STB-Net: a Siamese architecture-based reconstruction-segmentation network for ocular surface image segmentation.

ABSTRACT:

Background

Eyelid morphological parameters are crucial for quantitatively assessing eyelid morphology and diagnosing related disorders. However, achieving precise and automated measurement of these parameters remains a challenge. This study aims to develop an automated segmentation model for ocular surface images to accurately segment key anatomical structures and compute critical eyelid metrics.

Methods

We propose STB-Net, a novel segmentation model tailored for ocular surface imagery. The baseline model, TB-Net, enhances the TransUNet architecture by integrating a Bottom-up Local Attention Modulation (BLAM) module into its decoder, effectively encoding fine-grained features from shallow layers into high-level semantics. The final STB-Net framework integrates TB-Net with an

SUBMITTER: Wan C 

PROVIDER: S-EPMC12682546 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature