用深度生成模型分离X光片中的重叠骨骼，提升诊断准确性。
BLS-GAN: A Deep Layer Separation Framework for Eliminating Bone Overlap in Conventional Radiographs
- 基于成像原理设计重建模块，解决软组织干扰导致的训练不稳。
- 生成图像通过视觉图灵测试，下游任务性能显著提升。
- 适合医学影像分析、骨科疾病自动化诊断研究者使用。
常规放射摄影因易获取、多功能和低成本，被广泛用于肌肉骨骼（MSK）疾病的诊断、监测和预后。然而，骨骼重叠在常规X光片中普遍存在，会阻碍放射科医生或算法对骨骼特征的准确评估，给传统和计算机辅助诊断带来重大挑战。本文首次研究了常规X光片中骨骼层分离这一难题，通过分离重叠的骨骼区域，实现各骨骼层独立特征评估，为MSK疾病诊断及其自动化奠定基础。提出一种骨骼层分离生成对抗网络（BLS-GAN）框架，可生成高质量、具有合理骨骼特征与纹理的单层骨骼图像。该框架引入基于常规放射成像原理的重构器，实现高效重建，缓解因重叠区域软组织带来的重复计算与训练不稳定性问题。同时采用合成图像预训练以增强训练过程与结果的稳定性。生成图像通过视觉图灵测试，并在下游任务中表现更优。本工作验证了从常规X光片中提取骨骼层图像的可行性，为推动骨骼层分离技术在MSK诊断、监测和预后的综合分析研究中应用提供了可能。代码与数据集：https://github.com/pokeblow/BLS-GAN.git。
原文摘要 · Abstract (English)
Conventional radiography is the widely used imaging technology in diagnosing, monitoring, and prognosticating musculoskeletal (MSK) diseases because of its easy availability, versatility, and cost-effectiveness. In conventional radiographs, bone overlaps are prevalent, and can impede the accurate assessment of bone characteristics by radiologists or algorithms, posing significant challenges to conventional and computer-aided diagnoses. This work initiated the study of a challenging scenario - bone layer separation in conventional radiographs, in which separate overlapped bone regions enable the independent assessment of the bone characteristics of each bone layer and lay the groundwork for MSK disease diagnosis and its automation. This work proposed a Bone Layer Separation GAN (BLS-GAN) framework that can produce high-quality bone layer images with reasonable bone characteristics and texture. This framework introduced a reconstructor based on conventional radiography imaging principles, which achieved efficient reconstruction and mitigates the recurrent calculations and training instability issues caused by soft tissue in the overlapped regions. Additionally, pre-training with synthetic images was implemented to enhance the stability of both the training process and the results. The generated images passed the visual Turing test, and improved performance in downstream tasks. This work affirms the feasibility of extracting bone layer images from conventional radiographs, which holds promise for leveraging bone layer separation technology to facilitate more comprehensive analytical research in MSK diagnosis, monitoring, and prognosis. Code and dataset: https://github.com/pokeblow/BLS-GAN.git.
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。