Related Experiment Video
Updated: May 28, 2026
02:09
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
Published on: April 12, 2024
Label-Free Lung MRI Segmentation via Misalignment-Aware Diffusion Translation
Nejung Rue1, Gyeongdeok Jo2, Inye Na2
1Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea.
Journal of Imaging Informatics in Medicine
|May 26, 2026
Summary
This study introduces a novel method for lung MRI segmentation using a misalignment-aware diffusion framework. It generates synthetic CT images from MRI, enabling accurate, label-free segmentation comparable to direct CT scans.
Area of Science:
- Medical Imaging
- Radiology
- Computer Vision
Background:
- Lung magnetic resonance imaging (MRI) offers radiation-free functional assessment but faces segmentation challenges due to low signal-to-noise ratio and weak contrast.
- Existing methods struggle with limited reliable annotations for supervised learning, hindering automated segmentation.
- Lung computed tomography (CT) provides clear structural delineation and abundant labels, making it a potential source for indirect supervision.
Purpose of the Study:
- To develop a label-free lung MRI segmentation method by addressing the annotation bottleneck.
- To enable accurate segmentation of lung MRI by translating it into a structurally consistent synthetic CT.
- To propose a misalignment-aware diffusion framework for cross-modality medical image translation.
Main Methods:
- A misalignment-aware diffusion framework for MRI-to-CT translation was developed.
- The framework incorporates three-channel diversity and elastic deformation to handle respiratory motion and acquisition discrepancies.
- Normalized mutual information was used as a conditioning signal to convey cross-modality alignment.
Main Results:
- The proposed method achieved a Dice score of 82.38% and a 95th-percentile Hausdorff distance of 33.32 mm for lung segmentation.
- This significantly improved boundary delineation compared to direct MRI input (55.94% and 147.59 mm).
- Results were comparable to direct CT segmentation (81.11% and 38.02 mm).
Conclusions:
- The study presents a practical approach for label-free lung MRI segmentation.
- Misalignment-aware conditioning is a principled strategy for cross-modality medical image translation.
- The developed framework offers a viable alternative for functional lung assessment using MRI.
