This code is a pytorch implementation of our paper "GL-LCM: Global-Local Latent Consistency Models for Fast High-Resolution Bone Suppression in Chest X-Ray Images" accepted by MICCAI 2025. [🎩 arXiv][🌐 Project Page]
🎥 Visualization before (left) and after (right) bone suppression using GL-LCM
💡 Primary contributions
To overcome these challenges, we propose Global-Local Latent Consistency Model (GL-LCM). This is a novel framework for fast high-resolution bone suppression in CXR images based on Latent Consistency Models (LCMs). Our key contributions are summarized as follows:
🕐The GL-LCM architecture facilitates effective bone suppression while retaining texture details. This is achieved through the design of dual-path sampling in the latent space combined with global-local fusion in the pixel space.
🕑GL-LCM significantly enhances inference efficiency, which requires only approximately 10% of the inference time of current diffusion-based methods, making it more suitable for clinical applications.
🕒We introduce Local-Enhanced Guidance (LEG) to mitigate potential boundary artifacts and detail blurring issues in local-path sampling, without additional training.
🕓Extensive experiments on both the self-collected dataset SZCH-X-Rays and the public dataset JSRT demonstrate exceptional performance and efficiency of our GL-LCM.
🧗 Proposed method
Overview of GL-LCM framework. (a) Lung segmentation in the pixel space, (b) Dual-path sampling in the latent space, and (c) Global-local fusion in the pixel space.
🏎️ Comparisons
- Qualitative Results on SZCH-X-Rays and JSRT
- Quantitative results on SZCH-X-Rays
Method
BSR (%)↑
MSE (10⁻³)↓
PSNR↑
LPIPS↓
Universal Method
VAE
91.281 ± 3.088
1.169 ± 1.059
30.018 ± 2.007
0.237 ± 0.047
VQ-VAE
94.485 ± 2.407
0.645 ± 0.596
32.600 ± 2.071
0.137 ± 0.029
VQGAN
94.330 ± 3.402
0.923 ± 2.478
32.096 ± 2.420
0.083 ± 0.020
Task-Specific Method
Gusarev et al.
94.142 ± 2.666
1.028 ± 2.201
31.369 ± 2.385
0.156 ± 0.031
MCA-Net
95.442 ± 2.095
0.611 ± 0.435
32.689 ± 1.939
0.079 ± 0.018
ResNet-BS
94.508 ± 1.733
0.646 ± 0.339
32.265 ± 1.635
0.107 ± 0.022
Wang et al.
89.767 ± 6.079
1.080 ± 0.610
29.963 ± 1.378
0.072 ± 0.016
BS-Diff
92.428 ± 3.258
0.947 ± 0.510
30.627 ± 1.690
0.212 ± 0.041
BS-LDM
94.159 ± 2.751
0.701 ± 0.293
31.953 ± 1.969
0.070 ± 0.018
GL-LCM (Ours)
95.611 ± 1.529
0.512 ± 0.293
33.347 ± 1.829
0.056 ± 0.015
- Quantitative results on JSRT
Method
BSR (%)↑
MSE (10⁻³)↓
PSNR↑
LPIPS↓
Universal Method
VAE
85.646 ± 9.327
1.224 ± 0.749
29.814 ± 2.364
0.155 ± 0.032
VQ-VAE
86.445 ± 8.881
0.986 ± 0.596
30.712 ± 2.273
0.062 ± 0.017
VQGAN
86.594 ± 8.916
1.002 ± 0.606
30.635 ± 2.255
0.061 ± 0.017
Task-Specific Method
Gusarev et al.
89.283 ± 8.288
0.821 ± 0.570
31.700 ± 2.594
0.100 ± 0.024
MCA-Net
86.887 ± 9.825
0.876 ± 0.625
31.577 ± 2.905
0.057 ± 0.017
ResNet-BS
88.782 ± 8.905
0.960 ± 0.661
31.021 ± 2.576
0.060 ± 0.016
Wang et al.
89.679 ± 9.477
1.013 ± 0.655
30.681 ± 2.431
0.075 ± 0.015
BS-Diff
88.707 ± 8.859
1.003 ± 0.655
30.765 ± 2.504
0.154 ± 0.037
BS-LDM
89.322 ± 9.562
0.783 ± 0.632
32.307 ± 3.231
0.058 ± 0.017
GL-LCM (Ours)
90.056 ± 10.635
0.746 ± 0.680
32.951 ± 3.799
0.052 ± 0.015
- Inference efficiency comparison on SZCH-X-Rays
Method
Sampler
Sampling Steps
Parameters
Inference Time (s)
BS-Diff
DDPM
1000
254.7M
108.86
BS-LDM
DDPM
1000
421.3M
84.62
GL-LCM (Ours)
LCM
50
436.9M
8.54
🙇 Ablation study
- Qualitative results of LEG on SZCH-X-Rays and JSRT
A pseudo-color zoomed-in view is shown in the bottom right corner, and the green arrows mark the boundary artifacts.
- Quantitative results of LEG on SZCH-X-Rays and JSRT
Guidance Method
SZCH-X-Rays
JSRT
PSNR↑
LPIPS↓
PSNR↑
LPIPS↓
Vanilla Guidance
32.777 ± 2.091
0.058 ± 0.016
32.296 ± 3.454
0.073 ± 0.020
CFG
32.315 ± 1.717
0.068 ± 0.013
32.613 ± 3.604
0.070 ± 0.015
LEG (Ours)
33.347 ± 1.829
0.056 ± 0.015
32.951 ± 3.799
0.052 ± 0.015
- Quantitative results of Poisson Fusion on SZCH-X-Rays and JSRT
To do the evaluation process of VQGAN for visualization, please run the following command:
python vq-gan_eval.py
To do the evaluation process of GL-LCM, please run the following command:
python batch_lcm_eval.py
🐎 Training
If you want to train our model by yourself, you are primarily expected to split the whole dataset into training, validation and testing sets. Please run the following command:
python dataSegmentation.py
Then, you can run the following command to train the VQGAN model:
python vq-gan_train.py
Then after finishing the training of VQGAN, you can use the saved VQGAN model when training the noise estimator network of GL-LCM by running the following command:
python lcm_train.py
🔍 Metrics
You can also run the following command about evaluation metrics including BSR, MSE, PSNR and LPIPS:
python metrics.py
📢 Citation
@inproceedings{sun2025gl,
title={Gl-lcm: Global-local latent consistency models for fast high-resolution bone suppression in chest X-ray images},
author={Sun, Yifei and Chen, Zhanghao and Zheng, Hao and Lu, Yuqing and Duan, Lixin and Min, Wenwen and Fan, Fenglei and Elazab, Ahmed and Wan, Xiang and Wang, Changmiao and Ge, Ruiquan},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
year={2025},
organization={Springer}
}
