BLS-GAN: A Deep Layer Separation Framework for Eliminating Bone Overlap in Conventional Radiographs

Haolin Wang, Yafei Ou, Prasoon Ambalathankandy, Gen Ota, Pengyu Dai, Masayuki Ikebe, Kenji Suzuki, Tamotsu Kamishima
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Abstract

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 will be made available.
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BLS-GAN:消除传统射线照片中骨重叠的深层分离框架
传统放射摄影技术因其简便易行、用途广泛和成本效益高而被广泛用于诊断、监测和预后肌肉骨骼(MSK)疾病。在传统射线照片中,骨重叠现象非常普遍,会妨碍放射科医生或算法对骨特征的准确评估,给传统诊断和计算机辅助诊断带来了巨大挑战。这项工作开始研究一种具有挑战性的情况--骨层分离的非传统射线照片,在这种情况下,分离重叠的骨区域可以独立评估每个骨层的骨特征,并为 MSK 疾病诊断及其自动化奠定基础。这项研究提出了一种骨层分离 GAN(Bone Layer Separation GAN,BLS-GAN)框架,它能生成具有合理骨骼特征和纹理的高质量骨层图像。该框架引入了基于传统放射成像原理的重建器,实现了高效重建,并缓解了重叠区域软组织引起的重复计算和训练不稳定问题。此外,还利用合成图像进行了预训练,以提高训练过程和结果的稳定性。生成的图像通过了视觉图灵测试,并提高了下游任务的性能。这项工作证实了从传统射线照片中提取骨层图像的可行性,有望利用骨层分离技术促进MSK诊断、监测和预后方面更全面的分析研究。
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