提出分层分离网络，可生成可调关节间隙宽度的X光片。
Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography
- 通过分层分离技术解析软组织与上下骨层结构
- 合成图像与真实影像相似度高，显著提升下游任务性能
- 适合需要高质量标注数据的医学影像研究者
类风湿性关节炎（RA）是一种慢性自身免疫性疾病，以关节炎症和进行性结构损伤为特征。关节间隙宽度（JSW）是常规放射学评估疾病进展的关键指标，已成为计算机辅助诊断（CAD）系统的重要研究方向。然而，基于深度学习的放射学CAD系统在JSW分析中面临数据质量挑战，包括数据不平衡、多样性不足及标注困难。本文提出一个具有挑战性的图像合成场景，并引入分层分离网络（LSN），精确分离手指关节常规X光片中的软组织层、上骨层和下骨层。利用这些分层信息，可生成可调节关节间隙宽度的合成图像，以应对数据质量问题并实现真实标签（GT）生成。实验结果表明，基于LSN的合成图像与真实影像高度相似，且显著提升了下游任务性能。代码与数据集将公开。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。