Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929697138409472 |
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| author | Wang, Haolin Ou, Yafei Ambalathankandy, Prasoon Ota, Gen Dai, Pengyu Ikebe, Masayuki Suzuki, Kenji Kamishima, Tamotsu |
| author_facet | Wang, Haolin Ou, Yafei Ambalathankandy, Prasoon Ota, Gen Dai, Pengyu Ikebe, Masayuki Suzuki, Kenji Kamishima, Tamotsu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01972 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography Wang, Haolin Ou, Yafei Ambalathankandy, Prasoon Ota, Gen Dai, Pengyu Ikebe, Masayuki Suzuki, Kenji Kamishima, Tamotsu Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning I.3.3; J.3; I.4.0 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. |
| title | Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning I.3.3; J.3; I.4.0 |
| url | https://arxiv.org/abs/2502.01972 |