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 sub-regions 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 which suppresses the disturbance of regional representations by a self-support, Multi-direction 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.

翻译：少样本医学图像分割（FSMIS）是医学图像分割任务中更具前景的解决方案，其中高质量标注自然稀缺。然而，当前主流方法主要关注从支持图像中提取整体表征，而这些图像在形态和背景上存在较大的类内差异，导致难以适应查询图像。本工作中，我们提出一种方法从给定的支持医学图像中提取多个代表性子区域，从而实现对生成图像区域的细粒度选择。具体而言，支持图像的前景被分解为不同区域，随后通过设计的区域原型学习（RPL）模块导出区域级表征。接着，我们引入一种新颖的原型表征去偏（PRD）模块，该模块基于双向消除机制，通过自支持的多方向自去偏（MS）块和支持-查询的交互去偏（ID）块抑制区域表征的干扰。最后，设计了一种集成预测（AP）模块，用于平衡和整合通过堆叠PRD模块学习到的多个原型表征的预测结果。在三个公开医学影像数据集上进行的广泛实验结果表明，该方法相比主流FSMIS方法具有一致的改进。源代码见https://github.com/YazhouZhu19/PAMI。