Abstract
Existing tumor augmentation methods cannot deal with both domain and content information at the same time, causing a content distortion or domain gap (distortion problem) in the generated tumor. To address this challenge, we propose a Domain-aware and Content-consistent Cross-cycle Framework, named DCAug, for tumor augmentation to eliminate the distortion problem and improve the diversity and quality of synthetic tumors. Specifically, DCAug consists of one novel Cross-cycle Framework and two novel contrastive learning strategies: 1) Domain-aware Contrastive Learning (DaCL) and 2) Cross-domain Consistency Learning (CdCL), which disentangles the image information into two solely independent parts: 1) Domain-invariant content information; 2) Individual-specific domain information. During new sample generation, DCAug maintains the consistency of domain-invariant content information while adaptively adjusting individual-specific domain information through the advancement of DaCL and CdCL. We analyze and evaluate DCAug on two challenging tumor segmentation tasks. Experimental results (10.48% improvement in KiTS, 5.25% improvement in ATLAS) demonstrate that DCAug outperforms current state-of-the-art tumor augmentation methods and significantly improves the quality of the synthetic tumors.
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Acknowledgments
This work was supported by cross-innovation talent project in Renmin Hospital of Wuhan University (grant number JCRCZN-2022-016); Natural Science Foundation of Hubei Province (grant number 2022CFB252); Undergraduate education quality construction comprehensive reform project (grant number 2022ZG282) and the National Natural Science Foundation of China (grant number 81860276).
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Zhu, Q., Yin, L., Tang, Q., Wang, Y., Cheng, Y., Li, S. (2023). DCAug: Domain-Aware and Content-Consistent Cross-Cycle Framework for Tumor Augmentation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_33
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