We present the Atlas of Classifiers (AoC)—a conceptually novel framework for brain MRI segmentation. The AoC is a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights, a few for each voxel, represent the training dataset. It can, therefore, be considered as a light-weight learning machine, which despite its low capacity does not underfit the problem. The AoC construction is independent of the actual intensities of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities. In this sense, it does not overfit the training data, as well. The proposed method has been applied to numerous publicly available datasets for the segmentation of brain MRI tissues and is shown to be robust to noise and outreach commonly used methods. Promising results were also obtained for multi-modal, cross-modality MRI segmentation. Finally, we show how AoC trained on brain MRIs of healthy subjects can be exploited for lesion segmentation of multiple sclerosis patients.

我们展示了分类器图集(AoC)——一种概念上新颖的脑 MRI 分割框架。AoC 是从标记数据中学习到的体素多项式逻辑回归 (LR) 函数的空间图。收敛后，得到的固定 LR 权重（每个体素有几个）代表训练数据集。因此，它可以被认为是一种轻量级的学习机，尽管它的容量很低，但并不能满足这个问题。AoC 构造独立于测试图像的实际强度，提供了在可用标记数据上对其进行训练并将其用于从不同数据集和模态分割图像的灵活性。从这个意义上说，它也不会过度拟合训练数据。所提出的方法已应用于许多公开可用的数据集，用于脑 MRI 组织的分割，并且显示出对噪声和外展常用方法的鲁棒性。多模态、跨模态 MRI 分割也获得了有希望的结果。最后，我们展示了如何利用在健康受试者的脑部 MRI 上训练的 AoC 用于多发性硬化症患者的病灶分割。