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

• URL: http://arxiv.org/abs/2112.11177v1
• Date: Tue, 21 Dec 2021 13:18:46 GMT
• Title: Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization
• Authors: Ziqi Zhou, Lei Qi, Xin Yang, Dong Ni, Yinghuan Shi
• Abstract summary: We propose a novel dual-normalization module by leveraging the augmented source-similar and source-dissimilar images. Our method outperforms other state-of-the-art domain generalization methods.
• Score: 29.470385509955687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 module 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. Afterwards, 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 dataset, have demonstrated that our method outperforms other state-of-the-art domain generalization methods.

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