CNN-Transformer-based dual-encoder segmentation network model for glaucoma auxiliary diagnosis(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2026年第2期
- Page:
- 268-275
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- CNN-Transformer-based dual-encoder segmentation network model for glaucoma auxiliary diagnosis
- Author(s):
- MA Yuzhang; ZHANG Wei; SHAO Haochen
- School of Medical Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730000, China
- Keywords:
- Keywords: optic cup and disc segmentation auxiliary diagnosis of glaucoma Transformer feature fusion attention mechanism
- PACS:
- R318；TP391
- DOI:
- DOI:10.3969/j.issn.1005-202X.2026.02.018
- Abstract:
- Accurate segmentation of the optic cup and optic disc is a critical step for calculating morphological parameters in the early screening of glaucoma. To address the issues of boundary blurring and limited segmentation accuracy caused by the inefficiency of local-global feature fusion and inadequate modeling of long-distance dependency modeling in existing methods, this study proposes a dual-encoder segmentation network model based on CNN-Transformer for auxiliary diagnosis of glaucoma. Specifically, a dual-branch complementary feature fusion module is designed to replace the traditional skip connection, and a dynamic weight allocation strategy is adopted to achieve the synergistic optimization of the local details of the CNN and the global context of the Transformer, thereby improving the efficiency of feature fusion. Furthermore, a global attention enhancement module is introduced into the Transformer encoder, which models the pixel-level long-distance dependencies using the multi-head self-attention mechanism, and enhances the context awareness of the boundary regions by integrating the depth-separable convolution, thus effectively alleviating the discontinuity of optic cup/disc edge. Experiments on the REFUGE dataset show that compared with U-Net, the proposed method achieves a 4.11% improvement in Dice coefficient and 5.62% improvement in IoU for the optic disc segmentation task, while for the optic cup segmentation task, the corresponding improvements in the Dice coefficient and IoU reach 11.75% and 19.30%, respectively.
Last Update: 2026-01-27