Semantic Segmentation of Optical Coherence Tomography Images Based on TUnet+ Modeling

Abstract

Accurate segmentation of Optical Coherence Tomography images is critical for assisting clinicians in identifying lesion regions and evaluating disease progression in retinal and cardiovascular disorders. As OCT is an optical electromagnetic-wave imaging modality, segmentation performance is closely related to the interpretation of wave scattering, tissue-layer boundaries, and propagation-induced image features. Existing OCT segmentation models are often limited by high computational complexity and insufficient accuracy, which restrict efficient and reliable clinical application. To address these challenges, this study proposes a lightweight TUnet+ network incorporating an innovatively designed PSE_C2fCIB module. Extensive experiments were conducted on the publicly available GOALS2022 dataset, including 200 deidentified OCT images augmented for training and validation. The results show that TUnet+ achieves state-of-the-art performance, with an accuracy of 99.3%, an F1-score of 95.65%, and a mean Intersection over Union of 91.87%. Through multi-scale feature fusion, channel attention, and efficient contextual modeling, the proposed framework improves the recognition of fine structural boundaries in OCT images. The method provides a robust technical foundation for automated diagnostic support and quantitative disease assessment, and it also demonstrates the value of electromagnetic-wave imaging analysis in biomedical engineering.