📄 Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation (TriD)

This is the official pytorch implementation of our MICCAI 2023 paper "Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation". In this paper, we propose a multi-source domain generalization method called Treasure in Distribution (TriD) to construct an unprecedented search space for feature-space domain randomization.

Requirements

Python 3.7
Pytorch 1.8.0

Usage

Installation

• Clone this repo

git clone https://github.com/Chen-Ziyang/TriD.git
cd TriD/TriD-master

Data Preparation

OD/OC Segmentation
Prostate Segmentation

OD/OC Segmentation

We take the scenario using BinRushed (target domain) and other four datasets (source domains) as the example.

cd OPTIC
# Training
CUDA_VISIBLE_DEVICES=0 python main.py --mode train_DG --mixstyle_layers layer1 layer2 --random_type TriD --Target_Dataset BinRushed --Source_Dataset Magrabia REFUGE ORIGA Drishti_GS
# Test
CUDA_VISIBLE_DEVICES=0 python main.py --mode single_test --load_time TIME_OF_MODEL --Target_Dataset BinRushed

Prostate Segmentation

We take the scenario using BMC (target domain) and other five datasets (source domains) as the example.

cd PROSTATE
# Training
CUDA_VISIBLE_DEVICES=0 python main.py --mode train_DG --mixstyle_layers layer1 layer2 --random_type TriD --Target_Dataset BMC --Source_Dataset BIDMC HK I2CVB RUNMC UCL
# Test
CUDA_VISIBLE_DEVICES=0 python main.py --mode single_test --load_time TIME_OF_MODEL --Target_Dataset BMC

Acknowledgement

Part of the code is revised from the Pytorch implementation of DoCR.

Citation ✏️ 📄

If you find this repo useful for your research, please consider citing the paper as follows:

@inproceedings{chen2023treasure,
  title={Treasure in distribution: a domain randomization based multi-source domain generalization for 2d medical image segmentation},
  author={Chen, Ziyang and Pan, Yongsheng and Ye, Yiwen and Cui, Hengfei and Xia, Yong},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={89--99},
  year={2023},
  organization={Springer}
}