Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization

作者：Ziqi Zhou, Lei Qi, Xin Yang, Dong Ni, Yinghuan Shi · 发表于：2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 年份：2022 · DOI：10.1109/cvpr52688.2022.02019 · 被引用次数：101 · 研究领域：Domain Adaptation and Few-Shot Learning、Advanced Neural Network Applications、Radiomics and Machine Learning in Medical Imaging

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 dualnormalization 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 https://github.com/zzzqzhou/Dual-Normalization.