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:在医学图像分析中,值得略微学习的精华在于有效利用辅助图像数据,这些数据被贴上新课程的分类或分层,否则需要大量培训图像和专家说明。这项工作描述了完全3D原型微粒分解算法,使经过培训的网络能够有效地适应培训中缺少的临床有趣结构,只使用来自不同研究所的少数贴标签图像。首先,为了弥补不同机构在对新课程进行偶发性调整的机构之间广泛承认的空间变异性,一个新的空间登记机制被纳入了原型学习,包括一个分层头和一个空间调整模块。第二,为了协助培训与观察到的不完善的匹配,建议支持面罩调控模块进一步利用从支持图像中获得的批注。在应用对干预规划至关重要的8个解剖结构时进行了广泛的实验,使用了在7个机构获得的589个骨盆T2-加权MM图像数据集。结果显示,3D的每个配制、空间登记以及支持面模调制模块的有效性,包括一个空间登记,以及支持面调制模模模制模块。第二段的拟议数据是单独或相互改进的替代数据,这些部分是否具有了实际性,无论是从统计效果,还是由不同的部分独立地进行了对比。