BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models
Journal:
arXiv
Published Date:
Dec 20, 2024
Abstract
Lung diseases represent a significant global health challenge, with Chest
X-Ray (CXR) being a key diagnostic tool due to their 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 developed 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
introduce offset noise and a temporal adaptive thresholding strategy. These
additions help minimize discrepancies in generating low-frequency information,
thereby improving the clarity of the generated 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
dual-energy subtraction soft tissue images collected from a 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
experimental and clinical evaluations reveal that BS-LDM excels in bone
suppression, underscoring its significant clinical value.
