diaoquesang/GL-LCM · Hugging Face

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
Guidance Method	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

Fusion Strategy	SZCH-X-Rays		JSRT
Fusion Strategy	PSNR↑	LPIPS↓	PSNR↑	LPIPS↓
✗	31.360 ± 2.079	0.091 ± 0.020	31.638 ± 3.078	0.074 ± 0.021
α-Fusion	29.781 ± 1.522	0.181 ± 0.021	31.784 ± 3.043	0.092 ± 0.013
AE Fusion	30.850 ± 1.806	0.141 ± 0.028	31.835 ± 3.075	0.061 ± 0.017
Poisson Fusion (Ours)	33.347 ± 1.829	0.056 ± 0.015	32.951 ± 3.799	0.052 ± 0.015

⚙️ Pre-requisties

Linux
Python>=3.7
NVIDIA GPU (memory>=32G) + CUDA cuDNN

🚀 Pre-trained models

VQGAN - SZCH-X-Rays UNet - SZCH-X-Rays VQGAN - JSRT UNet - JSRT

📦 Download the datasets

The original JSRT dataset and processed JSRT dataset are located at https://drive.google.com/file/d/1RkiU85FFfouWuKQbpD7Pc7o3aZ7KrpYf/view?usp=sharing and https://drive.google.com/file/d/1o-T5l2RKdT5J75eBsqajqAuHPfZnzPhj/view?usp=sharing, respectively.

Three paired images with CXRs and DES soft-tissues images of SZCH-X-Rays for testing are located at

└─data
    ├─ CXR
    │   ├─ 0.png
    │   ├─ 1.png
    │   └─ 2.png
    └─ BS
        ├─ 0.png
        ├─ 1.png
        └─ 2.png

To implement lung segmentation in data preparation, please use lungSegmentation.ipynb.

🫳 Install dependencies

pip install -r requirements.txt

🚅 Evaluation

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}
}