The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations. This work describes a fully 3D prototypical few-shot segmentation algorithm, such that the trained networks can be effectively adapted to clinically interesting structures that are absent in training, using only a few labelled images from a different institute. First, to compensate for the widely recognised spatial variability between institutions in episodic adaptation of novel classes, a novel spatial registration mechanism is integrated into prototypical learning, consisting of a segmentation head and an spatial alignment module. Second, to assist the training with observed imperfect alignment, support mask conditioning module is proposed to further utilise the annotation available from the support images. Extensive experiments are presented in an application of segmenting eight anatomical structures important for interventional planning, using a data set of 589 pelvic T2-weighted MR images, acquired at seven institutes. The results demonstrate the efficacy in each of the 3D formulation, the spatial registration, and the support mask conditioning, all of which made positive contributions independently or collectively. Compared with the previously proposed 2D alternatives, the few-shot segmentation performance was improved with statistical significance, regardless whether the support data come from the same or different institutes.

翻译：少样本学习在医学图像分析中的优势在于能够有效利用带标注的支持图像数据，从而对新的类别进行分类或分割，而传统方法需要大量训练图像和专家标注。本文描述了一种全三维原型少样本分割算法，使得训练好的网络能够仅利用来自不同机构的少量标注图像，有效适应训练中未出现的临床感兴趣结构。首先，为补偿新类别情景适配中广泛存在的机构间空间变异性，我们在原型学习中集成了一种新颖的空间配准机制，该机制由分割头与空间对齐模块组成。其次，针对观测到的不完美对齐问题，我们提出支持掩码条件模块以进一步挖掘支持图像中的标注信息。在基于七家机构589例盆腔T2加权MR图像数据集的八种介入规划关键解剖结构分割应用中，开展了大量实验。结果表明，三维模型构建、空间配准及支持掩码条件模块各自或协同均具有积极贡献。与先前提出的二维替代方法相比，无论支持数据来自同一机构还是不同机构，少样本分割性能均具有统计显著性提升。