For medical image segmentation, imagine if a model was only trained using MR
images in source domain, how about its performance to directly segment CT
images in target domain? This setting, namely generalizable cross-modality
segmentation, owning its clinical potential, is much more challenging than
other related settings, e.g., domain adaptation. To achieve this goal, we in
this paper propose a novel dual-normalization model by leveraging the augmented
source-similar and source-dissimilar images during our generalizable
segmentation. To be specific, given a single source domain, aiming to simulate
the possible appearance change in unseen target domains, we first utilize a
nonlinear transformation to augment source-similar and source-dissimilar
images. Then, to sufficiently exploit these two types of augmentations, our
proposed dual-normalization based model employs a shared backbone yet
independent batch normalization layer for separate normalization. Afterward, we
put forward a style-based selection scheme to automatically choose the
appropriate path in the test stage. Extensive experiments on three publicly
available datasets, i.e., BraTS, Cross-Modality Cardiac, and Abdominal
Multi-Organ datasets, have demonstrated that our method outperforms other
state-of-the-art domain generalization methods. Code is available at
this https URL

本文提出了一种基于双标准化模型的，借助源域中类似和不类似的图像数据在目标域中进行通用分割的方法，旨在解决跨模态分割中不同领域之间的特征差异问题，并在 BraTS、Cross-Modality Cardiac 和 Abdominal Multi-Organ 等公共领域数据集上进行实验和测试，表明此方法优于其他最先进的领域泛化方法。