Related Experiment Video
Updated: May 10, 2026
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Frequency-aware domain randomization for single-source domain generalization in medical image segmentation
Jiayi Wu1, Zikai Chen2, Yiyi Chen1
1National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
The novel Random Domain Generalization (RandDG) method improves medical image segmentation by addressing domain shift challenges. This frequency-aware approach enhances generalization ability, showing significant improvements on abdominal and prostate datasets.
Area of Science:
- Medical Image Analysis
- Deep Learning
- Computer Vision
Background:
- Deep learning models for medical image segmentation struggle with domain shift, where test data differs from training data.
- Single-source domain generalization (DG) trains models on one domain to generalize to unseen domains, but current methods suffer from texture bias and limited style diversity.
Purpose of the Study:
- To propose Random Domain Generalization (RandDG), a frequency-aware method for single-source DG in medical image segmentation.
- To enhance generalization ability via coordinated input and feature space perturbations using a lightweight frequency-domain architecture.
Main Methods:
- Introduced a Global U-Shape Network (GUNet) for efficient long-range dependency modeling using Fourier transforms.
- Employed a Uniform Low Frequency spectrum Transform (ULoFT) filter for feature-space perturbation, mixing source statistics with uniform values.
- Utilized a dual-space randomization framework with input-space augmentation (GIN filters) and feature-space perturbation (ULoFT), regularized by a consistency loss.
Main Results:
- Achieved superior generalization on abdominal CT-MRI and cross-center prostate datasets, significantly outperforming competitive methods.
- Demonstrated an average DSC of 87.96% and HD of 4.82 mm on the abdominal dataset, and 75.95% DSC and 8.36 mm HD on the prostate dataset.
- Showed substantial improvements over the UNet baseline, with up to 19.36% higher average DSC and reduced average HD.
Conclusions:
- RandDG effectively addresses texture bias and limited style diversity in single-source DG for medical image segmentation using a frequency-aware dual-space randomization framework.
- The method shows promise for practical clinical deployment where target domain data is unavailable.
