Automatic segmentation of layered tissue is the key to optical coherence tomography (OCT) image analysis for esophagus. While deep learning technology offers promising solutions to this problem, the requirement for large numbers of annotated samples often poses a significant obstacle, as it is both expensive and challenging to obtain. With this in mind, we introduced a self-supervised segmentation framework for esophageal OCT images. In particular, the proposed method employs a masked autoencoder (MAE) for self-supervised training and constructs the segmentation network by integrating a pretrained vision transformer (ViT) encoder with an attentive transformer decoder. In this case, the segmentation network has the potential to accomplish the few-shot, or the more aggressive one-shot segmentation, and achieve high-quality segmentation performance. Experimental results on both a self-collected mouse esophageal dataset and a public human esophageal OCT dataset confirm the advantages and practical significance of the proposed method.

中文翻译：

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基于自监督视觉变换器的食管OCT图像少样本分割

分层组织的自动分割是食管光学相干断层扫描（OCT）图像分析的关键。虽然深度学习技术为这个问题提供了有希望的解决方案，但对大量带注释样本的需求往往会造成重大障碍，因为获取它既昂贵又具有挑战性。考虑到这一点，我们引入了食管 OCT 图像的自监督分割框架。特别是，所提出的方法采用掩码自动编码器（MAE）进行自监督训练，并通过将预训练视觉变换器（ViT）编码器与注意力变换器解码器集成来构建分割网络。在这种情况下，分割网络有潜力完成few-shot，或更激进的one-shot分割，并实现高质量的分割性能。在自采集的小鼠食管数据集和公共人类食管OCT数据集上的实验结果证实了该方法的优势和实际意义。