Self-similarity guided regression with contrast enhancement for spine segmentation
Year: 2025
Type: article
Abstract: Accurate spine segmentation is critical for scoliosis diagnosis and treatment. For instance, automatic Cobb angle measurement for scoliosis relies on precisely localized vertebral masks. However, it remains a challenging task due to low tissue contrast, blurred vertebral edges, and overlapping anatomical structures. In this paper, we propose SRNet, a pure segmentation network that produces binary masks of each vertebra. SRNet integrates two novel components, a Self-similarity Guided Dynamic Convolution (SGDC) module and a Contrast-Enhanced Boundary Decoder (CEBD). SGDC exploits the repetitive structure of vertebrae by leveraging non-local attention to compute self-similarity across feature maps and dynamic convolution to combine multiple convolution kernels adaptively. CEBD sharpens segmentation boundaries via a reverse-attention mechanism that erases the coarse prediction and focuses on missing edge details, combined with a spectral-residual filter that amplifies high-frequency edge information. Extensive experiments on the AASCE spine X-ray dataset show that our SRNet achieves a high Dice score of 92.37%, outperforming state-of-the-art approaches. While our primary focus here is mask segmentation, the accurate vertebral masks produced by SRNet could readily support future tasks such as scoliosis Cobb angle estimation.
Source: Visual Informatics
Authors: Xiaojia Zhu, Chunyu Li, Rui Chen, Zhiwen Shao
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Awards: G1218582835, G3496845951, G7070098255
OpenAlex ID: W4413428325