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• W4387211147 abstract "Statistical shape models (SSMs) are an established way to represent the anatomy of a population with various clinically relevant applications. However, they typically require domain expertise, and labor-intensive landmark annotations to construct. We address these shortcomings by proposing an unsupervised method that leverages deep geometric features and functional correspondences to simultaneously learn local and global shape structures across population anatomies. Our pipeline significantly improves unsupervised correspondence estimation for SSMs compared to baseline methods, even on highly irregular surface topologies. We demonstrate this for two different anatomical structures: the thyroid and a multi-chamber heart dataset. Furthermore, our method is robust enough to learn from noisy neural network predictions, potentially enabling scaling SSMs to larger patient populations without manual segmentation annotation. The code is publically available at: https://github.com/alexanderbaumann99/S3M" @default.
• W4387211147 created "2023-10-01" @default.
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• W4387211147 date "2023-01-01" @default.
• W4387211147 modified "2023-10-01" @default.
• W4387211147 title "S3M: Scalable Statistical Shape Modeling Through Unsupervised Correspondences" @default.
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• W4387211147 doi "https://doi.org/10.1007/978-3-031-43999-5_44" @default.
• W4387211147 hasPublicationYear "2023" @default.
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