Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography

2025-02-04

AI Summary

To address the challenges of labor-intensive joint space width (JSW) annotation, data scarcity, and class imbalance in radiographic assessment of rheumatoid arthritis (RA), this paper proposes a layer-separation synthesis paradigm. We first decouple finger joint X-ray images into three anatomically distinct layers—soft tissue, upper bone, and lower bone—and introduce the Layer Separation Network (LSN), a multi-branch architecture trained with hierarchical pixel-wise supervision for accurate layer decomposition. This paradigm enables continuous, controllable JSW synthesis and automatic ground-truth generation, overcoming the limitation of single-label-per-image. Synthesized images exhibit high visual fidelity; downstream JSW measurement error decreases by 37.2%, while classification and segmentation mAP improve by over 8.5%. The approach effectively alleviates small-sample and annotation bottlenecks, establishing a novel, interpretable, and controllable synthesis framework for quantitative RA analysis.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Manipulation
• Machine Learning: Learning with Manifolds

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by joint inflammation and progressive structural damage. Joint space width (JSW) is a critical indicator in conventional radiography for evaluating disease progression, which has become a prominent research topic in computer-aided diagnostic (CAD) systems. However, deep learning-based radiological CAD systems for JSW analysis face significant challenges in data quality, including data imbalance, limited variety, and annotation difficulties. This work introduced a challenging image synthesis scenario and proposed Layer Separation Networks (LSN) to accurately separate the soft tissue layer, the upper bone layer, and the lower bone layer in conventional radiographs of finger joints. Using these layers, the adjustable JSW images can be synthesized to address data quality challenges and achieve ground truth (GT) generation. Experimental results demonstrated that LSN-based synthetic images closely resemble real radiographs, and significantly enhanced the performance in downstream tasks. The code and dataset will be available.

Problem

• Synthesize adjustable Joint Space Width images
• Address data quality challenges in radiological CAD
• Accurately separate layers in conventional radiographs

Innovation

• Layer Separation Networks
• Adjustable JSW synthesis
• Enhanced CAD system performance