Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 32 references that are in the map. First posted on arXiv 4 Feb 2025.
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12 of 32 references.
References · 12
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2022Generative Adversarial Networkscited by 5,603
- 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
- 2016Tutorial on Variational Autoencoderscited by 404
- 2021Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentationcited by 88
- 2024Towards Generalizable Tumor Synthesiscited by 65
- 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generationcited by 54
- 2020Deflating Dataset Bias Using Synthetic Data Augmentationcited by 37
- 2021A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translationcited by 19
- 2022A Sub-pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritiscited by 4
- 2023A Deep Registration Method for Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritiscited by 3
- 2024BLS-GAN: A Deep Layer Separation Framework for Eliminating Bone Overlap in Conventional Radiographscited by 3
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