Medical image segmentation is one of the domains where sufficient annotated data is not available. This necessitates the application of low-data frameworks like few-shot learning. Contemporary prototype-based frameworks often do not account for the variation in features within the support and query images, giving rise to a large variance in prototype alignment. In this work, we adopt a prototype-based self-supervised one-way one-shot learning framework using pseudo-labels generated from superpixels to learn the semantic segmentation task itself. We use a correlation-based probability score to generate a dynamic prototype for each query pixel from the bag of prototypes obtained from the support feature map. This weighting scheme helps to give a higher weightage to contextually related prototypes. We also propose a quadrant masking strategy in the downstream segmentation task by utilizing prior domain information to discard unwanted false positives. We present extensive experimentations and evaluations on abdominal CT and MR datasets to show that the proposed simple but potent framework performs at par with the state-of-the-art methods.

翻译：医学图像分割是缺乏充足标注数据的领域之一，这需要采用低数据框架如少样本学习。当前基于原型的框架通常未考虑支持图像与查询图像内部特征的变化，导致原型对齐存在较大方差。本研究采用基于原型的自监督单向单次学习框架，利用超像素生成的伪标签来学习语义分割任务本身。我们通过基于相关的概率分数，从支持特征图获取的原型集合中为每个查询像素生成动态原型。该加权机制有助于为上下文相关的原型赋予更高权重。此外，在下游分割任务中，我们提出利用先验领域信息的象限掩码策略以消除无关的假阳性结果。通过在腹部CT和MR数据集上的大量实验与评估，我们证明所提出的简洁而有效的框架性能与最先进方法相当。