层分离：常规放射摄影中可调节关节间隙宽度图像的合成
Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography
AI总结：
本文提出层分离网络（LSN）分离手指关节X光片中的软组织层和上下骨层，以合成可调节关节间隙宽度图像，从而缓解类风湿关节炎JSW分析中的数据不平衡、多样性有限和标注困难问题，并显著提升下游任务性能。
摘要：
类风湿关节炎（RA）是一种以关节炎症和进行性结构损伤为特征的慢性自身免疫性疾病。关节间隙宽度（JSW）是常规放射摄影中评估疾病进展的关键指标，已成为计算机辅助诊断（CAD）系统中的重要研究课题。然而，基于深度学习的用于JSW分析的放射学CAD系统在数据质量方面面临重大挑战，包括数据不平衡、多样性有限和标注困难。本工作引入了一个具有挑战性的图像合成场景，并提出层分离网络（Layer Separation Networks，LSN），以准确分离手指关节常规放射照片中的软组织层、上骨层和下骨层。利用这些层，可以合成可调节的JSW图像，以应对数据质量挑战并实现真实标签（GT）生成。实验结果表明，基于LSN的合成图像与真实放射照片高度相似，并显著提升了下游任务的性能。代码和数据集将公开提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
