segmentation. Stage I establishes population consensus through a probabilistic consensus strategy, while Stage II captures expert-specific preference via adaptive prompts. For more details, please refer to our paper.

Usage

Task-List

[ ] Add NPC-170 process.
[ ] Polish code.

Installation & Data Preparation

See INSTALL.md for the installation of dependencies and dataset preperation required to run this codebase.

Training

Specify parameters such as stage in params.yml

python ddpm_train.py --params params.yml --gpu gpu_id

Inference

Specify parameters such as stage in params_eval.yml

python ddpm_eval.py  --params params_eval.yml --gpu gpu_id

Citation

If you found this repository useful to you, please consider giving a star ⭐️ and citing our paper:

@article{zhang2025diffoseg,
  title={DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model},
  author={Zhang, Han and Luo, Xiangde and Chen, Yong and Li, Kang},
  journal={arXiv preprint arXiv:2507.13087},
  year={2025}
}

Acknowledgements

Greatly appreciate the tremendous effort for the following projects:

ccdm-stochastic-segmentation, D-Persona, PromptIR, PromptMR, UniSeg

DiffOSeg

Install / Use

README