Training medical image segmentation models usually requires a large amount of labeled data. By contrast, humans can quickly learn to accurately recognise anatomy of interest from medical (e.g. MRI and CT) images with some limited guidance. Such recognition ability can easily generalise to new images from different clinical centres. This rapid and generalisable learning ability is mostly due to the compositional structure of image patterns in the human brain, which is less incorporated in medical image segmentation. In this paper, we model the compositional components (i.e. patterns) of human anatomy as learnable von-Mises-Fisher (vMF) kernels, which are robust to images collected from different domains (e.g. clinical centres). The image features can be decomposed to (or composed by) the components with the composing operations, i.e. the vMF likelihoods. The vMF likelihoods tell how likely each anatomical part is at each position of the image. Hence, the segmentation mask can be predicted based on the vMF likelihoods. Moreover, with a reconstruction module, unlabeled data can also be used to learn the vMF kernels and likelihoods by recombining them to reconstruct the input image. Extensive experiments show that the proposed vMFNet achieves improved generalisation performance on two benchmarks, especially when annotations are limited. Code is publicly available at: https://github.com/vios-s/vMFNet.

翻译：培训医学图像分解模型通常需要大量贴标签数据。相比之下,人类可以快速学习准确识别医学(如MRI和CT)图像中感兴趣的解剖学,但指导有限。这种识别能力可以很容易地向不同临床中心的新图像推广。这种快速和一般的学习能力主要归因于人类大脑图像模式的构成结构,而这种结构较少纳入医学图像分解。在本文中,我们将人类解剖解解解剖的成分(即模式)作为可学习的 von-Mises-Fisher(vMF)内核(vMF),这些成分对从不同领域(如临床中心)收集的图像具有很强的特性。这种图像特征可以很容易地(或由)向不同临床中心收集的新图像。这种快速和一般的学习能力,即 vMFI 可能性。 vMF 可能性表明每个解解剖部分可能出现在每个图像的位置。因此,分解面面面罩可以根据 vMF的可能性进行预测。此外,在两个重建模块中,未加贴的MFMF 数据也可以用来在一般的图像中学习, 。在两个数据库中, 的变现数据也可以用于学习。