# Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography

arXiv: https://arxiv.org/abs/2502.01972 · HTML: https://mlsanity.com/paper/2502.01972/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation](https://mlsanity.com/paper/2102.04525/) (2021)
- [Towards Generalizable Tumor Synthesis](https://mlsanity.com/paper/2402.19470/) (2024)
- [Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation](https://mlsanity.com/paper/2211.03364/) (2022)
- [Deflating Dataset Bias Using Synthetic Data Augmentation](https://mlsanity.com/paper/2004.13866/) (2020)
- [A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation](https://mlsanity.com/paper/2101.05434/) (2021)
- [A Sub-pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis](https://mlsanity.com/paper/2205.09315/) (2022)
- [A Deep Registration Method for Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis](https://mlsanity.com/paper/2304.13938/) (2023)
- [BLS-GAN: A Deep Layer Separation Framework for Eliminating Bone Overlap in Conventional Radiographs](https://mlsanity.com/paper/2409.07304/) (2024)

## Built on by (most cited first, 1 of 1)

- [RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis](https://mlsanity.com/paper/2507.05193/) (2025)

## 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.