Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography
Journal:
arXiv
Published Date:
Feb 4, 2025
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.
