Abstract
Segmentation models for thyroid ultrasound images are challenged by domain gaps across multi-center data. Some methods have been proposed to address this issue by enforcing consistency across multi-domains or by simulating domain gaps using augmented single-domain. Among them, single-domain generalization methods offer a more universal solution, but their heavy reliance on the data augmentation causes two issues for ultrasound image segmentation. Firstly, the corruption in data augmentation may affect the distribution of grayscale values with diagnostic significant, leading to a decline in model’s segmentation ability. The second is the real domain gap between ultrasound images is difficult to be simulated, resulting in features still correlate with domain, which in turn prevents the construction of the domain-independent latent space. To address these, given that the shape distribution of nodules is task-relevant but domain-independent, the SHape-prior Affine Network (SHAN) is proposed. SHAN serves shape prior as a stable latent mapping space, learning aspect ratio, size, and location of nodules through affine transformation of prior. Thus, our method enhances the segmentation capability and cross-domain generalization of model without any data augmentation methods. Additionally, SHAN is designed to be a plug-and-play method that can improve the performance of segmentation models with an encoder-decoder structure. Our experiments are performed on the public dataset TN3K and a private dataset TUI with 6 domains. By combining SHAN with several segmentation methods and comparing them with other single-domain generalization methods, it can be proved that SHAN performs optimally on both source and target domain data.
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Zhang, R., Lu, W., Guan, C., Gao, J., Wei, X., Li, X. (2024). SHAN: Shape Guided Network for Thyroid Nodule Ultrasound Cross-Domain Segmentation. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15004. Springer, Cham. https://doi.org/10.1007/978-3-031-72083-3_68
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