Learning OCT segmentation from a single label

Haoran Zhang; Jianlong Yang; Xubo Tang; Botao Guo; Jingqian Zhang; Xinyi Wang; Aili Zhang

doi:10.1117/12.2648934

8 March 2023 Learning OCT segmentation from a single label

Haoran Zhang, Jianlong Yang, Xubo Tang, Botao Guo, Jingqian Zhang, Xinyi Wang, Aili Zhang

Proceedings Volume 12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII; 123670Y (2023) https://doi.org/10.1117/12.2648934
Event: SPIE BiOS, 2023, San Francisco, California, United States

Conference Poster

Abstract

Deep learning boosts the performance of automatic OCT segmentation, which is a prerequisite for standardized diagnostic and therapeutic procedures. However, training deep neural network requires laborious data labeling, and the trained models only work well on data from the same manufacturer, imaging protocol, and region of interest. Here we propose a novel learning method to reduce labeling costs. By labeling and training on a single image, we achieved segmentation accuracy comparable to that of a U-Net model trained on ~25 to 50 labeled images. This reduction in labeling costs could significantly improve the flexibility and generalization of deep-learning-based OCT segmentation.

Citation Download Citation

Haoran Zhang, Jianlong Yang, Xubo Tang, Botao Guo, Jingqian Zhang, Xinyi Wang, and Aili Zhang "Learning OCT segmentation from a single label", Proc. SPIE 12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII, 123670Y (8 March 2023); https://doi.org/10.1117/12.2648934

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