Retinal layer segmentation in optical coherence tomography (OCT) images enables the quantification of retinal morphology, which is critical for diagnosing and monitoring ophthalmic diseases. However, interference factors such as speckle noise, intensity variations, and pathological abnormalities hinder efficient and topology-guided layer segmentation. As topology-guaranteed layers can be efficiently derived from structured layer boundaries, this study presents a structure-constrained regression network (SCRNet): a lightweight, end-to-end deep network that leverages structural priors inherent in OCT retinal layers to segment these boundaries efficiently. As the retinal structure is robust to interference factors and critical for establishing structured layer boundaries, SCRNet introduces a lightweight, two-stream architecture to capture structural information, with one stream targeting the layer topology and the other targeting boundary continuity. Each stream incorporates a tailored structural feature module (SFM) and a structure-constrained loss (SCL) to extract structural information effectively from a global perspective. Then, a structure-constrained regression module (SCRM) integrates the complementary structural information from both streams to enable structured boundary regression, enhancing accuracy and robustness. Extensive experiments on two publicly available benchmark datasets demonstrate that SCRNet achieves state-of-the-art performance in segmenting structured layer boundaries and topology-guaranteed layers while maintaining high efficiency. The source code will be publicly available at https://github.com/cyan323/SCRNet.
Liu et al. (Thu,) studied this question.