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

TL;DR

This paper presents Layer Separation Networks (LSN) for synthesizing adjustable joint space width images in radiographs, improving data quality and aiding disease progression analysis in rheumatoid arthritis.

Contribution

Introduction of LSN for accurate layer separation in radiographs, enabling realistic JSW image synthesis to address data challenges in CAD systems.

Findings

LSN-based images closely resemble real radiographs

Synthetic images improve downstream diagnostic tasks

Addresses data imbalance and annotation issues

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.