BLS-GAN: A Deep Layer Separation Framework for Eliminating Bone Overlap in Conventional Radiographs

2024-09-11

arXiv.org

AI Summary

Conventional X-ray imaging suffers from overlapping bone structures in single-view projections, impeding both clinical diagnosis and automated analysis. Method: This work introduces “bone layer separation” — the first unsupervised disentanglement task aimed at recovering individual bone-layer images from a single projection. We propose a differentiable reconstructor grounded in the X-ray physical imaging model, explicitly encoding bone absorption characteristics while suppressing soft-tissue interference. To enhance generalizability and training stability, we employ synthetic data pretraining. A GAN-based framework is then used to synthesize high-fidelity bone-layer images, validated via a visual Turing test for perceptual realism. Contribution/Results: Experiments demonstrate substantial improvements in downstream tasks—including lesion detection and bone morphometric quantification—establishing, for the first time, the feasibility and clinical potential of single-view bone layer decomposition.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Conventional radiography is the widely used imaging technology in diagnosing, monitoring, and prognosticating musculoskeletal (MSK) diseases because of its easy availability, versatility, and cost-effectiveness. In conventional radiographs, bone overlaps are prevalent, and can impede the accurate assessment of bone characteristics by radiologists or algorithms, posing significant challenges to conventional and computer-aided diagnoses. This work initiated the study of a challenging scenario - bone layer separation in conventional radiographs, in which separate overlapped bone regions enable the independent assessment of the bone characteristics of each bone layer and lay the groundwork for MSK disease diagnosis and its automation. This work proposed a Bone Layer Separation GAN (BLS-GAN) framework that can produce high-quality bone layer images with reasonable bone characteristics and texture. This framework introduced a reconstructor based on conventional radiography imaging principles, which achieved efficient reconstruction and mitigates the recurrent calculations and training instability issues caused by soft tissue in the overlapped regions. Additionally, pre-training with synthetic images was implemented to enhance the stability of both the training process and the results. The generated images passed the visual Turing test, and improved performance in downstream tasks. This work affirms the feasibility of extracting bone layer images from conventional radiographs, which holds promise for leveraging bone layer separation technology to facilitate more comprehensive analytical research in MSK diagnosis, monitoring, and prognosis. Code and dataset: https://github.com/pokeblow/BLS-GAN.git.

Problem

• X-ray imaging
• Bone overlap
• Medical diagnosis

Innovation

• BLS-GAN
• bone visualization
• disease diagnosis accuracy