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.

为在高质量注释匮乏的医学图像分割任务中提供更有前景的解决方案，本文提出了一种从给定支持医学图像中提取多个代表性子区域的方法，实现对生成图像区域的精细选择。通过区域性典型学习(RPL)模块，将支持图像的前景分解为不同的区域，并基于一种双向消除机制的新颖典型性代表去偏差(PRDP)模块抑制了区域性表示的干扰，最后，设计了一个组装预测(AP)模块来平衡和整合通过堆叠PRDP模块学习到的多个典型性表示的预测。在三个公开可访问的医学图像数据集上进行的大量实验证明了对主导的FSMIS方法的一致改进效果。源代码可在此https URL获取。

分割医学影像：提取多个代表性子区域用于少样本医学影像分割