Few-shot medical image segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally scarce. However, current mainstream methods primarily focus on extracting holistic representations from support images with large intra-class variations in appearance and background, and encounter difficulties in adapting to query images. In this work, we present an approach to extract multiple representative subregions from a given support medical image, enabling fine-grained selection over the generated image regions. Specifically, the foreground of the support image is decomposed into distinct regions, which are subsequently used to derive region-level representations via a designed regional prototypical learning (RPL) module. We then introduce a novel prototypical representation debiasing (PRD) module based on a two-way elimination mechanism that suppresses the disturbance of regional representations by a self-support, Multidirection Self-debiasing (MS) block, and a support-query, interactive debiasing (ID) block. Finally, an assembled prediction (AP) module is devised to balance and integrate predictions of multiple prototypical representations learned using stacked PRD modules. Results obtained through extensive experiments on three publicly accessible medical imaging datasets demonstrate consistent improvements over the leading FSMIS methods. The source code is available at https://github.com/YazhouZhu19/PAMI .

中文翻译：

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医学图像分区：提取多个代表性子区域以进行少样本医学图像分割

对于高质量注释自然稀缺的医学图像分割任务，少镜头医学图像分割（FSMIS）是一种更有前景的解决方案。然而，当前的主流方法主要侧重于从外观和背景类内变化较大的支持图像中提取整体表示，并且在适应查询图像方面遇到困难。在这项工作中，我们提出了一种从给定的支持医学图像中提取多个代表性子区域的方法，从而能够对生成的图像区域进行细粒度选择。具体来说，支持图像的前景被分解为不同的区域，随后通过设计的区域原型学习（RPL）模块将其用于导出区域级表示。然后，我们介绍了一种基于双向消除机制的新型原型表示去偏（PRD）模块，该模块通过自支持、多方向自去偏（MS）块和支持查询、交互式去偏来抑制区域表示的干扰(ID) 块。最后，设计了一个组装预测（AP）模块来平衡和集成使用堆叠 PRD 模块学习的多个原型表示的预测。通过对三个可公开访问的医学成像数据集进行广泛实验获得的结果表明，与领先的 FSMIS 方法相比，该方法取得了一致的改进。源代码位于https://github.com/YazhouZhu19/PAMI 。