BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models

TL;DR

BS-LDM introduces a novel conditional latent diffusion model framework for effective bone suppression in high-resolution chest X-ray images, improving lesion visibility and diagnostic accuracy.

Contribution

The paper presents a new end-to-end bone suppression framework using conditional latent diffusion models and a hybrid loss-constrained GAN, along with a high-quality dataset for training and evaluation.

Findings

BS-LDM outperforms existing methods in bone suppression quality.

The framework preserves soft tissue details effectively.

Experimental results demonstrate clinical applicability.

Abstract

Lung diseases represent a significant global health challenge, with Chest X-Ray (CXR) being a key diagnostic tool due to its accessibility and affordability. Nonetheless, the detection of pulmonary lesions is often hindered by overlapping bone structures in CXR images, leading to potential misdiagnoses. To address this issue, we develop an end-to-end framework called BS-LDM, designed to effectively suppress bone in high-resolution CXR images. This framework is based on conditional latent diffusion models and incorporates a multi-level hybrid loss-constrained vector-quantized generative adversarial network which is crafted for perceptual compression, ensuring the preservation of details. To further enhance the framework's performance, we utilize offset noise in the forward process, and a temporal adaptive thresholding strategy in the reverse process. These additions help minimize discrepancies in generating low-frequency information of soft tissue images. Additionally, we have compiled a high-quality bone suppression dataset named SZCH-X-Rays. This dataset includes 818 pairs of high-resolution CXR and soft tissue images collected from our partner hospital. Moreover, we processed 241 data pairs from the JSRT dataset into negative images, which are more commonly used in clinical practice. Our comprehensive experiments and downstream evaluations reveal that BS-LDM excels in bone suppression, underscoring its clinical value. Our code is available at https://github.com/diaoquesang/BS-LDM.