主流的基于全监督的深度学习分割模型在丰富的标记数据上训练时可以取得良好的效果，但医疗图像领域的图像分割存在标注成本高、分割目标种类多的问题，且往往缺少足够的标注数据。提出一个模型，通过融合自监督从数据中提取标签，利用超像素表征图像特性，进行小样本标注条件下的图像分割。引入多注意力机制使得模型更多关注图像的空间特征，位置注意模块和通道注意模块致力于单一图像内部的多尺度特征融合，而外部注意力模块显著突出了不同样本间的联系。在CHAOS健康腹部器官数据集上进行实验，1-shot极端情况下DSC达0.76，相较baseline分割结果提升3%左右。通过调整N-way-K-shot任务数来探讨小样本学习的意义，在7-shot设置下DSC有显著提升，与基于全监督的深度学习分割效果的差距在可接受范围内。


Mainstream fully supervised deep learning segmentation models can achieve good results when trained on abundant labeled data, but the image segmentation in the medical field faces the challenges of high annotation cost and diverse segmentation targets, often lacking sufficient labeled data. The model proposed in this paper incorporates the idea of extracting labels from data through self-supervision, utilizing superpixels to represent image characteristics for image segmentation under conditions of small sample annotation. The introduction of multiple attention mechanisms allows the model to focus more on spatial features of the image. The position attention module and channel attention module aim to fuse multi-scale features within a single image, while the external attention module highlights the connections between different samples. Experiments were conducted on the CHAOS healthy abdominal organ dataset. In the extreme case of the 1-shot, DSC reached 0.76, which is about 3%higher than the baseline result. In addition, this paper explores the significance of few-shot learning by adjusting the number of N-way-K-shot tasks. Under the 7-shot setting, DSC achieves significant improvement, which is within an acceptable range of the segmentation effect based on full supervision based on deep learning.


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